Pluralsight Path. Building Machine Learning Solutions with scikit-learn (2019)

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Pluralsight Path. Building Machine Learning Solutions with scikit-learn (2019)

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文件列表

  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/exercise.7z 140.7 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/exercise.7z 96.2 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/exercise.7z 47.5 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/07. Exploring scikit-learn Libraries.mp4 37.6 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/exercise.7z 30.4 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/5. Model Selection Techniques/1. Model Selection Techniques.mp4 27.8 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/exercise.7z 24.5 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/3. Evaluation Methods for Classification Models/7. Demo.mp4 21.1 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/2. What Is Model Evaluation and Selection/1. Model Evaluation and Selection.mp4 21.0 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/6. Working with Specialized Datasets/3. Exploring Internal Datasets.mp4 19.6 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/09. Evaluating K-means Clustering.mp4 18.9 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/7. Performing Kernel Approximations/5. Comparing Classifiers Trained Using Implicit and Explict Features.mp4 18.5 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/06. Demo - Observing the Influence of Model Complexity.mp4 18.2 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/6. Working with Specialized Datasets/4. Creating Artificial Datasets for Regression, Classification, Clustering, and Dimensionality Reduc.mp4 18.1 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/6. Working with Specialized Datasets/5. Generating Manifold Data.mp4 17.2 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/5. Using Dictionary Learning to Denoise and Reconstruct Images.mp4 17.2 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Linear Model/2. Simple Linear Regression.mp4 17.0 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/6. Clustering Image Data Using a Pixel Connectivity Graph.mp4 16.9 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/08. Demo - Preparing Data for Multi-label Classification.mp4 16.8 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/07. Outlier Detection Using Local Outlier Factor.mp4 16.2 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/3. Implementing Regression and Classification Using Neural Networks in scikit-learn/3. Exploring and Preparing the Diet Dataset for Regressi.mp4 16.0 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/02. Demo - Measuring Bulk and Atomic Prediction Latencies for Different Models.mp4 15.6 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/4. Building a Simple Machine Learning Model with scikit-learn/6. Training and Prediction Using a Logistic Regression Classifier.mp4 15.5 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/09. Exploring the Classification Dataset.mp4 15.4 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/7. Performing Kernel Approximations/6. Comparing Accuracy and Runtime for Different Sample Sizes.mp4 15.3 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/06. Exploring the Automobile Mpg Dataset.mp4 15.2 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/3. Exploring the Titanic Dataset.mp4 15.2 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/7. Clustering Images Using a Gradient Connectivity Graph.mp4 14.9 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/12. Reducing Dimensionality Using Factor Analysis.mp4 14.8 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/09. Elastic Net Regression.mp4 14.5 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/4. Building a Simple Machine Learning Model with scikit-learn/4. Training and Prediction Using Linear Regression.mp4 14.3 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/5. Hyperparameter Tuning for Classification Models/3. Hyperparameter Tuning a Decision Tree Clasifier Using Grid Search.mp4 14.3 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/6. Autoscaling of scikit-learn with Apache Spark/3. Demo - Working with Spark Using spark-sklearn.mp4 14.2 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/04. Clustering Objectives and Use Cases.mp4 14.2 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/4. Building a Simple Machine Learning Model with scikit-learn/3. Data Preparation for Machine Learning.mp4 13.9 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/06. Demo - Implementing Factor Analysis.mp4 13.8 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Linear Model/3. Linear Regression with Multiple Features.mp4 13.8 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/3. Implementing Regression and Classification Using Neural Networks in scikit-learn/4. Build and Train a Neural Network Using the MLPRegress.mp4 13.3 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/8. Defining Helper Functions to Train and Evaluate Classification Models.mp4 13.3 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/10. Normalization and Cosine Similarity.mp4 13.3 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/08. Demo - Training Models Using Dense and Sparse Input Representation.mp4 13.1 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/3. Regression Using AdaBoost.mp4 13.0 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/5. Implementing Ensemble Learning Using Model Stacking/3. Classification Using a Stacking Ensemble.mp4 12.9 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/5. Implementing Dimensionality Reduction Using Restricted Boltzmann Machines in scikit-learn/5. Dimensionality Reduction Using Restricted Bo.mp4 12.9 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Linear Model/6. Linear Regression and the Dummy Trap.mp4 12.7 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/08. Using the Standard Scaler for Standardizing Numeric Features.mp4 12.6 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/15. Demo - Dictionary Learning to Find Sparse Representations of Data.mp4 12.6 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/04. Vectorize Text Using the Bag-of-words Model.mp4 12.6 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/6. Applying Classification Models to Images and Text Data/3. Exploring the Fashion MNIST Dataset.mp4 12.6 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/12. Spectral Clustering Using a Precomputed Matrix.mp4 12.6 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/10. Novelty Detection Using Local Outlier Factor.mp4 12.5 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/4. Implementing Scaling of Instances Using Out-of-core Learning/4. Demo - Preparing Text Data for out of Core Learning.mp4 12.5 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/08. Outlier Detection Using Isolation Forest.mp4 12.4 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/5. Implementing Dimensionality Reduction Using Restricted Boltzmann Machines in scikit-learn/4. Training a Classifier on All Features of the.mp4 12.4 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/09. Demo - Prediction with Sparse Data and Memory Profiling.mp4 12.4 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/06. Demo - Exploring the Classification Dataset.mp4 12.3 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/6. Hyperparameter Tuning for Regression Models/3. Hyperparameter Tuning for Lasso Regression Using Grid Search.mp4 12.1 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/08. Support Vector Machines.mp4 12.1 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/05. Traditional and Representation ML Models.mp4 12.0 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/07. Calculating and Visualizing Summary Statistics.mp4 12.0 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/7. Hyperparameter Tuning - DBSCAN Clustering.mp4 11.9 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/03. Linear Discriminant Analysis and Quadratic Discriminant Analysis.mp4 11.9 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/4. Implementing Scaling of Instances Using Out-of-core Learning/3. Incremental Learning for Large Datasets.mp4 11.9 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/03. Demo - Implementing Principal Component Analysis.mp4 11.9 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/4. Extracting Patches from Image Data.mp4 11.8 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/11. Performing K-means Clustering and Evaluation.mp4 11.8 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/07. Visualizing Relationships and Correlations in Features.mp4 11.7 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/05. Defining Helper Functions to Build and Train Models and Compare Results.mp4 11.7 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/08. Performing K-means Clustering.mp4 11.7 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/6. Regression Using Gradient Boosting.mp4 11.6 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/09. California Housing Dataset - Exploring Numeric and Categorical Features.mp4 11.5 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Linear Model/5. Label Encoding and One-hot Encoding Categorical Data.mp4 11.5 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/10. Hard Voting.mp4 11.4 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/7. Performing Kernel Approximations/3. Kernel Approximations.mp4 11.3 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/10. California Housing Dataset - Exploring Relationships in Data.mp4 11.2 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/4. Perceptrons and Neurons.mp4 11.2 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/07. Regression Using Bagging and Pasting.mp4 11.2 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/03. Demo - Generate S-curve Manifold and Setup Helper Functions.mp4 11.2 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/11. Regression Using Random Forest.mp4 11.2 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/03. Demo - Influence of Number of Features on Bulk Prediction Latency.mp4 11.1 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/04. Optimizations to Improve Prediction Latency.mp4 11.0 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/6. Hyperparameter Tuning - K-means Clustering.mp4 10.9 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/08. Demo - Exploring the Regression Dataset.mp4 10.9 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/08. Mean-shift Clustering.mp4 10.7 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/11. Demo - Using Univariate Linear Regression Tests to Select Features.mp4 10.7 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/03. Connecting the Dots with Linear Regression.mp4 10.6 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/8. Hyperparameter Tuning Using Warm Start and Early Stopping.mp4 10.6 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/07. Exploring Built-in Datasets in scikit-learn.mp4 10.5 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Linear Model/4. Standardizing Numeric Data.mp4 10.5 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/02. The Intuition Behind Principal Components Analysis.mp4 10.5 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/6. Applying Classification Models to Images and Text Data/4. Classifying Images Using Logistic Regression.mp4 10.4 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/6. Putting It All Together/4. Demo - Using the Patient Dataset.mp4 10.4 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/11. Transforming Bimodally Distributed Data to a Normal Distribution Using a Quantile Tra.mp4 10.4 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/7. Calculating Accuracy, Precision and Recall for the Classification Model.mp4 10.3 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/6. Training a Logistic Regression Binary Classifier.mp4 10.3 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/7. Performing Kernel Approximations/4. Preparing Image Data.mp4 10.2 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/06. Exploring the Regression Dataset.mp4 10.2 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/04. Averaging and Boosting, Voting and Stacking.mp4 10.2 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/04. Choosing Clustering Algorithms.mp4 10.2 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/03. A Quick Overview of Ensemble Learning.mp4 10.2 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/6. Hyperparameter Tuning for Regression Models/4. Tuning Different Regression Models Using Grid Search.mp4 10.1 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/12. Outlier Detection Using the Head Brain Dataset.mp4 10.1 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/4. Building a Simple Machine Learning Model with scikit-learn/5. Understanding Logistic Regression.mp4 10.0 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/7. Determining Decision Threshold Using ROC Curves.mp4 10.0 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/09. Demo - Helper Functions to Generate Datasets and Train Models.mp4 10.0 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/08. Demo - Observing Class Seperation Boundaries on the Iris Dataset.mp4 9.9 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/05. Measuring Performance in Scaling.mp4 9.9 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/7. Loading and Visualizing the Lego Bricks Image Dataset.mp4 9.9 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/4. Implementing Scaling of Instances Using Out-of-core Learning/6. Demo - Visualizing Latencies and Accuracies.mp4 9.8 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/4. Implementing Scaling of Instances Using Out-of-core Learning/5. Demo - Using Partial Fit to Perform out of Core Learning.mp4 9.8 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/08. Supervised and Unsupervised Learning.mp4 9.7 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/3. Implementing Regression and Classification Using Neural Networks in scikit-learn/6. Exploring and Preparing the Spine Dataset for Classif.mp4 9.6 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/5. Implementing Dimensionality Reduction Using Restricted Boltzmann Machines in scikit-learn/2. Restricted Boltzmann Machines for Dimensiona.mp4 9.6 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/02. The Manifold Hypothesis and Manifold Learning.mp4 9.6 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/4. Visualizing Relationships in the Data.mp4 9.5 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/5. Preprocessing the Data.mp4 9.5 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/03. Using scikit-learn in the Machine Learning Workflow.mp4 9.5 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/11. Soft Voting.mp4 9.4 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/4. Classification Using AdaBoost.mp4 9.4 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/04. Implementing Linear Discriminant Analysis Classification.mp4 9.3 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/04. Demo - Running Concurrent Workers Using Joblib.mp4 9.3 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/10. Demo - Measuring Training Latencies for Different Models.mp4 9.2 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/5. Applying Clustering to Image Data/4. Clustering Image Data.mp4 9.2 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/4. Evaluation Methods for Regression Models/7. Demo.mp4 9.2 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/6. Accuracy, Precision, and Recall.mp4 9.2 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/3. Implementing Regression and Classification Using Neural Networks in scikit-learn/7. Build and Train a Neural Network Using the MLPClassif.mp4 9.1 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/08. Mitigating Risks in Simple and Multiple Regression.mp4 9.1 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/03. Support Vector Regression.mp4 9.1 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/6. Autoscaling of scikit-learn with Apache Spark/4. Demo - Working with Spark Using scikit-spark.mp4 9.0 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/08. Demo - Manifold Learning with Handwritten Digits.mp4 8.9 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/exercise.7z 8.8 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/7. Hyperparameter Tuning of the Gradient Boosting Regressor Using Grid Search.mp4 8.8 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/3. Implementing Regression and Classification Using Neural Networks in scikit-learn/2. Performing Regression Using Neural Networks.mp4 8.8 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/4. Logistic Regression Intuition.mp4 8.7 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/6. Training a Neural Network.mp4 8.7 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/3. Feature Extraction from Images.mp4 8.7 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/05. Vectorize Text Using the Bag-of-n-grams Model.mp4 8.7 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/08. Exploring the Boston Newsgroups and Digits Datasets.mp4 8.6 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/06. Evaluating Clustering Models.mp4 8.5 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/05. Hierarchical Clustering.mp4 8.5 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/4. Implementing Scaling of Instances Using Out-of-core Learning/7. Demo - Using the Passive Aggressive, Perceptron, and BernoulliNB Classifiers.mp4 8.4 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/03. The Curse of Dimensionality.mp4 8.4 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/02. Bagging and Pasting.mp4 8.4 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/06. The Niche of scikit-learn in ML.mp4 8.3 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/04. Learning from Data - Training and Prediction.mp4 8.3 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/03. Demo - Introducing Joblib.mp4 8.3 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/07. Demo - Preparing Images to Apply Manifold Learning for Dimensionality Reduction.mp4 8.2 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/3. Support for Neural Networks in scikit-learn.mp4 8.0 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/07. DBSCAN Clustering.mp4 8.0 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/02. Representing Text Data in Numeric Form.mp4 8.0 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/07. Overfitted Models and Ensemble Learning.mp4 7.9 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/4. Evaluation Methods for Regression Models/5. R-squared and Adjusted R-squared.mp4 7.9 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/13. Demo - Finding the Best Value of K.mp4 7.9 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/09. Demo - Performing Multi-label Classification.mp4 7.8 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/04. Lasso, Ridge and Elastic Net Regression.mp4 7.8 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/8. Building and Training a Classification Model on Image.mp4 7.8 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/2. Encoding Text in Numeric Form.mp4 7.7 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/06. Demo - Integrating Joblib with Dask ML.mp4 7.7 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/10. Feature Selection and Dictionary Learning.mp4 7.7 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/6. Autoscaling of scikit-learn with Apache Spark/2. Integrating Apache Spark and scikit-learn.mp4 7.6 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/13. Classification Using Random Forest and Extra Trees.mp4 7.6 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/09. Using the Robust Scaler to Scale Numeric Features.mp4 7.6 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/06. Influence of Number of Features.mp4 7.6 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/09. Demo - Preparing the Olivetti Faces Dataset for Manifold Learning.mp4 7.6 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/06. Agglomerative Clustering.mp4 7.5 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/3. Evaluation Methods for Classification Models/5. Choosing the Right Metric.mp4 7.5 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/09. Demo - Performing Kitchen Sink Regression Using ML and Non-ML Techniques.mp4 7.4 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/09. Classification Using Bagging and Pasting.mp4 7.4 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/04. Scaling and Standardization.mp4 7.4 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/11. Using the Predict Score Samples and Decision Function.mp4 7.4 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/03. Supervised and Unsupervised Learning.mp4 7.3 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/05. Demo - Manifold Learning Using Spectral Embedding TSNE and Isomap.mp4 7.3 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/09. Demo - Linear Discriminant Analysis for Classification.mp4 7.2 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/06. Single Feature, Kitchen Sink, and Parsimonious Linear Regression.mp4 7.2 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/05. Demo - Cross Validation Using Concurrent Workers.mp4 7.2 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/02. The Machine Learning Workflow.mp4 7.0 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/10. Exploring the Iris Dataset.mp4 7.0 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/06. Vectorize Text Using Tf-Idf Scores.mp4 7.0 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/12. Demo - Defining Helper Functions to Build and Train Multiple Models with D.mp4 7.0 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/08. Reducing Dimensions Using the Hashing Vectorizer.mp4 7.0 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/03. Detecting and Coping with Outlier Data.mp4 6.9 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/02. Parallelizing Computation Using Joblib.mp4 6.8 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/07. Lasso Regression.mp4 6.7 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/07. Influence of Feature Extraction Techniques.mp4 6.7 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/10. Affinilty Propagation Clustering.mp4 6.7 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/05. Nearest Neighbors Regression.mp4 6.7 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/09. Decision Tree Regression.mp4 6.7 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/04. Minimizing Least Square Error.mp4 6.5 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/3. Evaluation Methods for Classification Models/6. ROC Curves and AUC.mp4 6.5 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/5. Implementing Dimensionality Reduction Using Restricted Boltzmann Machines in scikit-learn/3. A Brief History of Restricted Boltzmann Mach.mp4 6.5 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/14. Demo - Using Mutual Information to Select Features.mp4 6.5 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/09. Outlier Detection Using Elliptic Envelope.mp4 6.4 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/03. Overfitting and Regularization.mp4 6.4 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/07. Demo - Using Optimized Libraries and Reducing Validation Overhead.mp4 6.4 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/04. Implementing Support Vector Regression.mp4 6.4 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/4. Building a Simple Machine Learning Model with scikit-learn/2. Understanding Linear Regression.mp4 6.3 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/05. K-means Clustering.mp4 6.3 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/07. Demo - Grid Search with Concurrent Workers.mp4 6.2 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/8. Types of Classification.mp4 6.2 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/14. Naive Bayes.mp4 6.2 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/5. Building and Training a Classification Model on Text .mp4 6.2 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/06. Isolation Forest.mp4 6.2 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/7. Performing Kernel Approximations/2. Support Vector Classifiers and the Kernel Trick.mp4 6.1 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/03. Setting up Helper Functions to Perform Clustering.mp4 6.1 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/10. Regression with Categorical Variables.mp4 6.0 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/07. Getting Started with scikit-learn Install and Setup.mp4 5.9 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/4. Creating Feature Vectors from Text Data Using Tf-Idf.mp4 5.9 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/5. Hyperparameter Tuning for Classification Models/2. Hyperparameter Tuning.mp4 5.9 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/09. Implementing Support Vector Classification.mp4 5.9 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/3. K-means Number of Clusters - The Elbow Method.mp4 5.9 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/09. Installing scikit-learn Libraries.mp4 5.8 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/07. Implementing Stochastic Gradient Descent Classification.mp4 5.8 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/10. Demo - Manifold Learning on Olivetti Faces Dataset.mp4 5.8 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/13. Implementing Decision Tree Classification.mp4 5.8 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/3. Classification as a Machine Learning Problem.mp4 5.8 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/4. Implementing Scaling of Instances Using Out-of-core Learning/2. Streaming Data.mp4 5.7 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/02. Categories of Clustering Algorithms.mp4 5.7 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/5. Applying Clustering to Image Data/3. Exploring the MNIST Handwritten Digits Dataset.mp4 5.7 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/2. Installing and Setting up scikit-learn.mp4 5.7 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/04. Choosing the Right Estimator - Classification.mp4 5.6 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/3. Evaluation Methods for Classification Models/4. Accuracy, Precision, Recall, and F1 Score.mp4 5.6 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/04. Local Outlier Factor.mp4 5.6 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/04. Overfitted Models and Data Sparsity.mp4 5.5 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/6. Hyperparameter Tuning for Regression Models/2. Hyperparameter Tuning.mp4 5.5 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/11. Least Angle Regression.mp4 5.5 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/05. Decision Trees in Ensemble Learning.mp4 5.4 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/10. Nearest Neighbors.mp4 5.3 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/4. K-means Number of Clusters - The Silhouette Method.mp4 5.3 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/04. Demo - Building Regression Models with Principal Components.mp4 5.2 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/06. Demo - Manifold Learning with Locally Linear Embedding.mp4 5.2 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/07. Hashing for Dimensionality Reduction.mp4 5.2 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/3. Loading and Exploring the Newsgroup Dataset.mp4 5.2 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/5. Hyperparameter Tuning for Classification Models/4. Hyperparameter Tuning a Logistic Regression Classifier Using Grid Search.mp4 5.2 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/5. Multi-layer Perceptrons and Neural Networks.mp4 5.2 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/5. Implementing Ensemble Learning Using Model Stacking/2. Stacking.mp4 5.1 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/05. Exploring Techniques for Reducing Dimensions.mp4 5.1 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/07. Demo - Performing Classification with All Features.mp4 5.1 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/09. BIRCH Clustering.mp4 5.1 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/06. Understanding Decision Trees.mp4 5.1 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/04. Demo - Metric and Non-metric Multi Dimensional Scaling.mp4 5.0 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/03. Introducing Machine Learning.mp4 5.0 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/05. Elliptic Envelope.mp4 5.0 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/06. Choosing the Right Estimator - Regression and Dimensionality Reduction.mp4 5.0 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/02. Outliers and Novelties.mp4 5.0 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/2. Adaptive Boosting (AdaBoost).mp4 4.9 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/09. Performing Feature Extraction on a Python Dictionary.mp4 4.9 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/05. Installing and Setting up scikit-learn.mp4 4.9 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/04. Extra Trees.mp4 4.9 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/12. Decision Trees.mp4 4.8 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/08. Implementing Stochastic Gradient Descent Regression.mp4 4.7 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/6. Applying Classification Models to Images and Text Data/2. Representing Images as Matrices.mp4 4.7 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/11. Mini-batch K-means Clustering.mp4 4.7 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/06. Implementing K-nearest-neighbors Regression.mp4 4.6 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/4. Evaluation Methods for Regression Models/3. Mean Square Error and Root Mean Square Error.mp4 4.6 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/08. Influence of Feature Representation.mp4 4.6 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/07. Stochastic Gradient Descent Regression.mp4 4.5 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/05. Normalization.mp4 4.5 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/5. Applying Clustering to Image Data/2. Images as Matrices.mp4 4.4 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 4.4 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/02. Choosing Regression Algorithms.mp4 4.4 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/03. Random Subspaces and Random Patches.mp4 4.3 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/08. Ridge Regression.mp4 4.3 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/05. Implementing Quadratic Discriminant Analysis Classification.mp4 4.3 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/5. Gradient Boosting.mp4 4.3 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/7. Overfitting and Underfitting.mp4 4.3 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/2. Representing Images as Matrices.mp4 4.3 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/2. Understanding the Silhouette Score.mp4 4.3 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/exercise.7z 4.2 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/6. Encoding Images in Numeric Form.mp4 4.2 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/07. Linear Discriminant Analysis for Dimensionality Reduction.mp4 4.2 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/06. Stochastic Gradient Descent.mp4 4.1 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/3. Implementing Regression and Classification Using Neural Networks in scikit-learn/5. Performing Classification Using Neural Networks.mp4 4.1 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/03. Bag-of-words and Bag-of-n-grams Models.mp4 4.0 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/12. Regression Using Extra Trees.mp4 4.0 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/08. Getting Started and Exploring the Environment.mp4 4.0 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/6. Working with Specialized Datasets/2. Internal, Artificial, and External Datasets in Scikit Learn.mp4 3.9 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 3.8 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/10. Classification Using Random Patches.mp4 3.7 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 3.7 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/15. Implementing Naive Bayes Classification.mp4 3.6 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/5. Cross Entropy Intuition.mp4 3.6 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/02. Choosing Classification Algorithms.mp4 3.5 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/02. Overview of Regression Models in scikit-learn.mp4 3.5 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 3.5 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 3.5 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/05. Averaging vs. Boosting.mp4 3.5 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/08. Regression Using Random Subspaces.mp4 3.4 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 3.4 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/05. Choosing the Right Estimator - Clustering.mp4 3.4 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/10. Implementing Decision Tree Regression.mp4 3.3 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/05. Factor Analysis Using Singular Value Decomposition.mp4 3.2 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 3.2 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/4. Evaluation Methods for Regression Models/6. Choosing the Right Metric.mp4 3.2 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 3.2 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/3. Evaluation Methods for Classification Models/3. Confusion Matrix.mp4 3.1 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/05. Optimizations to Improve Prediction Throughput.mp4 3.0 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/1. Course Overview/1. Course Overview.mp4 3.0 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/6. Putting It All Together/1. Revisiting the Data Scientists Dilemma.mp4 2.9 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/11. Implementing K-nearest-neighbors Classification.mp4 2.9 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/04. Dimensions of Scaling.mp4 2.9 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/7. Performing Kernel Approximations/7. Summary and Further Study.mp4 2.9 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/8. Hyperparameter Tuning - Mean-shift Clustering.mp4 2.8 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 2.8 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/6. Putting It All Together/2. Model Evaluation Methods.mp4 2.8 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/13. Regression with Polynomial Relationships.mp4 2.6 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/06. Transforming Data to Gaussian Distributions.mp4 2.6 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/6. Autoscaling of scikit-learn with Apache Spark/5. Summary and Further Study.mp4 2.5 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/12. Implementing Least Angle Regression.mp4 2.5 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/02. Prerequisites and Course Outline.mp4 2.4 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/exercise.7z 2.4 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/03. Prerequisites and Course Outline.mp4 2.4 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/6. Putting It All Together/3. Model Selection Techniques.mp4 2.4 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/5. Seeds and Distance Measures.mp4 2.3 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/02. Prerequisites and Course Outline.mp4 2.3 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/16. Module Summary.mp4 2.3 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/09. R-squared and Adjusted R-squared.mp4 2.3 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/10. Summary.mp4 2.3 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/2. Prerequisites and Course Outline.mp4 2.3 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/01. Module Overview.mp4 2.3 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/5. Implementing Dimensionality Reduction Using Restricted Boltzmann Machines in scikit-learn/1. Module Overview.mp4 2.2 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/5. Implementing Ensemble Learning Using Model Stacking/4. Summary and Further Study.mp4 2.2 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/03. Prerequisites and Course Outline.mp4 2.2 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/01. Module Overview.mp4 2.2 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/14. Module Summary.mp4 2.2 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/6. Autoscaling of scikit-learn with Apache Spark/1. Module Overview.mp4 2.2 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/11. Summary and Further Study.mp4 2.1 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/1. Module Overview.mp4 2.1 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/11. Module Summary.mp4 2.1 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/10. Module Summary.mp4 2.1 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/exercise.7z 2.1 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/2. Prerequisites and Course Outline.mp4 2.1 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/4. Evaluation Methods for Regression Models/2. Regression Model Refresher.mp4 2.1 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/02. Prerequisites and Course Outline.mp4 2.1 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/11. Summary.mp4 2.1 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/02. Module Overview.mp4 2.1 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/6. Working with Specialized Datasets/1. Module Overview.mp4 2.1 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/01. Module Overview.mp4 2.1 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/10. Module Summary.mp4 2.0 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/12. Module Summary.mp4 2.0 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/02. Module Overview.mp4 2.0 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/01. Module Overview.mp4 2.0 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/4. Implementing Scaling of Instances Using Out-of-core Learning/8. Module Summary.mp4 2.0 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/9. Module Summary.mp4 2.0 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/8. Module Summary.mp4 2.0 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/10. Summary.mp4 2.0 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/1. Module Overview.mp4 1.9 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/01. Module Overview.mp4 1.9 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/10. Module Summary.mp4 1.9 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/3. Implementing Regression and Classification Using Neural Networks in scikit-learn/8. Module Summary.mp4 1.9 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/9. Module Summary.mp4 1.9 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/4. Building a Simple Machine Learning Model with scikit-learn/7. Summary and Further Study.mp4 1.9 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/4. Implementing Scaling of Instances Using Out-of-core Learning/1. Module Overview.mp4 1.9 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/02. Prerequisites and Course Outline.mp4 1.9 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/14. Module Summary.mp4 1.9 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/01. Module Overview.mp4 1.9 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/8. Module Summary.mp4 1.9 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/13. Module Summary.mp4 1.8 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/01. Module Overview.mp4 1.8 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/6. Applying Classification Models to Images and Text Data/5. Summary and Further Study.mp4 1.8 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/13. Module Summary.mp4 1.8 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/1. Module Overview.mp4 1.8 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/01. Module Overview.mp4 1.7 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/5. Implementing Dimensionality Reduction Using Restricted Boltzmann Machines in scikit-learn/6. Summary and Further Study.mp4 1.7 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/1. Module Overview.mp4 1.7 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/5. Applying Clustering to Image Data/5. Summary and Further Study.mp4 1.7 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/6. Working with Specialized Datasets/6. Module Summary.mp4 1.7 MB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/7. Performing Kernel Approximations/1. Module Overview.mp4 1.7 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/3. Evaluation Methods for Classification Models/2. Classification Model Refresher.mp4 1.7 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/3. Implementing Regression and Classification Using Neural Networks in scikit-learn/1. Module Overview.mp4 1.7 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/9. Module Summary.mp4 1.7 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/11. Module Summary.mp4 1.7 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/01. Module Overview.mp4 1.7 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/01. Module Overview.mp4 1.7 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/9. Module Summary.mp4 1.7 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/01. Module Overview.mp4 1.6 MB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/1. Module Overview.mp4 1.6 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/1. Module Overview.mp4 1.6 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/01. Module Overview.mp4 1.6 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/01. Module Overview.mp4 1.6 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Linear Model/7. Module Summary.mp4 1.6 MB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/10. Module Summary.mp4 1.6 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Linear Model/1. Module Overview.mp4 1.6 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/16. Summary.mp4 1.6 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/4. Evaluation Methods for Regression Models/4. Mean Absolute Error.mp4 1.6 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/6. Hyperparameter Tuning for Regression Models/1. Module Overview.mp4 1.6 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/6. Applying Classification Models to Images and Text Data/1. Module Overview.mp4 1.6 MB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/6. Hyperparameter Tuning for Regression Models/5. Summary and Further Study.mp4 1.5 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/5. Hyperparameter Tuning for Classification Models/1. Module Overview.mp4 1.5 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/01. Module Overview.mp4 1.5 MB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/5. Hyperparameter Tuning for Classification Models/5. Module Summary.mp4 1.4 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/01. Module Overview.mp4 1.4 MB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/5. Implementing Ensemble Learning Using Model Stacking/1. Module Overview.mp4 1.4 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/4. Building a Simple Machine Learning Model with scikit-learn/1. Module Overview.mp4 1.3 MB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/02. Prerequisites and Course Outline.mp4 1.3 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/01. Module Overview.mp4 1.2 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/3. Evaluation Methods for Classification Models/1. Introduction.mp4 1.2 MB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/5. Applying Clustering to Image Data/1. Module Overview.mp4 1.1 MB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/01. Module Overview.mp4 1.1 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/exercise.7z 1.1 MB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/4. Evaluation Methods for Regression Models/1. Introduction.mp4 906.2 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/1. Module Overview.mp4 880.8 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/4. Evaluation Methods for Regression Models/8. Summary.mp4 818.8 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/01. Version Check.mp4 595.3 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/01. Version Check.mp4 554.7 kB
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  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/5. Model Selection Techniques/1. Model Selection Techniques.vtt 24.6 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/06. Demo - Observing the Influence of Model Complexity.vtt 13.6 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/2. What Is Model Evaluation and Selection/1. Model Evaluation and Selection.vtt 13.0 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/04. Clustering Objectives and Use Cases.vtt 13.0 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/08. Demo - Preparing Data for Multi-label Classification.vtt 12.8 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/4. Building a Simple Machine Learning Model with scikit-learn/6. Training and Prediction Using a Logistic Regression Classifier.vtt 12.3 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/4. Implementing Scaling of Instances Using Out-of-core Learning/3. Incremental Learning for Large Datasets.vtt 12.1 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/6. Working with Specialized Datasets/4. Creating Artificial Datasets for Regression, Classification, Clustering, and Dimensionality Reduc.vtt 12.0 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/02. Demo - Measuring Bulk and Atomic Prediction Latencies for Different Models.vtt 12.0 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/3. Evaluation Methods for Classification Models/7. Demo.vtt 11.8 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/3. Implementing Regression and Classification Using Neural Networks in scikit-learn/3. Exploring and Preparing the Diet Dataset for Regressi.vtt 11.7 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/4. Building a Simple Machine Learning Model with scikit-learn/3. Data Preparation for Machine Learning.vtt 11.7 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/04. Optimizations to Improve Prediction Latency.vtt 11.6 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/09. Evaluating K-means Clustering.vtt 11.5 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/12. Spectral Clustering Using a Precomputed Matrix.vtt 11.5 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/4. Building a Simple Machine Learning Model with scikit-learn/4. Training and Prediction Using Linear Regression.vtt 11.5 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/6. Working with Specialized Datasets/5. Generating Manifold Data.vtt 11.3 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/3. Exploring the Titanic Dataset.vtt 11.2 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Linear Model/2. Simple Linear Regression.vtt 11.1 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/05. Traditional and Representation ML Models.vtt 11.0 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/7. Performing Kernel Approximations/5. Comparing Classifiers Trained Using Implicit and Explict Features.vtt 11.0 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/03. Linear Discriminant Analysis and Quadratic Discriminant Analysis.vtt 10.9 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/08. Mean-shift Clustering.vtt 10.8 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/07. Exploring scikit-learn Libraries.vtt 10.8 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/04. Choosing Clustering Algorithms.vtt 10.8 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/08. Support Vector Machines.vtt 10.8 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/4. Perceptrons and Neurons.vtt 10.8 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/06. Exploring the Automobile Mpg Dataset.vtt 10.7 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/4. Building a Simple Machine Learning Model with scikit-learn/5. Understanding Logistic Regression.vtt 10.7 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/15. Demo - Dictionary Learning to Find Sparse Representations of Data.vtt 10.7 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/03. Connecting the Dots with Linear Regression.vtt 10.7 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/8. Defining Helper Functions to Train and Evaluate Classification Models.vtt 10.6 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/04. Averaging and Boosting, Voting and Stacking.vtt 10.6 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/02. The Intuition Behind Principal Components Analysis.vtt 10.5 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/08. Supervised and Unsupervised Learning.vtt 10.5 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/09. Exploring the Classification Dataset.vtt 10.4 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/6. Autoscaling of scikit-learn with Apache Spark/3. Demo - Working with Spark Using spark-sklearn.vtt 10.4 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/06. Demo - Implementing Factor Analysis.vtt 10.4 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/03. A Quick Overview of Ensemble Learning.vtt 10.4 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/07. Outlier Detection Using Local Outlier Factor.vtt 10.3 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/7. Performing Kernel Approximations/3. Kernel Approximations.vtt 10.3 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/03. Using scikit-learn in the Machine Learning Workflow.vtt 10.3 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/6. Working with Specialized Datasets/3. Exploring Internal Datasets.vtt 10.3 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/3. Implementing Regression and Classification Using Neural Networks in scikit-learn/4. Build and Train a Neural Network Using the MLPRegress.vtt 10.2 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/6. Clustering Image Data Using a Pixel Connectivity Graph.vtt 10.1 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/05. Measuring Performance in Scaling.vtt 10.0 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/03. Demo - Implementing Principal Component Analysis.vtt 10.0 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/5. Implementing Dimensionality Reduction Using Restricted Boltzmann Machines in scikit-learn/4. Training a Classifier on All Features of the.vtt 9.9 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/08. Demo - Training Models Using Dense and Sparse Input Representation.vtt 9.8 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/5. Using Dictionary Learning to Denoise and Reconstruct Images.vtt 9.7 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/5. Hyperparameter Tuning for Classification Models/3. Hyperparameter Tuning a Decision Tree Clasifier Using Grid Search.vtt 9.7 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/09. Demo - Prediction with Sparse Data and Memory Profiling.vtt 9.7 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/5. Implementing Dimensionality Reduction Using Restricted Boltzmann Machines in scikit-learn/2. Restricted Boltzmann Machines for Dimensiona.vtt 9.7 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/7. Determining Decision Threshold Using ROC Curves.vtt 9.6 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/4. Implementing Scaling of Instances Using Out-of-core Learning/4. Demo - Preparing Text Data for out of Core Learning.vtt 9.6 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/7. Performing Kernel Approximations/6. Comparing Accuracy and Runtime for Different Sample Sizes.vtt 9.6 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/06. Demo - Exploring the Classification Dataset.vtt 9.5 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/04. Learning from Data - Training and Prediction.vtt 9.5 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/11. Performing K-means Clustering and Evaluation.vtt 9.5 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/08. Using the Standard Scaler for Standardizing Numeric Features.vtt 9.4 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/10. Normalization and Cosine Similarity.vtt 9.4 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/5. Implementing Ensemble Learning Using Model Stacking/3. Classification Using a Stacking Ensemble.vtt 9.4 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/12. Reducing Dimensionality Using Factor Analysis.vtt 9.3 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/07. Exploring Built-in Datasets in scikit-learn.vtt 9.2 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/06. Evaluating Clustering Models.vtt 9.2 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/08. Performing K-means Clustering.vtt 9.1 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/08. Mitigating Risks in Simple and Multiple Regression.vtt 9.1 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/02. The Manifold Hypothesis and Manifold Learning.vtt 9.0 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/6. Accuracy, Precision, and Recall.vtt 9.0 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/05. Defining Helper Functions to Build and Train Models and Compare Results.vtt 9.0 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Linear Model/3. Linear Regression with Multiple Features.vtt 9.0 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/6. Hyperparameter Tuning - K-means Clustering.vtt 9.0 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/7. Clustering Images Using a Gradient Connectivity Graph.vtt 8.9 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/09. Elastic Net Regression.vtt 8.9 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/03. The Curse of Dimensionality.vtt 8.8 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/11. Demo - Using Univariate Linear Regression Tests to Select Features.vtt 8.8 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/09. California Housing Dataset - Exploring Numeric and Categorical Features.vtt 8.8 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/3. Feature Extraction from Images.vtt 8.8 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/3. Implementing Regression and Classification Using Neural Networks in scikit-learn/2. Performing Regression Using Neural Networks.vtt 8.8 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/03. Demo - Influence of Number of Features on Bulk Prediction Latency.vtt 8.7 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/07. Visualizing Relationships and Correlations in Features.vtt 8.7 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/6. Regression Using Gradient Boosting.vtt 8.6 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/06. The Niche of scikit-learn in ML.vtt 8.6 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/6. Applying Classification Models to Images and Text Data/3. Exploring the Fashion MNIST Dataset.vtt 8.6 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/07. Overfitted Models and Ensemble Learning.vtt 8.5 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/06. Influence of Number of Features.vtt 8.5 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/7. Hyperparameter Tuning - DBSCAN Clustering.vtt 8.5 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/4. Evaluation Methods for Regression Models/5. R-squared and Adjusted R-squared.vtt 8.5 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/6. Hyperparameter Tuning for Regression Models/3. Hyperparameter Tuning for Lasso Regression Using Grid Search.vtt 8.4 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/03. Demo - Generate S-curve Manifold and Setup Helper Functions.vtt 8.4 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/4. Logistic Regression Intuition.vtt 8.4 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/04. Lasso, Ridge and Elastic Net Regression.vtt 8.3 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/08. Demo - Observing Class Seperation Boundaries on the Iris Dataset.vtt 8.3 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/10. Novelty Detection Using Local Outlier Factor.vtt 8.2 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/03. Support Vector Regression.vtt 8.1 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/08. Demo - Exploring the Regression Dataset.vtt 8.1 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/03. Supervised and Unsupervised Learning.vtt 8.1 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/02. Bagging and Pasting.vtt 8.1 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/2. Encoding Text in Numeric Form.vtt 8.0 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/02. Representing Text Data in Numeric Form.vtt 8.0 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/09. Demo - Helper Functions to Generate Datasets and Train Models.vtt 8.0 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/4. Visualizing Relationships in the Data.vtt 8.0 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/07. DBSCAN Clustering.vtt 7.9 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/6. Training a Neural Network.vtt 7.9 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/10. Hard Voting.vtt 7.9 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/07. Calculating and Visualizing Summary Statistics.vtt 7.9 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/05. Hierarchical Clustering.vtt 7.8 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/04. Scaling and Standardization.vtt 7.8 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/4. Implementing Scaling of Instances Using Out-of-core Learning/6. Demo - Visualizing Latencies and Accuracies.vtt 7.8 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/3. Regression Using AdaBoost.vtt 7.8 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/10. California Housing Dataset - Exploring Relationships in Data.vtt 7.7 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/3. Support for Neural Networks in scikit-learn.vtt 7.6 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/5. Implementing Dimensionality Reduction Using Restricted Boltzmann Machines in scikit-learn/5. Dimensionality Reduction Using Restricted Bo.vtt 7.6 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/07. Influence of Feature Extraction Techniques.vtt 7.6 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/11. Regression Using Random Forest.vtt 7.6 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/6. Training a Logistic Regression Binary Classifier.vtt 7.6 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/10. Feature Selection and Dictionary Learning.vtt 7.5 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/08. Outlier Detection Using Isolation Forest.vtt 7.5 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Linear Model/6. Linear Regression and the Dummy Trap.vtt 7.5 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/04. Vectorize Text Using the Bag-of-words Model.vtt 7.4 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/11. Transforming Bimodally Distributed Data to a Normal Distribution Using a Quantile Tra.vtt 7.4 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/06. Agglomerative Clustering.vtt 7.4 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/07. Regression Using Bagging and Pasting.vtt 7.4 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/3. K-means Number of Clusters - The Elbow Method.vtt 7.1 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/7. Calculating Accuracy, Precision and Recall for the Classification Model.vtt 7.1 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/4. Implementing Scaling of Instances Using Out-of-core Learning/5. Demo - Using Partial Fit to Perform out of Core Learning.vtt 7.1 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/3. Implementing Regression and Classification Using Neural Networks in scikit-learn/7. Build and Train a Neural Network Using the MLPClassif.vtt 7.1 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/02. The Machine Learning Workflow.vtt 7.1 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/7. Performing Kernel Approximations/4. Preparing Image Data.vtt 7.0 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/6. Hyperparameter Tuning for Regression Models/4. Tuning Different Regression Models Using Grid Search.vtt 7.0 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/6. Autoscaling of scikit-learn with Apache Spark/2. Integrating Apache Spark and scikit-learn.vtt 7.0 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/5. Applying Clustering to Image Data/4. Clustering Image Data.vtt 7.0 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/02. Parallelizing Computation Using Joblib.vtt 6.9 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/03. Detecting and Coping with Outlier Data.vtt 6.9 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/03. Overfitting and Regularization.vtt 6.9 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Linear Model/5. Label Encoding and One-hot Encoding Categorical Data.vtt 6.8 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/09. Decision Tree Regression.vtt 6.8 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/04. Demo - Running Concurrent Workers Using Joblib.vtt 6.8 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/7. Loading and Visualizing the Lego Bricks Image Dataset.vtt 6.8 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/3. Implementing Regression and Classification Using Neural Networks in scikit-learn/6. Exploring and Preparing the Spine Dataset for Classif.vtt 6.6 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/3. Evaluation Methods for Classification Models/5. Choosing the Right Metric.vtt 6.6 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/8. Hyperparameter Tuning Using Warm Start and Early Stopping.vtt 6.6 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/6. Autoscaling of scikit-learn with Apache Spark/4. Demo - Working with Spark Using scikit-spark.vtt 6.6 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/4. Evaluation Methods for Regression Models/7. Demo.vtt 6.5 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/7. Hyperparameter Tuning of the Gradient Boosting Regressor Using Grid Search.vtt 6.4 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/05. Nearest Neighbors Regression.vtt 6.4 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/02. Categories of Clustering Algorithms.vtt 6.4 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Linear Model/4. Standardizing Numeric Data.vtt 6.4 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/4. Extracting Patches from Image Data.vtt 6.4 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/4. Implementing Scaling of Instances Using Out-of-core Learning/7. Demo - Using the Passive Aggressive, Perceptron, and BernoulliNB Classifiers.vtt 6.4 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/03. Demo - Introducing Joblib.vtt 6.4 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/5. Preprocessing the Data.vtt 6.4 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/10. Demo - Measuring Training Latencies for Different Models.vtt 6.4 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/14. Naive Bayes.vtt 6.4 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/11. Soft Voting.vtt 6.3 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/7. Performing Kernel Approximations/2. Support Vector Classifiers and the Kernel Trick.vtt 6.3 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/13. Demo - Finding the Best Value of K.vtt 6.3 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/10. Affinilty Propagation Clustering.vtt 6.3 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/07. Demo - Preparing Images to Apply Manifold Learning for Dimensionality Reduction.vtt 6.2 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/4. Building a Simple Machine Learning Model with scikit-learn/2. Understanding Linear Regression.vtt 6.1 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/05. K-means Clustering.vtt 6.1 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/06. Exploring the Regression Dataset.vtt 6.1 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/3. Classification as a Machine Learning Problem.vtt 6.1 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/10. Exploring the Iris Dataset.vtt 6.0 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/03. Introducing Machine Learning.vtt 6.0 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/5. Hyperparameter Tuning for Classification Models/2. Hyperparameter Tuning.vtt 6.0 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/8. Building and Training a Classification Model on Image.vtt 6.0 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/09. Using the Robust Scaler to Scale Numeric Features.vtt 6.0 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/04. Overfitted Models and Data Sparsity.vtt 6.0 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/08. Demo - Manifold Learning with Handwritten Digits.vtt 6.0 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/09. Demo - Linear Discriminant Analysis for Classification.vtt 6.0 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/6. Putting It All Together/4. Demo - Using the Patient Dataset.vtt 6.0 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/06. Isolation Forest.vtt 5.9 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/05. Demo - Manifold Learning Using Spectral Embedding TSNE and Isomap.vtt 5.9 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/09. Demo - Performing Multi-label Classification.vtt 5.9 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/6. Applying Classification Models to Images and Text Data/4. Classifying Images Using Logistic Regression.vtt 5.9 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/06. Single Feature, Kitchen Sink, and Parsimonious Linear Regression.vtt 5.8 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/12. Demo - Defining Helper Functions to Build and Train Multiple Models with D.vtt 5.8 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/4. Implementing Scaling of Instances Using Out-of-core Learning/2. Streaming Data.vtt 5.8 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/5. Implementing Dimensionality Reduction Using Restricted Boltzmann Machines in scikit-learn/3. A Brief History of Restricted Boltzmann Mach.vtt 5.8 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/04. Choosing the Right Estimator - Classification.vtt 5.7 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/04. Minimizing Least Square Error.vtt 5.7 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/12. Outlier Detection Using the Head Brain Dataset.vtt 5.7 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/04. Implementing Linear Discriminant Analysis Classification.vtt 5.6 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/10. Regression with Categorical Variables.vtt 5.6 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/4. K-means Number of Clusters - The Silhouette Method.vtt 5.5 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/6. Hyperparameter Tuning for Regression Models/2. Hyperparameter Tuning.vtt 5.5 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/02. Outliers and Novelties.vtt 5.5 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/05. Exploring Techniques for Reducing Dimensions.vtt 5.5 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/8. Types of Classification.vtt 5.5 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/09. Installing scikit-learn Libraries.vtt 5.5 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/4. Evaluation Methods for Regression Models/3. Mean Square Error and Root Mean Square Error.vtt 5.5 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/09. Demo - Preparing the Olivetti Faces Dataset for Manifold Learning.vtt 5.4 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/05. Decision Trees in Ensemble Learning.vtt 5.4 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/05. Demo - Cross Validation Using Concurrent Workers.vtt 5.4 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/09. Classification Using Bagging and Pasting.vtt 5.4 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/09. Demo - Performing Kitchen Sink Regression Using ML and Non-ML Techniques.vtt 5.3 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/07. Getting Started with scikit-learn Install and Setup.vtt 5.3 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/5. Implementing Ensemble Learning Using Model Stacking/2. Stacking.vtt 5.3 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/03. Setting up Helper Functions to Perform Clustering.vtt 5.3 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/playlist.m3u 5.3 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/3. Evaluation Methods for Classification Models/6. ROC Curves and AUC.vtt 5.3 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/11. Least Angle Regression.vtt 5.2 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/13. Classification Using Random Forest and Extra Trees.vtt 5.1 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/06. Understanding Decision Trees.vtt 5.1 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/4. Classification Using AdaBoost.vtt 5.1 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/08. Exploring the Boston Newsgroups and Digits Datasets.vtt 5.1 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/06. Choosing the Right Estimator - Regression and Dimensionality Reduction.vtt 5.0 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/04. Local Outlier Factor.vtt 5.0 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/3. Evaluation Methods for Classification Models/4. Accuracy, Precision, Recall, and F1 Score.vtt 5.0 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/14. Demo - Using Mutual Information to Select Features.vtt 4.9 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/05. Elliptic Envelope.vtt 4.9 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/2. Adaptive Boosting (AdaBoost).vtt 4.8 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/07. Demo - Grid Search with Concurrent Workers.vtt 4.8 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/09. BIRCH Clustering.vtt 4.8 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/5. Applying Clustering to Image Data/3. Exploring the MNIST Handwritten Digits Dataset.vtt 4.7 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/04. Extra Trees.vtt 4.7 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/4. Creating Feature Vectors from Text Data Using Tf-Idf.vtt 4.7 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/07. Hashing for Dimensionality Reduction.vtt 4.7 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/10. Nearest Neighbors.vtt 4.7 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/5. Applying Clustering to Image Data/2. Images as Matrices.vtt 4.7 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/12. Decision Trees.vtt 4.6 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/5. Building and Training a Classification Model on Text .vtt 4.6 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/10. Demo - Manifold Learning on Olivetti Faces Dataset.vtt 4.6 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/05. Vectorize Text Using the Bag-of-n-grams Model.vtt 4.5 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/playlist.m3u 4.5 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/2. Understanding the Silhouette Score.vtt 4.5 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/3. Building a Simple Classification Model/2. Installing and Setting up scikit-learn.vtt 4.5 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/04. Demo - Building Regression Models with Principal Components.vtt 4.5 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/07. Lasso Regression.vtt 4.5 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/7. Overfitting and Underfitting.vtt 4.5 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/08. Reducing Dimensions Using the Hashing Vectorizer.vtt 4.4 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/6. Working with Specialized Datasets/2. Internal, Artificial, and External Datasets in Scikit Learn.vtt 4.4 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/07. Linear Discriminant Analysis for Dimensionality Reduction.vtt 4.4 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/08. Influence of Feature Representation.vtt 4.4 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/3. Performing Clustering Using Multiple Techniques/11. Mini-batch K-means Clustering.vtt 4.3 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/07. Stochastic Gradient Descent Regression.vtt 4.3 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/07. Demo - Using Optimized Libraries and Reducing Validation Overhead.vtt 4.3 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/playlist.m3u 4.3 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/07. Demo - Performing Classification with All Features.vtt 4.3 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/6. Applying Classification Models to Images and Text Data/2. Representing Images as Matrices.vtt 4.3 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/6. Encoding Images in Numeric Form.vtt 4.3 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/playlist.m3u 4.3 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/4. Implementing Ensemble Learning Using Boosting Methods/5. Gradient Boosting.vtt 4.2 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/2. Representing Images as Matrices.vtt 4.2 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/02. Choosing Regression Algorithms.vtt 4.2 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/5. Multi-layer Perceptrons and Neural Networks.vtt 4.1 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/05. Installing and Setting up scikit-learn.vtt 4.1 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/05. Normalization.vtt 4.1 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/11. Using the Predict Score Samples and Decision Function.vtt 4.1 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/06. Stochastic Gradient Descent.vtt 4.1 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/03. Bag-of-words and Bag-of-n-grams Models.vtt 4.1 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/09. Implementing Support Vector Classification.vtt 4.0 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/3. Implementing Regression and Classification Using Neural Networks in scikit-learn/5. Performing Classification Using Neural Networks.vtt 4.0 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/playlist.m3u 4.0 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/3. Understanding and Implementing Novelty and Outlier Detection/09. Outlier Detection Using Elliptic Envelope.vtt 3.9 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/5. Implementing Multicore Parallelism in scikit-learn/06. Demo - Integrating Joblib with Dask ML.vtt 3.9 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/03. Random Subspaces and Random Patches.vtt 3.9 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/4. Implementing Text and Image Classification Using Neural Networks in scikit-learn/3. Loading and Exploring the Newsgroup Dataset.vtt 3.9 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/playlist.m3u 3.8 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/13. Implementing Decision Tree Classification.vtt 3.8 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/07. Implementing Stochastic Gradient Descent Classification.vtt 3.7 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/04. Demo - Metric and Non-metric Multi Dimensional Scaling.vtt 3.7 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/06. Vectorize Text Using Tf-Idf Scores.vtt 3.7 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/04. Implementing Support Vector Regression.vtt 3.7 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/4. Evaluation Methods for Regression Models/6. Choosing the Right Metric.vtt 3.6 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/5. Hyperparameter Tuning for Classification Models/4. Hyperparameter Tuning a Logistic Regression Classifier Using Grid Search.vtt 3.6 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/playlist.m3u 3.6 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/05. Choosing the Right Estimator - Clustering.vtt 3.5 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/02. Overview of Regression Models in scikit-learn.vtt 3.4 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/08. Getting Started and Exploring the Environment.vtt 3.4 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/05. Averaging vs. Boosting.vtt 3.4 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.vtt 3.3 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/4. Preparing Text Data for Machine Learning/09. Performing Feature Extraction on a Python Dictionary.vtt 3.3 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/3. Dimensionality Reduction in Linear Data/05. Factor Analysis Using Singular Value Decomposition.vtt 3.3 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/02. Choosing Classification Algorithms.vtt 3.3 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/04. Dimensions of Scaling.vtt 3.2 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/06. Demo - Manifold Learning with Locally Linear Embedding.vtt 3.2 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/playlist.m3u 3.2 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/6. Putting It All Together/1. Revisiting the Data Scientists Dilemma.vtt 3.2 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/08. Implementing Stochastic Gradient Descent Regression.vtt 3.2 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/3. Observing the Factors Affecting Prediction Latency/05. Optimizations to Improve Prediction Throughput.vtt 3.2 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/3. Evaluation Methods for Classification Models/3. Confusion Matrix.vtt 3.1 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/6. Putting It All Together/2. Model Evaluation Methods.vtt 3.1 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/5. Cross Entropy Intuition.vtt 3.1 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.vtt 3.1 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/7. Performing Kernel Approximations/7. Summary and Further Study.vtt 3.0 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.vtt 3.0 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/13. Regression with Polynomial Relationships.vtt 2.9 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/playlist.m3u 2.9 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/2. Prerequisites and Course Outline.vtt 2.9 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.vtt 2.9 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/~i.txt 2.9 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/2. Understanding Ensemble Learning Techniques/02. Prerequisites and Course Outline.vtt 2.9 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/6. Autoscaling of scikit-learn with Apache Spark/5. Summary and Further Study.vtt 2.9 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.vtt 2.8 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.vtt 2.8 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/03. Prerequisites and Course Outline.vtt 2.8 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/08. Ridge Regression.vtt 2.7 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/8. Hyperparameter Tuning - Mean-shift Clustering.vtt 2.7 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/05. Implementing Quadratic Discriminant Analysis Classification.vtt 2.7 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/5. Implementing Ensemble Learning Using Model Stacking/4. Summary and Further Study.vtt 2.7 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.vtt 2.7 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/4. Dimensionality Reduction in Non-linear Data/11. Summary and Further Study.vtt 2.7 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/06. Implementing K-nearest-neighbors Regression.vtt 2.7 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/6. Putting It All Together/3. Model Selection Techniques.vtt 2.6 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/3. Understanding the Machine Learning Workflow with scikit-learn/11. Summary.vtt 2.6 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/4. Performing Classification Using Multiple Techniques/16. Module Summary.vtt 2.6 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/2. Building a Simple Clustering Model in scikit-learn/02. Prerequisites and Course Outline.vtt 2.5 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/12. Regression Using Extra Trees.vtt 2.5 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/03. Prerequisites and Course Outline.vtt 2.5 kB
  • B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)/2. Getting Started with Feature Selection in scikit-learn/02. Prerequisites and Course Outline.vtt 2.4 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/02. Prerequisites and Course Outline.vtt 2.4 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/1. Course Overview/1. Course Overview.vtt 2.4 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/06. Transforming Data to Gaussian Distributions.vtt 2.4 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/2. Prerequisites and Course Outline.vtt 2.4 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/5. Implementing Dimensionality Reduction Using Restricted Boltzmann Machines in scikit-learn/1. Module Overview.vtt 2.4 kB
  • A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)/4. Hyperparameter Tuning for Clustering Models/5. Seeds and Distance Measures.vtt 2.4 kB
  • C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)/4. Evaluation Methods for Regression Models/2. Regression Model Refresher.vtt 2.4 kB
  • C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)/2. Understanding Strategies for Computational Scaling/11. Module Summary.vtt 2.4 kB
  • B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)/2. Introducing Neural Networks in scikit-learn/8. Module Summary.vtt 2.3 kB
  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Linear Regression as a Machine Learning Problem/09. R-squared and Adjusted R-squared.vtt 2.3 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/01. Module Overview.vtt 2.3 kB
  • B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)/3. Implementing Ensemble Learning Using Averaging Methods/10. Classification Using Random Patches.vtt 2.3 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.vtt 2.3 kB
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  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/10. Module Summary.vtt 2.1 kB
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  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/2. Preparing Numeric Data for Machine Learning/02. Module Overview.vtt 2.1 kB
  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/2. Understanding Classification as a Machine Learning Problem/1. Module Overview.vtt 2.1 kB
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  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/5. Preparing Image Data for Machine Learning/8. Module Summary.vtt 1.9 kB
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  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/4. Building Regularized Regression Models/01. Module Overview.vtt 1.8 kB
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  • A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)/5. Performing Regression Using Multiple Techniques/12. Implementing Least Angle Regression.vtt 1.7 kB
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  • A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)/5. Hyperparameter Tuning for Classification Models/5. Module Summary.vtt 1.7 kB
  • C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)/7. Performing Kernel Approximations/1. Module Overview.vtt 1.7 kB
  • A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)/2. Exploring scikit-learn for Machine Learning/01. Module Overview.vtt 1.6 kB
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