Udemy - The Data Science Course Complete Data Science Bootcamp 2025 (12.2024)

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Udemy - The Data Science Course Complete Data Science Bootcamp 2025 (12.2024)

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文件列表

  • 11. Probability - Bayesian Inference/12. A Practical Example of Bayesian Inference.mp4 146.0 MB
  • 12. Probability - Distributions/15. A Practical Example of Probability Distributions.mp4 145.0 MB
  • 16. Statistics - Practical Example Descriptive Statistics/01. Practical Example Descriptive Statistics.mp4 136.9 MB
  • 05. The Field of Data Science - Popular Data Science Techniques/01. Techniques for Working with Traditional Data.mp4 112.4 MB
  • 42. Part 6 Mathematics/11. Why is Linear Algebra Useful.mp4 92.8 MB
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/01. Practical Example Linear Regression (Part 1).mp4 88.9 MB
  • 03. The Field of Data Science - Connecting the Data Science Disciplines/01. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 87.6 MB
  • 06. The Field of Data Science - Popular Data Science Tools/01. Necessary Programming Languages and Software Used in Data Science.mp4 86.4 MB
  • 10. Probability - Combinatorics/11. A Practical Example of Combinatorics.mp4 84.6 MB
  • 05. The Field of Data Science - Popular Data Science Techniques/07. Techniques for Working with Traditional Methods.mp4 79.7 MB
  • 57. Appendix Deep Learning - TensorFlow 1 Business Case/04. Business Case Preprocessing.mp4 78.0 MB
  • 53. Deep Learning - Business Case Example/04. Business Case Preprocessing the Data.mp4 77.4 MB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp4 73.1 MB
  • 05. The Field of Data Science - Popular Data Science Techniques/10. Types of Machine Learning.mp4 72.8 MB
  • 19. Statistics - Practical Example Inferential Statistics/01. Practical Example Inferential Statistics.mp4 72.4 MB
  • 05. The Field of Data Science - Popular Data Science Techniques/03. Techniques for Working with Big Data.mp4 65.1 MB
  • 57. Appendix Deep Learning - TensorFlow 1 Business Case/01. Business Case Getting Acquainted with the Dataset.mp4 63.2 MB
  • 58. Software Integration/02. What are Data Connectivity, APIs, and Endpoints.mp4 63.1 MB
  • 08. The Field of Data Science - Debunking Common Misconceptions/01. Debunking Common Misconceptions.mp4 61.7 MB
  • 57. Appendix Deep Learning - TensorFlow 1 Business Case/06. Creating a Data Provider.mp4 59.0 MB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/03. Checking the Content of the Data Set.mp4 56.6 MB
  • 05. The Field of Data Science - Popular Data Science Techniques/05. Business Intelligence (BI) Techniques.mp4 55.5 MB
  • 18. Statistics - Inferential Statistics Confidence Intervals/02. Confidence Intervals; Population Variance Known; Z-score.mp4 54.7 MB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp4 53.8 MB
  • 53. Deep Learning - Business Case Example/01. Business Case Exploring the Dataset and Identifying Predictors.mp4 53.8 MB
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/08. Practical Example Linear Regression (Part 5).mp4 52.9 MB
  • 05. The Field of Data Science - Popular Data Science Techniques/09. Machine Learning (ML) Techniques.mp4 51.8 MB
  • 02. The Field of Data Science - The Various Data Science Disciplines/04. Continuing with BI, ML, and AI.mp4 49.9 MB
  • 04. The Field of Data Science - The Benefits of Each Discipline/01. The Reason Behind These Disciplines.mp4 49.0 MB
  • 21. Statistics - Practical Example Hypothesis Testing/01. Practical Example Hypothesis Testing.mp4 48.1 MB
  • 09. Part 2 Probability/02. Computing Expected Values.mp4 47.9 MB
  • 02. The Field of Data Science - The Various Data Science Disciplines/07. A Breakdown of our Data Science Infographic.mp4 47.6 MB
  • 62. Case Study - Loading the 'absenteeism_module'/03. Deploying the 'absenteeism_module' - Part II.mp4 47.3 MB
  • 18. Statistics - Inferential Statistics Confidence Intervals/09. Confidence intervals. Two means. Dependent samples.mp4 47.2 MB
  • 53. Deep Learning - Business Case Example/09. Business Case Setting an Early Stopping Mechanism.mp4 45.9 MB
  • 64. Appendix - Additional Python Tools/05. List Comprehensions.mp4 45.3 MB
  • 15. Statistics - Descriptive Statistics/01. Types of Data.mp4 45.3 MB
  • 57. Appendix Deep Learning - TensorFlow 1 Business Case/07. Business Case Model Outline.mp4 44.6 MB
  • 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/02. The Naive Bayes Algorithm.mp4 44.1 MB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/07. Dropping a Column from a DataFrame in Python.mp4 43.2 MB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/08. Interpreting the Coefficients for Our Problem.mp4 43.1 MB
  • 13. Probability - Probability in Other Fields/01. Probability in Finance.mp4 42.3 MB
  • 63. Case Study - Analyzing the Predicted Outputs in Tableau/04. Analyzing Reasons vs Probability in Tableau.mp4 42.2 MB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp4 42.1 MB
  • 07. The Field of Data Science - Careers in Data Science/01. Finding the Job - What to Expect and What to Look for.mp4 42.0 MB
  • 45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/04. Basic NN Example (Part 4).mp4 41.9 MB
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/06. Practical Example Linear Regression (Part 4).mp4 41.3 MB
  • 20. Statistics - Hypothesis Testing/03. Rejection Region and Significance Level.mp4 40.6 MB
  • 63. Case Study - Analyzing the Predicted Outputs in Tableau/02. Analyzing Age vs Probability in Tableau.mp4 40.6 MB
  • 56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/09. MNIST Results and Testing.mp4 40.0 MB
  • 38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp4 39.3 MB
  • 09. Part 2 Probability/03. Frequency.mp4 39.2 MB
  • 65. Appendix - pandas Fundamentals/11. Data Selection in pandas DataFrames.mp4 39.1 MB
  • 20. Statistics - Hypothesis Testing/05. Test for the Mean. Population Variance Known.mp4 38.7 MB
  • 05. The Field of Data Science - Popular Data Science Techniques/08. Real Life Examples of Traditional Methods.mp4 38.5 MB
  • 40. ChatGPT for Data Science/05. First attempt at machine learning with ChatGPT.mp4 38.5 MB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/05. Splitting the Data for Training and Testing.mp4 37.8 MB
  • 37. Advanced Statistical Methods - Cluster Analysis/02. Some Examples of Clusters.mp4 37.6 MB
  • 12. Probability - Distributions/02. Types of Probability Distributions.mp4 37.3 MB
  • 34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.mp4 37.3 MB
  • 14. Part 3 Statistics/01. Population and Sample.mp4 36.8 MB
  • 56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/04. MNIST Model Outline.mp4 36.4 MB
  • 42. Part 6 Mathematics/10. Dot Product of Matrices.mp4 36.0 MB
  • 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/02. Adjusted R-Squared.mp4 35.9 MB
  • 38. Advanced Statistical Methods - K-Means Clustering/02. A Simple Example of Clustering.mp4 35.8 MB
  • 38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp4 35.7 MB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp4 35.5 MB
  • 20. Statistics - Hypothesis Testing/07. p-value.mp4 35.4 MB
  • 40. ChatGPT for Data Science/10. Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec.mp4 35.3 MB
  • 40. ChatGPT for Data Science/19. Using ChatGPT for ethical considerations.mp4 35.2 MB
  • 40. ChatGPT for Data Science/14. Decoding comic book data Python Regular Expressions and ChatGPT.mp4 34.7 MB
  • 64. Appendix - Additional Python Tools/04. Triple Nested For Loops.mp4 34.6 MB
  • 20. Statistics - Hypothesis Testing/10. Test for the Mean. Dependent Samples.mp4 34.4 MB
  • 52. Deep Learning - Classifying on the MNIST Dataset/06. MNIST Preprocess the Data - Shuffle and Batch.mp4 34.3 MB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/02. Creating the Targets for the Logistic Regression.mp4 34.0 MB
  • 28. Python - Sequences/05. Dictionaries.mp4 34.0 MB
  • 65. Appendix - pandas Fundamentals/12. pandas DataFrames - Indexing with .iloc[].mp4 33.8 MB
  • 15. Statistics - Descriptive Statistics/02. Levels of Measurement.mp4 33.8 MB
  • 20. Statistics - Hypothesis Testing/01. Null vs Alternative Hypothesis.mp4 33.5 MB
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/02. Practical Example Linear Regression (Part 2).mp4 33.4 MB
  • 56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/08. MNIST Learning.mp4 33.4 MB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp4 33.4 MB
  • 13. Probability - Probability in Other Fields/02. Probability in Statistics.mp4 33.1 MB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp4 33.1 MB
  • 52. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.mp4 32.5 MB
  • 12. Probability - Distributions/06. Discrete Distributions The Binomial Distribution.mp4 32.1 MB
  • 02. The Field of Data Science - The Various Data Science Disciplines/06. More Examples of Generative AI.mp4 32.0 MB
  • 28. Python - Sequences/02. Using Methods.mp4 31.8 MB
  • 32. Advanced Statistical Methods - Linear Regression with StatsModels/05. First Regression in Python.mp4 31.0 MB
  • 53. Deep Learning - Business Case Example/08. Business Case Learning and Interpreting the Result.mp4 30.8 MB
  • 09. Part 2 Probability/01. The Basic Probability Formula.mp4 30.8 MB
  • 32. Advanced Statistical Methods - Linear Regression with StatsModels/08. How to Interpret the Regression Table.mp4 30.1 MB
  • 40. ChatGPT for Data Science/04. Data Preprocessing with ChatGPT.mp4 30.1 MB
  • 18. Statistics - Inferential Statistics Confidence Intervals/01. What are Confidence Intervals.mp4 30.0 MB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp4 29.9 MB
  • 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/10. Machine Learning with Naïve Bayes (First Attempt).mp4 29.5 MB
  • 38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp4 29.4 MB
  • 05. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Machine Learning (ML).mp4 29.1 MB
  • 17. Statistics - Inferential Statistics Fundamentals/08. Estimators and Estimates.mp4 29.0 MB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp4 29.0 MB
  • 15. Statistics - Descriptive Statistics/03. Categorical Variables - Visualization Techniques.mp4 28.8 MB
  • 34. Advanced Statistical Methods - Linear Regression with sklearn/03. Simple Linear Regression with sklearn.mp4 28.8 MB
  • 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/08. A3 Normality and Homoscedasticity.mp4 28.7 MB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp4 28.7 MB
  • 46. Deep Learning - TensorFlow 2.0 Introduction/01. How to Install TensorFlow 2.0.mp4 28.7 MB
  • 05. The Field of Data Science - Popular Data Science Techniques/11. Evolution and Latest Trends of Machine Learning (ML).mp4 28.7 MB
  • 57. Appendix Deep Learning - TensorFlow 1 Business Case/03. The Importance of Working with a Balanced Dataset.mp4 28.6 MB
  • 40. ChatGPT for Data Science/08. Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM.mp4 28.5 MB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp4 28.3 MB
  • 46. Deep Learning - TensorFlow 2.0 Introduction/06. Outlining the Model with TensorFlow 2.mp4 28.3 MB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/07. Creating a Summary Table with the Coefficients and Intercept.mp4 28.3 MB
  • 57. Appendix Deep Learning - TensorFlow 1 Business Case/08. Business Case Optimization.mp4 28.2 MB
  • 38. Advanced Statistical Methods - K-Means Clustering/06. How to Choose the Number of Clusters.mp4 28.2 MB
  • 40. ChatGPT for Data Science/01. Traditional data science methods and the role of ChatGPT.mp4 27.4 MB
  • 46. Deep Learning - TensorFlow 2.0 Introduction/07. Interpreting the Result and Extracting the Weights and Bias.mp4 27.2 MB
  • 09. Part 2 Probability/04. Events and Their Complements.mp4 27.1 MB
  • 64. Appendix - Additional Python Tools/01. Using the .format() Method.mp4 26.9 MB
  • 65. Appendix - pandas Fundamentals/10. pandas DataFrames - Common Attributes.mp4 26.9 MB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp4 26.8 MB
  • 65. Appendix - pandas Fundamentals/01. Introduction to pandas Series.mp4 26.2 MB
  • 36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp4 26.1 MB
  • 05. The Field of Data Science - Popular Data Science Techniques/06. Real Life Examples of Business Intelligence (BI).mp4 25.8 MB
  • 02. The Field of Data Science - The Various Data Science Disciplines/05. Traditional AI vs. Generative AI.mp4 25.7 MB
  • 58. Software Integration/03. Taking a Closer Look at APIs.mp4 25.7 MB
  • 15. Statistics - Descriptive Statistics/11. Mean, median and mode.mp4 25.7 MB
  • 34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.mp4 25.7 MB
  • 20. Statistics - Hypothesis Testing/14. Test for the mean. Independent Samples (Part 2).mp4 25.6 MB
  • 56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/06. Calculating the Accuracy of the Model.mp4 25.6 MB
  • 65. Appendix - pandas Fundamentals/06. Using .unique() and .nunique().mp4 25.5 MB
  • 59. Case Study - What's Next in the Course/03. Introducing the Data Set.mp4 25.4 MB
  • 11. Probability - Bayesian Inference/04. Union of Sets.mp4 25.4 MB
  • 12. Probability - Distributions/07. Discrete Distributions The Poisson Distribution.mp4 25.1 MB
  • 36. Advanced Statistical Methods - Logistic Regression/03. Logistic vs Logit Function.mp4 24.9 MB
  • 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/03. Digging into a Deep Net.mp4 24.8 MB
  • 32. Advanced Statistical Methods - Linear Regression with StatsModels/04. Python Packages Installation.mp4 24.8 MB
  • 10. Probability - Combinatorics/06. Solving Combinations.mp4 24.8 MB
  • 44. Deep Learning - Introduction to Neural Networks/11. Optimization Algorithm 1-Parameter Gradient Descent.mp4 24.7 MB
  • 15. Statistics - Descriptive Statistics/15. Variance.mp4 24.7 MB
  • 17. Statistics - Inferential Statistics Fundamentals/06. Central Limit Theorem.mp4 24.3 MB
  • 18. Statistics - Inferential Statistics Confidence Intervals/08. Margin of Error.mp4 24.2 MB
  • 28. Python - Sequences/01. Lists.mp4 24.2 MB
  • 52. Deep Learning - Classifying on the MNIST Dataset/04. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp4 24.0 MB
  • 64. Appendix - Additional Python Tools/06. Anonymous (Lambda) Functions.mp4 23.9 MB
  • 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. Dealing with Categorical Data - Dummy Variables.mp4 23.7 MB
  • 52. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.mp4 23.7 MB
  • 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/04. Non-Linearities and their Purpose.mp4 23.6 MB
  • 32. Advanced Statistical Methods - Linear Regression with StatsModels/10. What is the OLS.mp4 23.6 MB
  • 53. Deep Learning - Business Case Example/03. Business Case Balancing the Dataset.mp4 23.4 MB
  • 34. Advanced Statistical Methods - Linear Regression with sklearn/04. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 23.4 MB
  • 42. Part 6 Mathematics/06. Addition and Subtraction of Matrices.mp4 23.2 MB
  • 52. Deep Learning - Classifying on the MNIST Dataset/08. MNIST Outline the Model.mp4 23.1 MB
  • 36. Advanced Statistical Methods - Logistic Regression/02. A Simple Example in Python.mp4 22.9 MB
  • 40. ChatGPT for Data Science/06. Analyzing a client database with ChatGPT in Python.mp4 22.7 MB
  • 40. ChatGPT for Data Science/09. Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot.mp4 22.6 MB
  • 36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp4 22.6 MB
  • 11. Probability - Bayesian Inference/11. Bayes' Law.mp4 22.4 MB
  • 12. Probability - Distributions/08. Characteristics of Continuous Distributions.mp4 22.3 MB
  • 65. Appendix - pandas Fundamentals/05. Parameters and Arguments in pandas.mp4 22.2 MB
  • 12. Probability - Distributions/10. Continuous Distributions The Standard Normal Distribution.mp4 22.1 MB
  • 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/12. Testing the Model on New Data.mp4 21.8 MB
  • 65. Appendix - pandas Fundamentals/13. pandas DataFrames - Indexing with .loc[].mp4 21.7 MB
  • 57. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.mp4 21.6 MB
  • 34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).mp4 21.5 MB
  • 34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.mp4 21.4 MB
  • 34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).mp4 21.4 MB
  • 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/07. Backpropagation.mp4 21.3 MB
  • 10. Probability - Combinatorics/08. Solving Combinations with Separate Sample Spaces.mp4 21.3 MB
  • 36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp4 21.2 MB
  • 11. Probability - Bayesian Inference/10. The Multiplication Law.mp4 21.2 MB
  • 29. Python - Iterations/02. While Loops and Incrementing.mp4 21.2 MB
  • 15. Statistics - Descriptive Statistics/17. Standard Deviation and Coefficient of Variation.mp4 21.1 MB
  • 11. Probability - Bayesian Inference/07. The Conditional Probability Formula.mp4 21.0 MB
  • 12. Probability - Distributions/09. Continuous Distributions The Normal Distribution.mp4 21.0 MB
  • 23. Python - Variables and Data Types/03. Python Strings.mp4 20.7 MB
  • 20. Statistics - Hypothesis Testing/08. Test for the Mean. Population Variance Unknown.mp4 20.7 MB
  • 15. Statistics - Descriptive Statistics/09. Cross Tables and Scatter Plots.mp4 20.7 MB
  • 59. Case Study - What's Next in the Course/01. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 20.6 MB
  • 62. Case Study - Loading the 'absenteeism_module'/02. Deploying the 'absenteeism_module' - Part I.mp4 20.6 MB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/02. Importing the Absenteeism Data in Python.mp4 20.5 MB
  • 58. Software Integration/01. What are Data, Servers, Clients, Requests, and Responses.mp4 20.5 MB
  • 12. Probability - Distributions/01. Fundamentals of Probability Distributions.mp4 20.4 MB
  • 15. Statistics - Descriptive Statistics/21. Correlation Coefficient.mp4 20.3 MB
  • 28. Python - Sequences/03. List Slicing.mp4 20.1 MB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp4 20.1 MB
  • 25. Python - Other Python Operators/02. Logical and Identity Operators.mp4 19.9 MB
  • 42. Part 6 Mathematics/04. Arrays in Python - A Convenient Way To Represent Matrices.mp4 19.9 MB
  • 22. Part 4 Introduction to Python/06. Prerequisites for Coding in the Jupyter Notebooks.mp4 19.9 MB
  • 18. Statistics - Inferential Statistics Confidence Intervals/04. Confidence Interval Clarifications.mp4 19.9 MB
  • 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/11. Machine Learning with Naïve Bayes – converting the problem to a binary one.mp4 19.8 MB
  • 22. Part 4 Introduction to Python/04. Installing Python and Jupyter.mp4 19.7 MB
  • 36. Advanced Statistical Methods - Logistic Regression/06. An Invaluable Coding Tip.mp4 19.7 MB
  • 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/07. Optimizing User Reviews Data Preprocessing & EDA.mp4 19.6 MB
  • 57. Appendix Deep Learning - TensorFlow 1 Business Case/09. Business Case Interpretation.mp4 19.5 MB
  • 39. Advanced Statistical Methods - Other Types of Clustering/03. Heatmaps.mp4 19.4 MB
  • 29. Python - Iterations/06. How to Iterate over Dictionaries.mp4 19.3 MB
  • 05. The Field of Data Science - Popular Data Science Techniques/02. Real Life Examples of Traditional Data.mp4 19.3 MB
  • 15. Statistics - Descriptive Statistics/19. Covariance.mp4 19.3 MB
  • 39. Advanced Statistical Methods - Other Types of Clustering/02. Dendrogram.mp4 19.2 MB
  • 10. Probability - Combinatorics/05. Solving Variations without Repetition.mp4 19.1 MB
  • 28. Python - Sequences/04. Tuples.mp4 19.1 MB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/04. Introduction to Terms with Multiple Meanings.mp4 18.9 MB
  • 40. ChatGPT for Data Science/17. Algorithm recommendation recommendation engine for movies with ChatGPT.mp4 18.7 MB
  • 65. Appendix - pandas Fundamentals/09. Introduction to pandas DataFrames - Part II.mp4 18.7 MB
  • 15. Statistics - Descriptive Statistics/05. Numerical Variables - Frequency Distribution Table.mp4 18.6 MB
  • 55. Appendix Deep Learning - TensorFlow 1 Introduction/07. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 18.5 MB
  • 11. Probability - Bayesian Inference/01. Sets and Events.mp4 18.5 MB
  • 58. Software Integration/04. Communication between Software Products through Text Files.mp4 18.4 MB
  • 50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/04. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 18.4 MB
  • 10. Probability - Combinatorics/02. Permutations and How to Use Them.mp4 18.4 MB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp4 18.2 MB
  • 29. Python - Iterations/04. Conditional Statements and Loops.mp4 18.2 MB
  • 40. ChatGPT for Data Science/16. Algorithm recommendation Movie Database Analysis with ChatGPT.mp4 18.1 MB
  • 17. Statistics - Inferential Statistics Fundamentals/02. What is a Distribution.mp4 18.0 MB
  • 55. Appendix Deep Learning - TensorFlow 1 Introduction/09. Basic NN Example with TF Model Output.mp4 17.9 MB
  • 34. Advanced Statistical Methods - Linear Regression with sklearn/08. Calculating the Adjusted R-Squared in sklearn.mp4 17.7 MB
  • 55. Appendix Deep Learning - TensorFlow 1 Introduction/04. TensorFlow Intro.mp4 17.7 MB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/09. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 17.7 MB
  • 44. Deep Learning - Introduction to Neural Networks/12. Optimization Algorithm n-Parameter Gradient Descent.mp4 17.7 MB
  • 46. Deep Learning - TensorFlow 2.0 Introduction/08. Customizing a TensorFlow 2 Model.mp4 17.6 MB
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/04. Practical Example Linear Regression (Part 3).mp4 17.5 MB
  • 44. Deep Learning - Introduction to Neural Networks/06. The Linear model with Multiple Inputs and Multiple Outputs.mp4 17.4 MB
  • 63. Case Study - Analyzing the Predicted Outputs in Tableau/06. Analyzing Transportation Expense vs Probability in Tableau.mp4 17.3 MB
  • 10. Probability - Combinatorics/09. Combinatorics in Real-Life The Lottery.mp4 17.2 MB
  • 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. Making Predictions with the Linear Regression.mp4 17.1 MB
  • 12. Probability - Distributions/14. Continuous Distributions The Logistic Distribution.mp4 17.0 MB
  • 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/08. Reg Ex for Analyzing Text Review Data.mp4 17.0 MB
  • 54. Deep Learning - Conclusion/06. An Overview of non-NN Approaches.mp4 16.9 MB
  • 29. Python - Iterations/03. Lists with the range() Function.mp4 16.8 MB
  • 58. Software Integration/05. Software Integration - Explained.mp4 16.8 MB
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  • 09. Part 2 Probability/03. Frequency.vtt 7.1 kB
  • 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/12. Testing the Model on New Data.vtt 7.1 kB
  • 34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).vtt 7.1 kB
  • 02. The Field of Data Science - The Various Data Science Disciplines/05. Traditional AI vs. Generative AI.vtt 7.1 kB
  • 15. Statistics - Descriptive Statistics/09. Cross Tables and Scatter Plots.vtt 7.1 kB
  • 57. Appendix Deep Learning - TensorFlow 1 Business Case/08. Business Case Optimization.vtt 7.1 kB
  • 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/03. Digging into a Deep Net.vtt 7.1 kB
  • 02. The Field of Data Science - The Various Data Science Disciplines/06. More Examples of Generative AI.vtt 7.0 kB
  • 30. Python - Advanced Python Tools/01. Object Oriented Programming.vtt 7.0 kB
  • 01. Part 1 Introduction/01. A Practical Example What You Will Learn in This Course.vtt 7.0 kB
  • 26. Python - Conditional Statements/03. The ELIF Statement.vtt 7.0 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/assets/12. Market-segmentation-example-Part2-with-comments.ipynb 7.0 kB
  • 32. Advanced Statistical Methods - Linear Regression with StatsModels/11. R-Squared.vtt 7.0 kB
  • 45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/assets/03. Minimal-example-Part-3.ipynb 7.0 kB
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  • 20. Statistics - Hypothesis Testing/10. Test for the Mean. Dependent Samples.vtt 6.9 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.vtt 6.9 kB
  • 52. Deep Learning - Classifying on the MNIST Dataset/assets/12. TensorFlow-MNIST-complete.ipynb 6.9 kB
  • 29. Python - Iterations/01. For Loops.vtt 6.9 kB
  • 46. Deep Learning - TensorFlow 2.0 Introduction/07. Interpreting the Result and Extracting the Weights and Bias.vtt 6.9 kB
  • 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/11. Machine Learning with Naïve Bayes – converting the problem to a binary one.vtt 6.9 kB
  • 15. Statistics - Descriptive Statistics/03. Categorical Variables - Visualization Techniques.vtt 6.9 kB
  • 45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/02. Basic NN Example (Part 2).vtt 6.8 kB
  • 34. Advanced Statistical Methods - Linear Regression with sklearn/08. Calculating the Adjusted R-Squared in sklearn.vtt 6.8 kB
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  • 46. Deep Learning - TensorFlow 2.0 Introduction/01. How to Install TensorFlow 2.0.vtt 6.7 kB
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  • 04. The Field of Data Science - The Benefits of Each Discipline/01. The Reason Behind These Disciplines.vtt 6.7 kB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.vtt 6.7 kB
  • 52. Deep Learning - Classifying on the MNIST Dataset/04. MNIST Preprocess the Data - Create a Validation Set and Scale It.vtt 6.7 kB
  • 40. ChatGPT for Data Science/14. Decoding comic book data Python Regular Expressions and ChatGPT.vtt 6.6 kB
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  • 40. ChatGPT for Data Science/05. First attempt at machine learning with ChatGPT.vtt 6.5 kB
  • 54. Deep Learning - Conclusion/04. An overview of CNNs.vtt 6.5 kB
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  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/07. Creating a Summary Table with the Coefficients and Intercept.vtt 6.5 kB
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  • 53. Deep Learning - Business Case Example/08. Business Case Learning and Interpreting the Result.vtt 6.5 kB
  • 32. Advanced Statistical Methods - Linear Regression with StatsModels/08. How to Interpret the Regression Table.vtt 6.5 kB
  • 50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/04. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.vtt 6.5 kB
  • 58. Software Integration/01. What are Data, Servers, Clients, Requests, and Responses.vtt 6.4 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/11. Confidence intervals. Two means. Independent Samples (Part 1).vtt 6.4 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/09. To Standardize or not to Standardize.vtt 6.4 kB
  • 11. Probability - Bayesian Inference/04. Union of Sets.vtt 6.4 kB
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  • 39. Advanced Statistical Methods - Other Types of Clustering/03. Heatmaps.vtt 6.3 kB
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  • 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/02. The Naive Bayes Algorithm.vtt 6.2 kB
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  • 10. Probability - Combinatorics/06. Solving Combinations.vtt 6.2 kB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.vtt 6.2 kB
  • 40. ChatGPT for Data Science/assets/19. friendships.csv 6.1 kB
  • 15. Statistics - Descriptive Statistics/11. Mean, median and mode.vtt 6.1 kB
  • 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/07. Optimizing User Reviews Data Preprocessing & EDA.vtt 6.1 kB
  • 25. Python - Other Python Operators/02. Logical and Identity Operators.vtt 6.1 kB
  • 52. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.vtt 6.1 kB
  • 20. Statistics - Hypothesis Testing/08. Test for the Mean. Population Variance Unknown.vtt 6.1 kB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.vtt 6.1 kB
  • 36. Advanced Statistical Methods - Logistic Regression/02. A Simple Example in Python.vtt 6.0 kB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.vtt 6.0 kB
  • 11. Probability - Bayesian Inference/07. The Conditional Probability Formula.vtt 6.0 kB
  • 17. Statistics - Inferential Statistics Fundamentals/02. What is a Distribution.vtt 6.0 kB
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  • 14. Part 3 Statistics/01. Population and Sample.vtt 6.0 kB
  • 05. The Field of Data Science - Popular Data Science Techniques/03. Techniques for Working with Big Data.vtt 6.0 kB
  • 40. ChatGPT for Data Science/08. Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM.vtt 6.0 kB
  • 58. Software Integration/04. Communication between Software Products through Text Files.vtt 6.0 kB
  • 15. Statistics - Descriptive Statistics/01. Types of Data.vtt 6.0 kB
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  • 36. Advanced Statistical Methods - Logistic Regression/assets/15. Testing-the-model.ipynb 5.9 kB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.vtt 5.9 kB
  • 54. Deep Learning - Conclusion/06. An Overview of non-NN Approaches.vtt 5.9 kB
  • 12. Probability - Distributions/10. Continuous Distributions The Standard Normal Distribution.vtt 5.9 kB
  • 34. Advanced Statistical Methods - Linear Regression with sklearn/assets/13. sklearn-Multiple-Linear-Regression-Exercise.ipynb 5.8 kB
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  • 17. Statistics - Inferential Statistics Fundamentals/06. Central Limit Theorem.vtt 5.7 kB
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  • 57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/04. TensorFlow-Audiobooks-Preprocessing.ipynb 5.7 kB
  • 59. Case Study - What's Next in the Course/01. Game Plan for this Python, SQL, and Tableau Business Exercise.vtt 5.7 kB
  • 50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/06. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).vtt 5.7 kB
  • 32. Advanced Statistical Methods - Linear Regression with StatsModels/04. Python Packages Installation.vtt 5.7 kB
  • 65. Appendix - pandas Fundamentals/13. pandas DataFrames - Indexing with .loc[].vtt 5.7 kB
  • 34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.vtt 5.7 kB
  • 42. Part 6 Mathematics/08. Transpose of a Matrix.vtt 5.7 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/assets/07. How-to-Choose-the-Number-of-Clusters-Exercise.ipynb 5.7 kB
  • 08. The Field of Data Science - Debunking Common Misconceptions/01. Debunking Common Misconceptions.vtt 5.7 kB
  • 28. Python - Sequences/03. List Slicing.vtt 5.7 kB
  • 27. Python - Python Functions/assets/07. Notable-Built-In-Functions-in-Python-Solution-Py3.ipynb 5.7 kB
  • 20. Statistics - Hypothesis Testing/04. Type I Error and Type II Error.vtt 5.6 kB
  • 46. Deep Learning - TensorFlow 2.0 Introduction/02. TensorFlow Outline and Comparison with Other Libraries.vtt 5.6 kB
  • 44. Deep Learning - Introduction to Neural Networks/03. Types of Machine Learning.vtt 5.6 kB
  • 54. Deep Learning - Conclusion/01. Summary on What You've Learned.vtt 5.6 kB
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  • 11. Probability - Bayesian Inference/01. Sets and Events.vtt 5.6 kB
  • 23. Python - Variables and Data Types/assets/03. Strings-Solution-Py3.ipynb 5.6 kB
  • 01. Part 1 Introduction/02. What Does the Course Cover.vtt 5.6 kB
  • 12. Probability - Distributions/14. Continuous Distributions The Logistic Distribution.vtt 5.5 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/06. Confidence Intervals; Population Variance Unknown; T-score.vtt 5.5 kB
  • 36. Advanced Statistical Methods - Logistic Regression/assets/13. Calculating-the-Accuracy-of-the-Model-Exercise.ipynb 5.5 kB
  • 20. Statistics - Hypothesis Testing/14. Test for the mean. Independent Samples (Part 2).vtt 5.5 kB
  • 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/09. Understanding Differences between Multinomial and Bernouilli Naive Bayes.vtt 5.5 kB
  • 57. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.vtt 5.5 kB
  • 44. Deep Learning - Introduction to Neural Networks/10. Common Objective Functions Cross-Entropy Loss.vtt 5.5 kB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.vtt 5.5 kB
  • 05. The Field of Data Science - Popular Data Science Techniques/08. Real Life Examples of Traditional Methods.vtt 5.5 kB
  • 55. Appendix Deep Learning - TensorFlow 1 Introduction/04. TensorFlow Intro.vtt 5.5 kB
  • 36. Advanced Statistical Methods - Logistic Regression/assets/02. Admittance-with-comments.ipynb 5.4 kB
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  • 36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.vtt 5.4 kB
  • 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/05. Activation Functions.vtt 5.4 kB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/09. Standardizing only the Numerical Variables (Creating a Custom Scaler).vtt 5.4 kB
  • 12. Probability - Distributions/05. Discrete Distributions The Bernoulli Distribution.vtt 5.3 kB
  • 17. Statistics - Inferential Statistics Fundamentals/03. The Normal Distribution.vtt 5.3 kB
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  • 60. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.vtt 5.3 kB
  • 36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.vtt 5.3 kB
  • 40. ChatGPT for Data Science/07. Analyzing a client database with ChatGPT in Python – analyzing top products.vtt 5.3 kB
  • 02. The Field of Data Science - The Various Data Science Disciplines/02. What is the difference between Analysis and Analytics.vtt 5.2 kB
  • 15. Statistics - Descriptive Statistics/19. Covariance.vtt 5.2 kB
  • 12. Probability - Distributions/09. Continuous Distributions The Normal Distribution.vtt 5.2 kB
  • 02. The Field of Data Science - The Various Data Science Disciplines/07. A Breakdown of our Data Science Infographic.vtt 5.2 kB
  • 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/08. Reg Ex for Analyzing Text Review Data.vtt 5.2 kB
  • 36. Advanced Statistical Methods - Logistic Regression/03. Logistic vs Logit Function.vtt 5.2 kB
  • 39. Advanced Statistical Methods - Other Types of Clustering/01. Types of Clustering.vtt 5.2 kB
  • 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/05. Overcome Imbalanced Data in Machine Learning.vtt 5.2 kB
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  • 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/09. A4 No Autocorrelation.vtt 5.1 kB
  • 48. Deep Learning - Overfitting/03. What is Validation.vtt 5.1 kB
  • 62. Case Study - Loading the 'absenteeism_module'/02. Deploying the 'absenteeism_module' - Part I.vtt 5.1 kB
  • 15. Statistics - Descriptive Statistics/21. Correlation Coefficient.vtt 5.1 kB
  • 37. Advanced Statistical Methods - Cluster Analysis/01. Introduction to Cluster Analysis.vtt 5.1 kB
  • 10. Probability - Combinatorics/05. Solving Variations without Repetition.vtt 5.1 kB
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  • 22. Part 4 Introduction to Python/04. Installing Python and Jupyter.vtt 5.0 kB
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  • 55. Appendix Deep Learning - TensorFlow 1 Introduction/08. Basic NN Example with TF Loss Function and Gradient Descent.vtt 5.0 kB
  • 44. Deep Learning - Introduction to Neural Networks/06. The Linear model with Multiple Inputs and Multiple Outputs.vtt 5.0 kB
  • 15. Statistics - Descriptive Statistics/02. Levels of Measurement.vtt 4.9 kB
  • 43. Part 7 Deep Learning/01. What to Expect from this Part.vtt 4.9 kB
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  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/01. Exploring the Problem with a Machine Learning Mindset.vtt 4.9 kB
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  • 60. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.vtt 4.9 kB
  • 44. Deep Learning - Introduction to Neural Networks/02. Training the Model.vtt 4.9 kB
  • 11. Probability - Bayesian Inference/10. The Multiplication Law.vtt 4.8 kB
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  • 07. The Field of Data Science - Careers in Data Science/01. Finding the Job - What to Expect and What to Look for.vtt 4.8 kB
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  • 11. Probability - Bayesian Inference/02. Ways Sets Can Interact.vtt 4.7 kB
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  • 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/10. A5 No Multicollinearity.vtt 4.7 kB
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  • 60. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.vtt 4.7 kB
  • 38. Advanced Statistical Methods - K-Means Clustering/08. Pros and Cons of K-Means Clustering.vtt 4.7 kB
  • 18. Statistics - Inferential Statistics Confidence Intervals/05. Student's T Distribution.vtt 4.7 kB
  • 42. Part 6 Mathematics/assets/04. Scalars-Vectors-and-Matrices.ipynb 4.7 kB
  • 45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/01. Basic NN Example (Part 1).vtt 4.6 kB
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  • 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/06. Activation Functions Softmax Activation.vtt 4.6 kB
  • 35. Advanced Statistical Methods - Practical Example Linear Regression/04. Practical Example Linear Regression (Part 3).vtt 4.6 kB
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  • 32. Advanced Statistical Methods - Linear Regression with StatsModels/09. Decomposition of Variability.vtt 4.6 kB
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  • 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. Making Predictions with the Linear Regression.vtt 4.5 kB
  • 10. Probability - Combinatorics/02. Permutations and How to Use Them.vtt 4.5 kB
  • 36. Advanced Statistical Methods - Logistic Regression/09. What do the Odds Actually Mean.vtt 4.5 kB
  • 45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/03. Basic NN Example (Part 3).vtt 4.5 kB
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  • 48. Deep Learning - Overfitting/05. N-Fold Cross Validation.vtt 4.5 kB
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  • 40. ChatGPT for Data Science/16. Algorithm recommendation Movie Database Analysis with ChatGPT.vtt 4.5 kB
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  • 42. Part 6 Mathematics/09. Dot Product.vtt 4.4 kB
  • 59. Case Study - What's Next in the Course/03. Introducing the Data Set.vtt 4.4 kB
  • 60. Case Study - Preprocessing the 'Absenteeism_data'/04. Introduction to Terms with Multiple Meanings.vtt 4.4 kB
  • 66. Bonus Lecture/01. Bonus Lecture Next Steps.html 4.4 kB
  • 53. Deep Learning - Business Case Example/03. Business Case Balancing the Dataset.vtt 4.4 kB
  • 46. Deep Learning - TensorFlow 2.0 Introduction/08. Customizing a TensorFlow 2 Model.vtt 4.4 kB
  • 36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.vtt 4.4 kB
  • 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/04. Standardizing the Data.vtt 4.4 kB
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  • 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/04. Non-Linearities and their Purpose.vtt 4.3 kB
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  • 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/09. Backpropagation - A Peek into the Mathematics of Optimization.html 543 Bytes
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  • 14. Part 3 Statistics/external-links/01. Statistics-Flashcards.url 51 Bytes
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