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

Creation Time	July 21, 2025, 6:26 p.m.
Last Access Time	Oct. 23, 2025, 8:56 p.m.
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Keywords	12 Complete 2025 Science - Udemy 2024 Course The Bootcamp Data
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11. Probability - Bayesian Inference/12. A Practical Example of Bayesian Inference.mp4 139.2 MB
12. Probability - Distributions/15. A Practical Example of Probability Distributions.mp4 138.3 MB
16. Statistics - Practical Example Descriptive Statistics/01. Practical Example Descriptive Statistics.mp4 130.5 MB
05. The Field of Data Science - Popular Data Science Techniques/01. Techniques for Working with Traditional Data.mp4 107.2 MB
42. Part 6 Mathematics/11. Why is Linear Algebra Useful.mp4 88.5 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/01. Practical Example Linear Regression (Part 1).mp4 84.7 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 83.5 MB
06. The Field of Data Science - Popular Data Science Tools/01. Necessary Programming Languages and Software Used in Data Science.mp4 82.4 MB
10. Probability - Combinatorics/11. A Practical Example of Combinatorics.mp4 80.7 MB
05. The Field of Data Science - Popular Data Science Techniques/07. Techniques for Working with Traditional Methods.mp4 76.0 MB
57. Appendix Deep Learning - TensorFlow 1 Business Case/04. Business Case Preprocessing.mp4 74.4 MB
53. Deep Learning - Business Case Example/04. Business Case Preprocessing the Data.mp4 73.8 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp4 69.8 MB
05. The Field of Data Science - Popular Data Science Techniques/10. Types of Machine Learning.mp4 69.5 MB
19. Statistics - Practical Example Inferential Statistics/01. Practical Example Inferential Statistics.mp4 69.0 MB
05. The Field of Data Science - Popular Data Science Techniques/03. Techniques for Working with Big Data.mp4 62.1 MB
57. Appendix Deep Learning - TensorFlow 1 Business Case/01. Business Case Getting Acquainted with the Dataset.mp4 60.3 MB
58. Software Integration/02. What are Data Connectivity, APIs, and Endpoints.mp4 60.2 MB
08. The Field of Data Science - Debunking Common Misconceptions/01. Debunking Common Misconceptions.mp4 58.9 MB
57. Appendix Deep Learning - TensorFlow 1 Business Case/06. Creating a Data Provider.mp4 56.3 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/03. Checking the Content of the Data Set.mp4 54.0 MB
05. The Field of Data Science - Popular Data Science Techniques/05. Business Intelligence (BI) Techniques.mp4 52.9 MB
18. Statistics - Inferential Statistics Confidence Intervals/02. Confidence Intervals; Population Variance Known; Z-score.mp4 52.2 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp4 51.3 MB
53. Deep Learning - Business Case Example/01. Business Case Exploring the Dataset and Identifying Predictors.mp4 51.3 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/08. Practical Example Linear Regression (Part 5).mp4 50.4 MB
05. The Field of Data Science - Popular Data Science Techniques/09. Machine Learning (ML) Techniques.mp4 49.4 MB
02. The Field of Data Science - The Various Data Science Disciplines/04. Continuing with BI, ML, and AI.mp4 47.6 MB
04. The Field of Data Science - The Benefits of Each Discipline/01. The Reason Behind These Disciplines.mp4 46.8 MB
21. Statistics - Practical Example Hypothesis Testing/01. Practical Example Hypothesis Testing.mp4 45.8 MB
09. Part 2 Probability/02. Computing Expected Values.mp4 45.7 MB
02. The Field of Data Science - The Various Data Science Disciplines/07. A Breakdown of our Data Science Infographic.mp4 45.4 MB
62. Case Study - Loading the 'absenteeism_module'/03. Deploying the 'absenteeism_module' - Part II.mp4 45.1 MB
18. Statistics - Inferential Statistics Confidence Intervals/09. Confidence intervals. Two means. Dependent samples.mp4 45.0 MB
53. Deep Learning - Business Case Example/09. Business Case Setting an Early Stopping Mechanism.mp4 43.8 MB
64. Appendix - Additional Python Tools/05. List Comprehensions.mp4 43.2 MB
15. Statistics - Descriptive Statistics/01. Types of Data.mp4 43.2 MB
57. Appendix Deep Learning - TensorFlow 1 Business Case/07. Business Case Model Outline.mp4 42.5 MB
41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/02. The Naive Bayes Algorithm.mp4 42.1 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/07. Dropping a Column from a DataFrame in Python.mp4 41.2 MB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/08. Interpreting the Coefficients for Our Problem.mp4 41.1 MB
13. Probability - Probability in Other Fields/01. Probability in Finance.mp4 40.3 MB
63. Case Study - Analyzing the Predicted Outputs in Tableau/04. Analyzing Reasons vs Probability in Tableau.mp4 40.3 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp4 40.1 MB
07. The Field of Data Science - Careers in Data Science/01. Finding the Job - What to Expect and What to Look for.mp4 40.0 MB
45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/04. Basic NN Example (Part 4).mp4 40.0 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/06. Practical Example Linear Regression (Part 4).mp4 39.4 MB
20. Statistics - Hypothesis Testing/03. Rejection Region and Significance Level.mp4 38.7 MB
63. Case Study - Analyzing the Predicted Outputs in Tableau/02. Analyzing Age vs Probability in Tableau.mp4 38.7 MB
56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/09. MNIST Results and Testing.mp4 38.1 MB
38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp4 37.4 MB
09. Part 2 Probability/03. Frequency.mp4 37.4 MB
65. Appendix - pandas Fundamentals/11. Data Selection in pandas DataFrames.mp4 37.3 MB
20. Statistics - Hypothesis Testing/05. Test for the Mean. Population Variance Known.mp4 36.9 MB
05. The Field of Data Science - Popular Data Science Techniques/08. Real Life Examples of Traditional Methods.mp4 36.7 MB
40. ChatGPT for Data Science/05. First attempt at machine learning with ChatGPT.mp4 36.7 MB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/05. Splitting the Data for Training and Testing.mp4 36.1 MB
37. Advanced Statistical Methods - Cluster Analysis/02. Some Examples of Clusters.mp4 35.9 MB
12. Probability - Distributions/02. Types of Probability Distributions.mp4 35.6 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.mp4 35.6 MB
14. Part 3 Statistics/01. Population and Sample.mp4 35.1 MB
56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/04. MNIST Model Outline.mp4 34.7 MB
42. Part 6 Mathematics/10. Dot Product of Matrices.mp4 34.3 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/02. Adjusted R-Squared.mp4 34.2 MB
38. Advanced Statistical Methods - K-Means Clustering/02. A Simple Example of Clustering.mp4 34.2 MB
38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp4 34.1 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp4 33.9 MB
20. Statistics - Hypothesis Testing/07. p-value.mp4 33.7 MB
40. ChatGPT for Data Science/10. Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec.mp4 33.7 MB
40. ChatGPT for Data Science/19. Using ChatGPT for ethical considerations.mp4 33.5 MB
40. ChatGPT for Data Science/14. Decoding comic book data Python Regular Expressions and ChatGPT.mp4 33.1 MB
64. Appendix - Additional Python Tools/04. Triple Nested For Loops.mp4 33.0 MB
20. Statistics - Hypothesis Testing/10. Test for the Mean. Dependent Samples.mp4 32.8 MB
52. Deep Learning - Classifying on the MNIST Dataset/06. MNIST Preprocess the Data - Shuffle and Batch.mp4 32.7 MB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/02. Creating the Targets for the Logistic Regression.mp4 32.4 MB
28. Python - Sequences/05. Dictionaries.mp4 32.4 MB
65. Appendix - pandas Fundamentals/12. pandas DataFrames - Indexing with .iloc[].mp4 32.2 MB
15. Statistics - Descriptive Statistics/02. Levels of Measurement.mp4 32.2 MB
20. Statistics - Hypothesis Testing/01. Null vs Alternative Hypothesis.mp4 31.9 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/02. Practical Example Linear Regression (Part 2).mp4 31.9 MB
56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/08. MNIST Learning.mp4 31.8 MB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp4 31.8 MB
13. Probability - Probability in Other Fields/02. Probability in Statistics.mp4 31.6 MB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp4 31.6 MB
52. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.mp4 31.0 MB
12. Probability - Distributions/06. Discrete Distributions The Binomial Distribution.mp4 30.6 MB
02. The Field of Data Science - The Various Data Science Disciplines/06. More Examples of Generative AI.mp4 30.5 MB
28. Python - Sequences/02. Using Methods.mp4 30.4 MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/05. First Regression in Python.mp4 29.6 MB
53. Deep Learning - Business Case Example/08. Business Case Learning and Interpreting the Result.mp4 29.4 MB
09. Part 2 Probability/01. The Basic Probability Formula.mp4 29.4 MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/08. How to Interpret the Regression Table.mp4 28.7 MB
40. ChatGPT for Data Science/04. Data Preprocessing with ChatGPT.mp4 28.7 MB
18. Statistics - Inferential Statistics Confidence Intervals/01. What are Confidence Intervals.mp4 28.6 MB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp4 28.6 MB
41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/10. Machine Learning with Naïve Bayes (First Attempt).mp4 28.1 MB
38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp4 28.0 MB
05. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Machine Learning (ML).mp4 27.7 MB
17. Statistics - Inferential Statistics Fundamentals/08. Estimators and Estimates.mp4 27.7 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp4 27.6 MB
15. Statistics - Descriptive Statistics/03. Categorical Variables - Visualization Techniques.mp4 27.5 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/03. Simple Linear Regression with sklearn.mp4 27.4 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/08. A3 Normality and Homoscedasticity.mp4 27.4 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp4 27.3 MB
46. Deep Learning - TensorFlow 2.0 Introduction/01. How to Install TensorFlow 2.0.mp4 27.3 MB
05. The Field of Data Science - Popular Data Science Techniques/11. Evolution and Latest Trends of Machine Learning (ML).mp4 27.3 MB
57. Appendix Deep Learning - TensorFlow 1 Business Case/03. The Importance of Working with a Balanced Dataset.mp4 27.3 MB
40. ChatGPT for Data Science/08. Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM.mp4 27.2 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp4 27.0 MB
46. Deep Learning - TensorFlow 2.0 Introduction/06. Outlining the Model with TensorFlow 2.mp4 27.0 MB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/07. Creating a Summary Table with the Coefficients and Intercept.mp4 27.0 MB
57. Appendix Deep Learning - TensorFlow 1 Business Case/08. Business Case Optimization.mp4 26.9 MB
38. Advanced Statistical Methods - K-Means Clustering/06. How to Choose the Number of Clusters.mp4 26.9 MB
40. ChatGPT for Data Science/01. Traditional data science methods and the role of ChatGPT.mp4 26.2 MB
46. Deep Learning - TensorFlow 2.0 Introduction/07. Interpreting the Result and Extracting the Weights and Bias.mp4 25.9 MB
09. Part 2 Probability/04. Events and Their Complements.mp4 25.8 MB
64. Appendix - Additional Python Tools/01. Using the .format() Method.mp4 25.7 MB
65. Appendix - pandas Fundamentals/10. pandas DataFrames - Common Attributes.mp4 25.6 MB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp4 25.5 MB
65. Appendix - pandas Fundamentals/01. Introduction to pandas Series.mp4 25.0 MB
36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp4 24.9 MB
05. The Field of Data Science - Popular Data Science Techniques/06. Real Life Examples of Business Intelligence (BI).mp4 24.6 MB
02. The Field of Data Science - The Various Data Science Disciplines/05. Traditional AI vs. Generative AI.mp4 24.5 MB
58. Software Integration/03. Taking a Closer Look at APIs.mp4 24.5 MB
15. Statistics - Descriptive Statistics/11. Mean, median and mode.mp4 24.5 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.mp4 24.5 MB
20. Statistics - Hypothesis Testing/14. Test for the mean. Independent Samples (Part 2).mp4 24.4 MB
56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/06. Calculating the Accuracy of the Model.mp4 24.4 MB
65. Appendix - pandas Fundamentals/06. Using .unique() and .nunique().mp4 24.3 MB
59. Case Study - What's Next in the Course/03. Introducing the Data Set.mp4 24.2 MB
11. Probability - Bayesian Inference/04. Union of Sets.mp4 24.2 MB
12. Probability - Distributions/07. Discrete Distributions The Poisson Distribution.mp4 23.9 MB
36. Advanced Statistical Methods - Logistic Regression/03. Logistic vs Logit Function.mp4 23.7 MB
47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/03. Digging into a Deep Net.mp4 23.7 MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/04. Python Packages Installation.mp4 23.7 MB
10. Probability - Combinatorics/06. Solving Combinations.mp4 23.6 MB
44. Deep Learning - Introduction to Neural Networks/11. Optimization Algorithm 1-Parameter Gradient Descent.mp4 23.6 MB
15. Statistics - Descriptive Statistics/15. Variance.mp4 23.5 MB
17. Statistics - Inferential Statistics Fundamentals/06. Central Limit Theorem.mp4 23.2 MB
18. Statistics - Inferential Statistics Confidence Intervals/08. Margin of Error.mp4 23.1 MB
28. Python - Sequences/01. Lists.mp4 23.0 MB
52. Deep Learning - Classifying on the MNIST Dataset/04. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp4 22.9 MB
64. Appendix - Additional Python Tools/06. Anonymous (Lambda) Functions.mp4 22.8 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. Dealing with Categorical Data - Dummy Variables.mp4 22.6 MB
52. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.mp4 22.6 MB
47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/04. Non-Linearities and their Purpose.mp4 22.5 MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/10. What is the OLS.mp4 22.5 MB
53. Deep Learning - Business Case Example/03. Business Case Balancing the Dataset.mp4 22.3 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/04. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 22.3 MB
42. Part 6 Mathematics/06. Addition and Subtraction of Matrices.mp4 22.1 MB
52. Deep Learning - Classifying on the MNIST Dataset/08. MNIST Outline the Model.mp4 22.1 MB
36. Advanced Statistical Methods - Logistic Regression/02. A Simple Example in Python.mp4 21.9 MB
40. ChatGPT for Data Science/06. Analyzing a client database with ChatGPT in Python.mp4 21.6 MB
40. ChatGPT for Data Science/09. Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot.mp4 21.6 MB
36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp4 21.6 MB
11. Probability - Bayesian Inference/11. Bayes' Law.mp4 21.3 MB
12. Probability - Distributions/08. Characteristics of Continuous Distributions.mp4 21.3 MB
65. Appendix - pandas Fundamentals/05. Parameters and Arguments in pandas.mp4 21.1 MB
12. Probability - Distributions/10. Continuous Distributions The Standard Normal Distribution.mp4 21.1 MB
41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/12. Testing the Model on New Data.mp4 20.8 MB
65. Appendix - pandas Fundamentals/13. pandas DataFrames - Indexing with .loc[].mp4 20.7 MB
57. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.mp4 20.6 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).mp4 20.5 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.mp4 20.4 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).mp4 20.4 MB
47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/07. Backpropagation.mp4 20.3 MB
10. Probability - Combinatorics/08. Solving Combinations with Separate Sample Spaces.mp4 20.3 MB
36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp4 20.3 MB
11. Probability - Bayesian Inference/10. The Multiplication Law.mp4 20.2 MB
29. Python - Iterations/02. While Loops and Incrementing.mp4 20.2 MB
15. Statistics - Descriptive Statistics/17. Standard Deviation and Coefficient of Variation.mp4 20.1 MB
11. Probability - Bayesian Inference/07. The Conditional Probability Formula.mp4 20.1 MB
12. Probability - Distributions/09. Continuous Distributions The Normal Distribution.mp4 20.0 MB
23. Python - Variables and Data Types/03. Python Strings.mp4 19.7 MB
20. Statistics - Hypothesis Testing/08. Test for the Mean. Population Variance Unknown.mp4 19.7 MB
15. Statistics - Descriptive Statistics/09. Cross Tables and Scatter Plots.mp4 19.7 MB
59. Case Study - What's Next in the Course/01. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 19.7 MB
62. Case Study - Loading the 'absenteeism_module'/02. Deploying the 'absenteeism_module' - Part I.mp4 19.7 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/02. Importing the Absenteeism Data in Python.mp4 19.5 MB
58. Software Integration/01. What are Data, Servers, Clients, Requests, and Responses.mp4 19.5 MB
12. Probability - Distributions/01. Fundamentals of Probability Distributions.mp4 19.4 MB
15. Statistics - Descriptive Statistics/21. Correlation Coefficient.mp4 19.3 MB
28. Python - Sequences/03. List Slicing.mp4 19.2 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp4 19.1 MB
25. Python - Other Python Operators/02. Logical and Identity Operators.mp4 19.0 MB
42. Part 6 Mathematics/04. Arrays in Python - A Convenient Way To Represent Matrices.mp4 19.0 MB
22. Part 4 Introduction to Python/06. Prerequisites for Coding in the Jupyter Notebooks.mp4 19.0 MB
18. Statistics - Inferential Statistics Confidence Intervals/04. Confidence Interval Clarifications.mp4 18.9 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 18.9 MB
22. Part 4 Introduction to Python/04. Installing Python and Jupyter.mp4 18.8 MB
36. Advanced Statistical Methods - Logistic Regression/06. An Invaluable Coding Tip.mp4 18.8 MB
41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/07. Optimizing User Reviews Data Preprocessing & EDA.mp4 18.7 MB
57. Appendix Deep Learning - TensorFlow 1 Business Case/09. Business Case Interpretation.mp4 18.6 MB
39. Advanced Statistical Methods - Other Types of Clustering/03. Heatmaps.mp4 18.5 MB
29. Python - Iterations/06. How to Iterate over Dictionaries.mp4 18.4 MB
05. The Field of Data Science - Popular Data Science Techniques/02. Real Life Examples of Traditional Data.mp4 18.4 MB
15. Statistics - Descriptive Statistics/19. Covariance.mp4 18.4 MB
39. Advanced Statistical Methods - Other Types of Clustering/02. Dendrogram.mp4 18.3 MB
10. Probability - Combinatorics/05. Solving Variations without Repetition.mp4 18.3 MB
28. Python - Sequences/04. Tuples.mp4 18.2 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/04. Introduction to Terms with Multiple Meanings.mp4 18.0 MB
40. ChatGPT for Data Science/17. Algorithm recommendation recommendation engine for movies with ChatGPT.mp4 17.8 MB
65. Appendix - pandas Fundamentals/09. Introduction to pandas DataFrames - Part II.mp4 17.8 MB
15. Statistics - Descriptive Statistics/05. Numerical Variables - Frequency Distribution Table.mp4 17.7 MB
55. Appendix Deep Learning - TensorFlow 1 Introduction/07. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 17.7 MB
11. Probability - Bayesian Inference/01. Sets and Events.mp4 17.7 MB
58. Software Integration/04. Communication between Software Products through Text Files.mp4 17.5 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 17.5 MB
10. Probability - Combinatorics/02. Permutations and How to Use Them.mp4 17.5 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp4 17.3 MB
29. Python - Iterations/04. Conditional Statements and Loops.mp4 17.3 MB
40. ChatGPT for Data Science/16. Algorithm recommendation Movie Database Analysis with ChatGPT.mp4 17.3 MB
17. Statistics - Inferential Statistics Fundamentals/02. What is a Distribution.mp4 17.2 MB
55. Appendix Deep Learning - TensorFlow 1 Introduction/09. Basic NN Example with TF Model Output.mp4 17.1 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/08. Calculating the Adjusted R-Squared in sklearn.mp4 16.9 MB
55. Appendix Deep Learning - TensorFlow 1 Introduction/04. TensorFlow Intro.mp4 16.9 MB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/09. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 16.9 MB
44. Deep Learning - Introduction to Neural Networks/12. Optimization Algorithm n-Parameter Gradient Descent.mp4 16.8 MB
46. Deep Learning - TensorFlow 2.0 Introduction/08. Customizing a TensorFlow 2 Model.mp4 16.8 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/04. Practical Example Linear Regression (Part 3).mp4 16.7 MB
44. Deep Learning - Introduction to Neural Networks/06. The Linear model with Multiple Inputs and Multiple Outputs.mp4 16.6 MB
63. Case Study - Analyzing the Predicted Outputs in Tableau/06. Analyzing Transportation Expense vs Probability in Tableau.mp4 16.5 MB
10. Probability - Combinatorics/09. Combinatorics in Real-Life The Lottery.mp4 16.4 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. Making Predictions with the Linear Regression.mp4 16.3 MB
12. Probability - Distributions/14. Continuous Distributions The Logistic Distribution.mp4 16.2 MB
41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/08. Reg Ex for Analyzing Text Review Data.mp4 16.2 MB
54. Deep Learning - Conclusion/06. An Overview of non-NN Approaches.mp4 16.1 MB
29. Python - Iterations/03. Lists with the range() Function.mp4 16.1 MB
58. Software Integration/05. Software Integration - Explained.mp4 16.0 MB
12. Probability - Distributions/13. Continuous Distributions The Exponential Distribution.mp4 16.0 MB
41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/03. Tokenization and Vectorization.mp4 15.8 MB
56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/05. MNIST Loss and Optimization Algorithm.mp4 15.8 MB
45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/03. Basic NN Example (Part 3).mp4 15.7 MB
02. The Field of Data Science - The Various Data Science Disciplines/01. Data Science and Business Buzzwords Why are there so Many.mp4 15.6 MB
66. Bonus Lecture/assets/01. 365-Data-Science-Data-Science-Interview-Questions-Guide.pdf 15.6 MB
42. Part 6 Mathematics/05. What is a Tensor.mp4 15.5 MB
20. Statistics - Hypothesis Testing/12. Test for the mean. Independent Samples (Part 1).mp4 15.4 MB
46. Deep Learning - TensorFlow 2.0 Introduction/03. TensorFlow 1 vs TensorFlow 2.mp4 15.3 MB
20. Statistics - Hypothesis Testing/04. Type I Error and Type II Error.mp4 15.3 MB
46. Deep Learning - TensorFlow 2.0 Introduction/02. TensorFlow Outline and Comparison with Other Libraries.mp4 15.3 MB
45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/02. Basic NN Example (Part 2).mp4 15.2 MB
65. Appendix - pandas Fundamentals/07. Using .sort_values().mp4 15.2 MB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/06. Fitting the Model and Assessing its Accuracy.mp4 15.2 MB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.mp4 15.2 MB
40. ChatGPT for Data Science/07. Analyzing a client database with ChatGPT in Python – analyzing top products.mp4 15.2 MB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/04. Standardizing the Data.mp4 15.1 MB
12. Probability - Distributions/05. Discrete Distributions The Bernoulli Distribution.mp4 15.1 MB
40. ChatGPT for Data Science/13. Marvels comic book database Intro to Regular Expressions (RegEx).mp4 15.0 MB
11. Probability - Bayesian Inference/06. Dependence and Independence of Sets.mp4 14.9 MB
22. Part 4 Introduction to Python/01. Introduction to Programming.mp4 14.9 MB
41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/06. Loading the Dataset and Preprocessing.mp4 14.8 MB
40. ChatGPT for Data Science/18. Ethical principles in data and AI utilization.mp4 14.7 MB
18. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Independent Samples (Part 2).mp4 14.6 MB
02. The Field of Data Science - The Various Data Science Disciplines/03. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 14.6 MB
36. Advanced Statistical Methods - Logistic Regression/07. Understanding Logistic Regression Tables.mp4 14.6 MB
41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/05. Overcome Imbalanced Data in Machine Learning.mp4 14.6 MB
37. Advanced Statistical Methods - Cluster Analysis/01. Introduction to Cluster Analysis.mp4 14.5 MB
40. ChatGPT for Data Science/12. Hypothesis testing with ChatGPT.mp4 14.4 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp4 14.3 MB
13. Probability - Probability in Other Fields/03. Probability in Data Science.mp4 14.2 MB
26. Python - Conditional Statements/03. The ELIF Statement.mp4 14.2 MB
42. Part 6 Mathematics/08. Transpose of a Matrix.mp4 14.2 MB
11. Probability - Bayesian Inference/08. The Law of Total Probability.mp4 14.2 MB
48. Deep Learning - Overfitting/02. Underfitting and Overfitting for Classification.mp4 14.0 MB
10. Probability - Combinatorics/04. Solving Variations with Repetition.mp4 13.9 MB
41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/09. Understanding Differences between Multinomial and Bernouilli Naive Bayes.mp4 13.9 MB
53. Deep Learning - Business Case Example/06. Business Case Load the Preprocessed Data.mp4 13.8 MB
10. Probability - Combinatorics/07. Symmetry of Combinations.mp4 13.7 MB
42. Part 6 Mathematics/03. Linear Algebra and Geometry.mp4 13.7 MB
18. Statistics - Inferential Statistics Confidence Intervals/06. Confidence Intervals; Population Variance Unknown; T-score.mp4 13.7 MB
18. Statistics - Inferential Statistics Confidence Intervals/05. Student's T Distribution.mp4 13.7 MB
55. Appendix Deep Learning - TensorFlow 1 Introduction/08. Basic NN Example with TF Loss Function and Gradient Descent.mp4 13.6 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.mp4 13.5 MB
17. Statistics - Inferential Statistics Fundamentals/07. Standard error.mp4 13.5 MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/01. The Linear Regression Model.mp4 13.5 MB
54. Deep Learning - Conclusion/04. An overview of CNNs.mp4 13.4 MB
15. Statistics - Descriptive Statistics/13. Skewness.mp4 13.3 MB
65. Appendix - pandas Fundamentals/03. Working with Methods in Python - Part I.mp4 13.2 MB
17. Statistics - Inferential Statistics Fundamentals/03. The Normal Distribution.mp4 13.1 MB
44. Deep Learning - Introduction to Neural Networks/03. Types of Machine Learning.mp4 13.1 MB
05. The Field of Data Science - Popular Data Science Techniques/04. Real Life Examples of Big Data.mp4 13.0 MB
29. Python - Iterations/01. For Loops.mp4 13.0 MB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/01. Exploring the Problem with a Machine Learning Mindset.mp4 13.0 MB
42. Part 6 Mathematics/09. Dot Product.mp4 12.8 MB
64. Appendix - Additional Python Tools/02. Iterating Over Range Objects.mp4 12.6 MB
65. Appendix - pandas Fundamentals/08. Introduction to pandas DataFrames - Part I.mp4 12.5 MB
52. Deep Learning - Classifying on the MNIST Dataset/03. MNIST Importing the Relevant Packages and Loading the Data.mp4 12.2 MB
22. Part 4 Introduction to Python/02. Why Python.mp4 12.2 MB
64. Appendix - Additional Python Tools/03. Introduction to Nested For Loops.mp4 12.2 MB
10. Probability - Combinatorics/10. A Recap of Combinatorics.mp4 12.1 MB
51. Deep Learning - Preprocessing/03. Standardization.mp4 12.1 MB
40. ChatGPT for Data Science/assets/16. movies-metadata.zip 12.0 MB
18. Statistics - Inferential Statistics Confidence Intervals/11. Confidence intervals. Two means. Independent Samples (Part 1).mp4 12.0 MB
42. Part 6 Mathematics/01. What is a Matrix.mp4 11.9 MB
43. Part 7 Deep Learning/01. What to Expect from this Part.mp4 11.7 MB
36. Advanced Statistical Methods - Logistic Regression/09. What do the Odds Actually Mean.mp4 11.4 MB
11. Probability - Bayesian Inference/02. Ways Sets Can Interact.mp4 11.3 MB
59. Case Study - What's Next in the Course/02. The Business Task.mp4 11.3 MB
56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/03. MNIST Relevant Packages.mp4 11.2 MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/11. R-Squared.mp4 11.2 MB
02. The Field of Data Science - The Various Data Science Disciplines/02. What is the difference between Analysis and Analytics.mp4 11.2 MB
12. Probability - Distributions/12. Continuous Distributions The Chi-Squared Distribution.mp4 11.2 MB
38. Advanced Statistical Methods - K-Means Clustering/08. Pros and Cons of K-Means Clustering.mp4 11.1 MB
11. Probability - Bayesian Inference/09. The Additive Rule.mp4 11.1 MB
11. Probability - Bayesian Inference/03. Intersection of Sets.mp4 11.0 MB
38. Advanced Statistical Methods - K-Means Clustering/09. To Standardize or not to Standardize.mp4 10.9 MB
38. Advanced Statistical Methods - K-Means Clustering/01. K-Means Clustering.mp4 10.8 MB
48. Deep Learning - Overfitting/01. What is Overfitting.mp4 10.8 MB
01. Part 1 Introduction/01. A Practical Example What You Will Learn in This Course.mp4 10.8 MB
52. Deep Learning - Classifying on the MNIST Dataset/09. MNIST Select the Loss and the Optimizer.mp4 10.7 MB
11. Probability - Bayesian Inference/05. Mutually Exclusive Sets.mp4 10.6 MB
10. Probability - Combinatorics/03. Simple Operations with Factorials.mp4 10.5 MB
44. Deep Learning - Introduction to Neural Networks/01. Introduction to Neural Networks.mp4 10.5 MB
41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/01. Intro to the Case Study.mp4 10.4 MB
38. Advanced Statistical Methods - K-Means Clustering/04. Clustering Categorical Data.mp4 10.4 MB
12. Probability - Distributions/04. Discrete Distributions The Uniform Distribution.mp4 10.3 MB
48. Deep Learning - Overfitting/06. Early Stopping or When to Stop Training.mp4 10.3 MB
27. Python - Python Functions/07. Built-in Functions in Python.mp4 10.2 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.mp4 10.0 MB
27. Python - Python Functions/02. How to Create a Function with a Parameter.mp4 10.0 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/06. Using a Statistical Approach towards the Solution to the Exercise.mp4 9.9 MB
30. Python - Advanced Python Tools/04. Importing Modules in Python.mp4 9.9 MB
54. Deep Learning - Conclusion/01. Summary on What You've Learned.mp4 9.8 MB
44. Deep Learning - Introduction to Neural Networks/10. Common Objective Functions Cross-Entropy Loss.mp4 9.8 MB
37. Advanced Statistical Methods - Cluster Analysis/03. Difference between Classification and Clustering.mp4 9.7 MB
15. Statistics - Descriptive Statistics/07. The Histogram.mp4 9.6 MB
01. Part 1 Introduction/02. What Does the Course Cover.mp4 9.6 MB
56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/07. MNIST Batching and Early Stopping.mp4 9.5 MB
12. Probability - Distributions/03. Characteristics of Discrete Distributions.mp4 9.4 MB
48. Deep Learning - Overfitting/04. Training, Validation, and Test Datasets.mp4 9.4 MB
45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/01. Basic NN Example (Part 1).mp4 9.3 MB
12. Probability - Distributions/11. Continuous Distributions The Students' T Distribution.mp4 9.2 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/07. A2 No Endogeneity.mp4 9.2 MB
51. Deep Learning - Preprocessing/01. Preprocessing Introduction.mp4 9.2 MB
47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/02. What is a Deep Net.mp4 9.1 MB
55. Appendix Deep Learning - TensorFlow 1 Introduction/05. Actual Introduction to TensorFlow.mp4 9.0 MB
39. Advanced Statistical Methods - Other Types of Clustering/01. Types of Clustering.mp4 9.0 MB
65. Appendix - pandas Fundamentals/04. Working with Methods in Python - Part II.mp4 9.0 MB
23. Python - Variables and Data Types/01. Variables.mp4 8.9 MB
49. Deep Learning - Initialization/01. What is Initialization.mp4 8.9 MB
55. Appendix Deep Learning - TensorFlow 1 Introduction/06. Types of File Formats, supporting Tensors.mp4 8.9 MB
46. Deep Learning - TensorFlow 2.0 Introduction/05. Types of File Formats Supporting TensorFlow.mp4 8.9 MB
47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/05. Activation Functions.mp4 8.8 MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/09. Decomposition of Variability.mp4 8.8 MB
47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/06. Activation Functions Softmax Activation.mp4 8.7 MB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/03. Selecting the Inputs for the Logistic Regression.mp4 8.7 MB
30. Python - Advanced Python Tools/01. Object Oriented Programming.mp4 8.7 MB
12. Probability - Distributions/assets/15. FIFA19-post.csv 8.6 MB
12. Probability - Distributions/assets/15. FIFA19.csv 8.6 MB
24. Python - Basic Python Syntax/01. Using Arithmetic Operators in Python.mp4 8.6 MB
17. Statistics - Inferential Statistics Fundamentals/04. The Standard Normal Distribution.mp4 8.6 MB
36. Advanced Statistical Methods - Logistic Regression/04. Building a Logistic Regression.mp4 8.6 MB
51. Deep Learning - Preprocessing/05. Binary and One-Hot Encoding.mp4 8.5 MB
42. Part 6 Mathematics/02. Scalars and Vectors.mp4 8.5 MB
50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/06. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 8.5 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/01. What is sklearn and How is it Different from Other Packages.mp4 8.5 MB
48. Deep Learning - Overfitting/03. What is Validation.mp4 8.4 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/07. Multiple Linear Regression with sklearn.mp4 8.3 MB
53. Deep Learning - Business Case Example/11. Business Case Testing the Model.mp4 8.2 MB
47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/08. Backpropagation Picture.mp4 8.1 MB
56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/02. MNIST How to Tackle the MNIST.mp4 8.0 MB
22. Part 4 Introduction to Python/03. Why Jupyter.mp4 8.0 MB
44. Deep Learning - Introduction to Neural Networks/04. The Linear Model (Linear Algebraic Version).mp4 8.0 MB
52. Deep Learning - Classifying on the MNIST Dataset/02. MNIST How to Tackle the MNIST.mp4 7.9 MB
44. Deep Learning - Introduction to Neural Networks/05. The Linear Model with Multiple Inputs.mp4 7.9 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/09. A4 No Autocorrelation.mp4 7.9 MB
50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/01. Stochastic Gradient Descent.mp4 7.8 MB
44. Deep Learning - Introduction to Neural Networks/07. Graphical Representation of Simple Neural Networks.mp4 7.8 MB
44. Deep Learning - Introduction to Neural Networks/02. Training the Model.mp4 7.7 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/10. A5 No Multicollinearity.mp4 7.6 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/assets/29. Absenteeism-Exercise-Preprocessing-LECTURES.ipynb 7.6 MB
36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.mp4 7.5 MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/07. Using Seaborn for Graphs.mp4 7.4 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/04. Test for Significance of the Model (F-Test).mp4 7.2 MB
50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/07. Adam (Adaptive Moment Estimation).mp4 7.1 MB
54. Deep Learning - Conclusion/05. An Overview of RNNs.mp4 7.0 MB
02. The Field of Data Science - The Various Data Science Disciplines/assets/04. 365-DataScience.png 6.9 MB
02. The Field of Data Science - The Various Data Science Disciplines/assets/07. 365-DataScience.png 6.9 MB
18. Statistics - Inferential Statistics Confidence Intervals/15. Confidence intervals. Two means. Independent Samples (Part 3).mp4 6.9 MB
26. Python - Conditional Statements/01. The IF Statement.mp4 6.7 MB
23. Python - Variables and Data Types/02. Numbers and Boolean Values in Python.mp4 6.6 MB
41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/04. Imbalanced Data Sets.mp4 6.6 MB
27. Python - Python Functions/03. Defining a Function in Python - Part II.mp4 6.5 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/12. Creating a Summary Table with P-values.mp4 6.4 MB
48. Deep Learning - Overfitting/05. N-Fold Cross Validation.mp4 6.2 MB
44. Deep Learning - Introduction to Neural Networks/08. What is the Objective Function.mp4 6.2 MB
22. Part 4 Introduction to Python/05. Understanding Jupyter's Interface - the Notebook Dashboard.mp4 6.1 MB
27. Python - Python Functions/05. Conditional Statements and Functions.mp4 6.0 MB
26. Python - Conditional Statements/02. The ELSE Statement.mp4 6.0 MB
10. Probability - Combinatorics/01. Fundamentals of Combinatorics.mp4 5.9 MB
36. Advanced Statistical Methods - Logistic Regression/01. Introduction to Logistic Regression.mp4 5.9 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/18. Underfitting and Overfitting.mp4 5.8 MB
60. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.mp4 5.8 MB
42. Part 6 Mathematics/07. Errors when Adding Matrices.mp4 5.8 MB
49. Deep Learning - Initialization/02. Types of Simple Initializations.mp4 5.7 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/01. Multiple Linear Regression.mp4 5.7 MB
44. Deep Learning - Introduction to Neural Networks/09. Common Objective Functions L2-norm Loss.mp4 5.5 MB
49. Deep Learning - Initialization/03. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 5.5 MB
51. Deep Learning - Preprocessing/04. Preprocessing Categorical Data.mp4 5.4 MB
40. ChatGPT for Data Science/03. How ChatGPT can boost your productivity.mp4 5.4 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/02. How are we Going to Approach this Section.mp4 5.3 MB
37. Advanced Statistical Methods - Cluster Analysis/04. Math Prerequisites.mp4 5.3 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/05. OLS Assumptions.mp4 5.3 MB
40. ChatGPT for Data Science/02. How to install ChatGPT.mp4 5.2 MB
50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/03. Momentum.mp4 5.2 MB
47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/01. What is a Layer.mp4 5.2 MB
30. Python - Advanced Python Tools/03. What is the Standard Library.mp4 5.1 MB
55. Appendix Deep Learning - TensorFlow 1 Introduction/02. How to Install TensorFlow 1.mp4 5.0 MB
56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/01. MNIST What is the MNIST Dataset.mp4 4.8 MB
54. Deep Learning - Conclusion/02. What's Further out there in terms of Machine Learning.mp4 4.8 MB
46. Deep Learning - TensorFlow 2.0 Introduction/04. A Note on TensorFlow 2 Syntax.mp4 4.6 MB
52. Deep Learning - Classifying on the MNIST Dataset/01. MNIST The Dataset.mp4 4.5 MB
57. Appendix Deep Learning - TensorFlow 1 Business Case/10. Business Case Testing the Model.mp4 4.4 MB
29. Python - Iterations/05. Conditional Statements, Functions, and Loops.mp4 4.3 MB
26. Python - Conditional Statements/04. A Note on Boolean Values.mp4 4.2 MB
25. Python - Other Python Operators/01. Comparison Operators.mp4 4.2 MB
57. Appendix Deep Learning - TensorFlow 1 Business Case/02. Business Case Outlining the Solution.mp4 4.2 MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/02. Correlation vs Regression.mp4 3.8 MB
50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/02. Problems with Gradient Descent.mp4 3.7 MB
31. Part 5 Advanced Statistical Methods in Python/01. Introduction to Regression Analysis.mp4 3.6 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/06. A1 Linearity.mp4 3.6 MB
40. ChatGPT for Data Science/assets/13. Marvel-Comics.zip 3.5 MB
38. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.mp4 3.5 MB
51. Deep Learning - Preprocessing/02. Types of Basic Preprocessing.mp4 3.2 MB
27. Python - Python Functions/04. How to Use a Function within a Function.mp4 3.2 MB
27. Python - Python Functions/01. Defining a Function in Python.mp4 3.2 MB
50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/05. Learning Rate Schedules Visualized.mp4 3.2 MB
17. Statistics - Inferential Statistics Fundamentals/01. Introduction.mp4 3.1 MB
53. Deep Learning - Business Case Example/02. Business Case Outlining the Solution.mp4 3.0 MB
27. Python - Python Functions/06. Functions Containing a Few Arguments.mp4 2.8 MB
24. Python - Basic Python Syntax/07. Structuring with Indentation.mp4 2.8 MB
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24. Python - Basic Python Syntax/04. Add Comments.mp4 2.4 MB
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40. ChatGPT for Data Science/assets/16. ratings-small.csv 2.3 MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/03. Geometrical Representation of the Linear Regression Model.mp4 2.3 MB
22. Part 4 Introduction to Python/assets/01. Introduction-to-Python-Course-Notes.pdf 2.2 MB
23. Python - Variables and Data Types/assets/01. Introduction-to-Python-Course-Notes.pdf 2.2 MB
30. Python - Advanced Python Tools/02. Modules and Packages.mp4 2.1 MB
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41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/assets/12. 365-User-Reviews-Naive-Bayes-Sentiment-Analysis.ipynb 1.7 MB
24. Python - Basic Python Syntax/05. Understanding Line Continuation.mp4 1.2 MB
20. Statistics - Hypothesis Testing/assets/07. Online-p-value-calculator.pdf 1.2 MB
47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/assets/02. Course-Notes-Section-6.pdf 936.4 KB
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01. Part 1 Introduction/assets/03. FAQ-The-Data-Science-Course.pdf 306.1 KB
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12. Probability - Distributions/assets/15. Customers-Membership.xlsx 9.7 KB
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20. Statistics - Hypothesis Testing/assets/12. 4.8.Test-for-the-mean.Independent-samples-Part-1-lesson.xlsx 9.6 KB
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18. Statistics - Inferential Statistics Confidence Intervals/02. Confidence Intervals; Population Variance Known; Z-score.vtt 9.6 KB
52. Deep Learning - Classifying on the MNIST Dataset/06. MNIST Preprocess the Data - Shuffle and Batch.vtt 9.6 KB
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57. Appendix Deep Learning - TensorFlow 1 Business Case/assets/07. TensorFlow-Audiobooks-Outlining-the-model.ipynb 9.4 KB
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58. Software Integration/02. What are Data Connectivity, APIs, and Endpoints.vtt 9.2 KB
56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/04. MNIST Model Outline.vtt 9.2 KB
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13. Probability - Probability in Other Fields/02. Probability in Statistics.vtt 9.1 KB
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42. Part 6 Mathematics/10. Dot Product of Matrices.vtt 9.1 KB
46. Deep Learning - TensorFlow 2.0 Introduction/assets/06. TensorFlow-Minimal-example-Part2.ipynb 9.1 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/assets/19. sklearn-Train-Test-Split-with-comments.ipynb 9.0 KB
09. Part 2 Probability/01. The Basic Probability Formula.vtt 9.0 KB
28. Python - Sequences/05. Dictionaries.vtt 8.9 KB
60. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.vtt 8.9 KB
05. The Field of Data Science - Popular Data Science Techniques/05. Business Intelligence (BI) Techniques.vtt 8.8 KB
12. Probability - Distributions/06. Discrete Distributions The Binomial Distribution.vtt 8.8 KB
44. Deep Learning - Introduction to Neural Networks/11. Optimization Algorithm 1-Parameter Gradient Descent.vtt 8.8 KB
34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).vtt 8.8 KB
61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/02. Creating the Targets for the Logistic Regression.vtt 8.7 KB
28. Python - Sequences/02. Using Methods.vtt 8.7 KB
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20. Statistics - Hypothesis Testing/03. Rejection Region and Significance Level.vtt 8.6 KB
21. Statistics - Practical Example Hypothesis Testing/01. Practical Example Hypothesis Testing.vtt 8.6 KB
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61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/08. Interpreting the Coefficients for Our Problem.vtt 8.5 KB
56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/assets/07. 12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb 8.5 KB
64. Appendix - Additional Python Tools/03. Introduction to Nested For Loops.vtt 8.5 KB
60. Case Study - Preprocessing the 'Absenteeism_data'/assets/32. Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb 8.5 KB
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61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/05. Splitting the Data for Training and Testing.vtt 8.5 KB
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12. Probability - Distributions/01. Fundamentals of Probability Distributions.vtt 8.4 KB
46. Deep Learning - TensorFlow 2.0 Introduction/06. Outlining the Model with TensorFlow 2.vtt 8.4 KB
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60. Case Study - Preprocessing the 'Absenteeism_data'/07. Dropping a Column from a DataFrame in Python.vtt 8.2 KB
56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/09. MNIST Results and Testing.vtt 8.2 KB
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32. Advanced Statistical Methods - Linear Regression with StatsModels/01. The Linear Regression Model.vtt 8.1 KB
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60. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.vtt 8.0 KB
55. Appendix Deep Learning - TensorFlow 1 Introduction/07. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.vtt 8.0 KB
06. The Field of Data Science - Popular Data Science Tools/01. Necessary Programming Languages and Software Used in Data Science.vtt 8.0 KB
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