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| 1 |
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| 1. Become An Alumni.html |
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| 1. Bonus Lecture.html |
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| 1. Breaking The Flow.mp4 |
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| 1. Course Outline.mp4 |
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| 1. Milestone Projects!.html |
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| 1. Statistics and Mathematics.html |
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| 1. The 2 Paths.mp4 |
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| 1. What Is A Programming Language.mp4 |
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| 1. What Is Machine Learning.mp4 |
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| 10.1 Conda documentation on sharing an environment.html |
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| 10.1 Loading TensorFlow 2.0 into a Colab Notebook (if it isn't the default).html |
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| 10.1 Pandas Categorical Datatype Documentation.html |
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| 10. For Loops.mp4 |
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| 10. Optional Learn SQL.html |
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| 11.1 Introduction to Pandas Jupyter Notebook (with annotations).html |
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| 11.3 Dataquest Jupyter Notebook for Beginners Tutorial.html |
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| 11. Contributing To Open Source.mp4 |
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| 11. Getting Your Data Ready Convert Data To Numbers.mp4 |
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| 11. Preparing Our Data For Machine Learning.mp4 |
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| 11. Reshape and Transpose.mp4 |
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| 12.2 Google Colab Example of GPU speed up versus CPU.html |
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| 12. Apache Spark and Apache Flink.mp4 |
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| 12. Assignment Pandas Practice.html |
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| 12. Choosing The Right Models.mp4 |
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| 12. Dot Product vs Element Wise.mp4 |
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| 12. Fitting A Machine Learning Model.mp4 |
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| 12. Getting Your Data Ready Handling Missing Values With Pandas.mp4 |
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| 12. Jupyter Notebook Walkthrough 2.mp4 |
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| 13.1 Google Colab.html |
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| 13.1 heart-disease.csv |
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| 13.2 Course notebooks - Github.html |
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| 13. Coding Challenges.html |
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| 13. DEVELOPER FUNDAMENTALS I.mp4 |
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| 13. DEVELOPER FUNDAMENTALS I.srt |
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| 13. Exercise Nut Butter Store Sales.mp4 |
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| 13. Experimentation.mp4 |
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| 13. How To Download The Course Assignments.mp4 |
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| 13. Jupyter Notebook Walkthrough 3.mp4 |
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| 13. Kafka and Stream Processing.mp4 |
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| 13. Optional Reloading Colab Notebook.mp4 |
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| 13. Plotting from Pandas DataFrames 4.mp4 |
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| 13. range().mp4 |
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| 14.1 Documentation on how many images Google recommends for image problems.html |
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| 14.1 Exercise Repl.html |
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| 14. Challenge What's wrong with splitting data after filling it.html |
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| 14. Comparison Operators.mp4 |
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| 14. enumerate().mp4 |
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| 14. Exercise Contribute To Open Source.html |
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| 14. Loading Our Data Labels.mp4 |
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| 14. Note Correction in the upcoming video (splitting data).html |
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| 14. Operator Precedence.mp4 |
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| 14. Plotting from Pandas DataFrames 5.mp4 |
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| 14. Tools We Will Use.mp4 |
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| 14. TuningImproving Our Model.mp4 |
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| 15.1 Exercise Repl.html |
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| 15. Custom Evaluation Function.mp4 |
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| 15. Custom Evaluation Function.srt |
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| 15. Exercise Operator Precedence.html |
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| 15. Getting Your Data Ready Handling Missing Values With Scikit-learn.mp4 |
136.89MB |
| 15. Getting Your Data Ready Handling Missing Values With Scikit-learn.srt |
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| 15. Optional Elements of AI.html |
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| 15. Plotting from Pandas DataFrames 6.mp4 |
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| 15. Plotting from Pandas DataFrames 6.srt |
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| 15. Preparing The Images.mp4 |
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| 15. Preparing The Images.srt |
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| 15. Sorting Arrays.mp4 |
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| 15. Tuning Hyperparameters.mp4 |
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| 15. While Loops.mp4 |
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| 16.1 Base Numbers.html |
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| 16.1 Introduction to NumPy Jupyter Notebook (from the videos).html |
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| 16.1 Scikit-Learn machine learning map (how to choose the right machine learning model).html |
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| 16.2 Introduction to NumPy Jupyter Notebook (with annotations).html |
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| 16.3 numpy-images.zip |
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| 16. Choosing The Right Model For Your Data.mp4 |
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| 16. Choosing The Right Model For Your Data.srt |
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| 16. Optional bin() and complex.mp4 |
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| 16. Optional bin() and complex.srt |
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| 16. Plotting from Pandas DataFrames 7.mp4 |
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| 16. Plotting from Pandas DataFrames 7.srt |
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| 16. Reducing Data.mp4 |
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| 16. Tuning Hyperparameters 2.mp4 |
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| 16. Tuning Hyperparameters 2.srt |
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| 16. Turn Images Into NumPy Arrays.mp4 |
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| 16. Turn Images Into NumPy Arrays.srt |
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| 16. Turning Data Labels Into Numbers.mp4 |
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| 16. Turning Data Labels Into Numbers.srt |
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| 16. While Loops 2.mp4 |
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| 17.1 Blog post by Rachel Thomas (of fast.ai) on how and why you should create a validation set.html |
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| 17.1 Python Keywords.html |
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| 17. Assignment NumPy Practice.html |
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| 17. break, continue, pass.mp4 |
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| 17. break, continue, pass.srt |
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| 17. Choosing The Right Model For Your Data 2 (Regression).mp4 |
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| 17. Choosing The Right Model For Your Data 2 (Regression).srt |
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| 17. Creating Our Own Validation Set.mp4 |
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| 17. Creating Our Own Validation Set.srt |
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| 17. Customizing Your Plots.mp4 |
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| 17. Customizing Your Plots.srt |
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| 17. RandomizedSearchCV.mp4 |
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| 17. RandomizedSearchCV.srt |
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| 17. Tuning Hyperparameters 3.mp4 |
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| 17. Variables.mp4 |
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| 18.1 Documentation for loading images in TensorFlow.html |
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| 18.1 Exercise Repl.html |
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| 18.2 Solution Repl.html |
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| 18.2 TensorFlow guidelines for loading all kinds of data (turning your data into Tensors).html |
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| 18. Customizing Your Plots 2.mp4 |
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| 18. Customizing Your Plots 2.srt |
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| 18. Expressions vs Statements.mp4 |
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| 18. Expressions vs Statements.srt |
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| 18. Improving Hyperparameters.mp4 |
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| 18. Improving Hyperparameters.srt |
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| 18. Optional Extra NumPy resources.html |
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| 18. Our First GUI.mp4 |
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| 18. Our First GUI.srt |
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| 18. Preprocess Images.mp4 |
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| 18. Preprocess Images.srt |
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| 18. Quick Note Confusion Matrix Labels.html |
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| 19.1 Exercise Repl.html |
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| 19.1 Introduction to Matplotlib Notebook (from the videos).html |
195B |
| 19. Augmented Assignment Operator.mp4 |
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| 19. Augmented Assignment Operator.srt |
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| 19. DEVELOPER FUNDAMENTALS IV.mp4 |
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| 19. DEVELOPER FUNDAMENTALS IV.srt |
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| 19. Evaluating Our Model.mp4 |
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| 19. Evaluating Our Model.srt |
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| 19. Preproccessing Our Data.mp4 |
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| 19. Preproccessing Our Data.srt |
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| 19. Preprocess Images 2.mp4 |
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| 19. Preprocess Images 2.srt |
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| 19. Quick Tip How ML Algorithms Work.mp4 |
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| 19. Quick Tip How ML Algorithms Work.srt |
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| 19. Saving And Sharing Your Plots.mp4 |
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| 2.1 End-to-end Heart Disease Classification Notebook (with annotations).html |
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| 2.1 How to Think About Communicating and Sharing Your Work (blog post).html |
142B |
| 2.1 Introduction to Matplotlib Jupyter Notebook (from the upcoming videos).html |
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| 2.1 Introduction to NumPy Jupyter Notebook (with annotations).html |
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| 2.1 Introduction to Scikit-Learn Jupyter Notebook (from the upcoming videos).html |
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| 2.1 Kaggle.html |
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| 2.1 python.org.html |
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| 2.1 Structured Data Projects on GitHub.html |
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| 2.2 End-to-end Bluebook Bulldozer Regression Notebook (same as in videos).html |
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| 2.2 Matplotlib Documentation.html |
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| 2.2 NumPy Documentation.html |
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| 2.2 Scikit-Learn Documentation.html |
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| 2.2 Structured Data Projects on GitHub.html |
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| 2.3 End-to-end Bluebook Bulldozer Regression Notebook (with annotations).html |
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| 2.3 End-to-end Heart Disease Classification Notebook (same as in videos).html |
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| 2.3 Introduction to NumPy Jupyter Notebook (from the upcoming videos).html |
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| 2.3 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html |
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| 2.4 Kaggle Bluebook for Bulldozers Competition.html |
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| 2. AIMachine LearningData Science.mp4 |
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| 2. Communicating Your Work.mp4 |
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| 2. Conditional Logic.mp4 |
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| 2. Deep Learning and Unstructured Data.mp4 |
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| 2. Downloading Workbooks and Assignments.html |
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| 2. Introducing Our Framework.mp4 |
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| 2. Introducing Our Framework.srt |
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| 2. Introducing Our Tools.mp4 |
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| 2. Join Our Online Classroom!.html |
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| 2. Matplotlib Introduction.mp4 |
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| 2. Matplotlib Introduction.srt |
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| 2. NumPy Introduction.mp4 |
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| 2. Python + Machine Learning Monthly.html |
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| 2. Quick Note Upcoming Video.html |
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| 2. Scikit-learn Introduction.mp4 |
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| 2. Thank You.mp4 |
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| 20.1 Solution Repl.html |
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| 20. Assignment Matplotlib Practice.html |
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| 20. Choosing The Right Model For Your Data 3 (Classification).mp4 |
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| 20. Exercise Find Duplicates.mp4 |
20.26MB |
| 20. Exercise Find Duplicates.srt |
4.39KB |
| 20. Making Predictions.mp4 |
79.21MB |
| 20. Making Predictions.srt |
11.37KB |
| 20. Strings.mp4 |
30.98MB |
| 20. Strings.srt |
6.29KB |
| 20. Turning Data Into Batches.mp4 |
87.77MB |
| 20. Turning Data Into Batches.srt |
11.61KB |
| 200 |
1.49MB |
| 201 |
1.50MB |
| 202 |
170.18KB |
| 203 |
632.16KB |
| 204 |
1.56MB |
| 205 |
1.69MB |
| 206 |
443.98KB |
| 207 |
1.06MB |
| 208 |
1.17MB |
| 209 |
1.30MB |
| 21 |
1006.09KB |
| 21.1 End-to-end Bluebook Bulldozer Regression Notebook (with annotations).html |
208B |
| 21.1 Yann LeCun's (OG of deep learning) Tweet on Batch Sizes.html |
118B |
| 21.2 End-to-end Bluebook Bulldozer Regression Notebook (same as in videos).html |
214B |
| 21. Evaluating Our Model 3.mp4 |
64.84MB |
| 21. Evaluating Our Model 3.srt |
11.55KB |
| 21. Feature Importance.mp4 |
142.30MB |
| 21. Feature Importance.srt |
17.26KB |
| 21. Fitting A Model To The Data.mp4 |
56.56MB |
| 21. Fitting A Model To The Data.srt |
9.33KB |
| 21. Functions.mp4 |
48.60MB |
| 21. Functions.srt |
9.20KB |
| 21. String Concatenation.mp4 |
7.34MB |
| 21. String Concatenation.srt |
1.42KB |
| 21. Turning Data Into Batches 2.mp4 |
149.38MB |
| 21. Turning Data Into Batches 2.srt |
20.15KB |
| 210 |
1.45MB |
| 211 |
502.61KB |
| 212 |
600.80KB |
| 213 |
1.02MB |
| 214 |
1.31MB |
| 215 |
1.44MB |
| 216 |
1.50MB |
| 217 |
685.17KB |
| 218 |
769.78KB |
| 219 |
1.15MB |
| 22 |
111.04KB |
| 22. Finding The Most Important Features.mp4 |
127.49MB |
| 22. Finding The Most Important Features.srt |
22.33KB |
| 22. Making Predictions With Our Model.mp4 |
66.50MB |
| 22. Making Predictions With Our Model.srt |
12.08KB |
| 22. Parameters and Arguments.mp4 |
23.14MB |
| 22. Parameters and Arguments.srt |
4.88KB |
| 22. Type Conversion.mp4 |
18.99MB |
| 22. Type Conversion.srt |
3.09KB |
| 22. Visualizing Our Data.mp4 |
121.99MB |
| 22. Visualizing Our Data.srt |
15.66KB |
| 220 |
1.47MB |
| 221 |
1.67MB |
| 222 |
1.67MB |
| 223 |
1.67MB |
| 224 |
1.68MB |
| 225 |
1.89MB |
| 226 |
1.97MB |
| 227 |
77.15KB |
| 228 |
336.73KB |
| 229 |
488.40KB |
| 23 |
1.74MB |
| 23.1 End-to-end Heart Disease Classification Notebook (with annotations).html |
201B |
| 23.1 TensorFlow Hub (resource for pre-trained deep learning models and more).html |
79B |
| 23.2 End-to-end Heart Disease Classification Notebook (same as in videos).html |
207B |
| 23. Default Parameters and Keyword Arguments.mp4 |
38.15MB |
| 23. Default Parameters and Keyword Arguments.srt |
5.98KB |
| 23. Escape Sequences.mp4 |
23.16MB |
| 23. Escape Sequences.srt |
5.01KB |
| 23. predict() vs predict_proba().mp4 |
54.33MB |
| 23. predict() vs predict_proba().srt |
11.56KB |
| 23. Preparing Our Inputs and Outputs.mp4 |
50.07MB |
| 23. Preparing Our Inputs and Outputs.srt |
7.78KB |
| 23. Reviewing The Project.mp4 |
86.14MB |
| 23. Reviewing The Project.srt |
13.81KB |
| 230 |
572.74KB |
| 231 |
618.56KB |
| 232 |
682.14KB |
| 233 |
859.04KB |
| 234 |
1.16MB |
| 235 |
1.37MB |
| 236 |
1.62MB |
| 237 |
46.05KB |
| 238 |
71.95KB |
| 239 |
362.20KB |
| 24 |
138.24KB |
| 24.1 Exercise Repl.html |
104B |
| 24. Formatted Strings.mp4 |
49.25MB |
| 24. Formatted Strings.srt |
8.84KB |
| 24. Making Predictions With Our Model (Regression).mp4 |
44.91MB |
| 24. Making Predictions With Our Model (Regression).srt |
9.13KB |
| 24. Optional How machines learn and what's going on behind the scenes.html |
2.72KB |
| 24. return.mp4 |
63.04MB |
| 24. return.srt |
14.97KB |
| 240 |
506.02KB |
| 241 |
1.20MB |
| 242 |
1.71MB |
| 243 |
1.76MB |
| 244 |
428.37KB |
| 245 |
449.99KB |
| 246 |
554.77KB |
| 247 |
775.69KB |
| 248 |
859.93KB |
| 249 |
877.36KB |
| 25 |
521.44KB |
| 25.1 Andrei Karpathy's talk on AI at Tesla.html |
95B |
| 25.1 Exercise Repl.html |
101B |
| 25.2 Papers with Code (a great resource for some of the best machine learning papers with code examples).html |
88B |
| 25.3 MobileNetV2 (the model we're using) on TensorFlow Hub.html |
132B |
| 25.4 PyTorch Hub (PyTorch version of TensorFlow Hub).html |
85B |
| 25.5 TensorFlow Hub (resource for pre-trained deep learning models and more).html |
79B |
| 25. Building A Deep Learning Model.mp4 |
121.85MB |
| 25. Building A Deep Learning Model.srt |
15.92KB |
| 25. Evaluating A Machine Learning Model (Score).mp4 |
87.14MB |
| 25. Evaluating A Machine Learning Model (Score).srt |
12.86KB |
| 25. Exercise Tesla.html |
402B |
| 25. String Indexes.mp4 |
49.15MB |
| 25. String Indexes.srt |
9.21KB |
| 250 |
1.24MB |
| 251 |
1.78MB |
| 252 |
37.34KB |
| 253 |
38.42KB |
| 254 |
105.58KB |
| 255 |
157.58KB |
| 256 |
689.05KB |
| 257 |
751.56KB |
| 258 |
1.13MB |
| 259 |
1.20MB |
| 26 |
1.02MB |
| 26.1 Keras in TensorFlow Overview Documentation.html |
108B |
| 26. Building A Deep Learning Model 2.mp4 |
105.90MB |
| 26. Building A Deep Learning Model 2.srt |
12.54KB |
| 26. Evaluating A Machine Learning Model 2 (Cross Validation).mp4 |
95.97MB |
| 26. Evaluating A Machine Learning Model 2 (Cross Validation).srt |
17.25KB |
| 26. Immutability.mp4 |
20.80MB |
| 26. Immutability.srt |
3.50KB |
| 26. Methods vs Functions.mp4 |
30.69MB |
| 26. Methods vs Functions.srt |
5.25KB |
| 260 |
1.63MB |
| 261 |
1.67MB |
| 262 |
1.74MB |
| 263 |
1.80MB |
| 264 |
1.86MB |
| 265 |
303.94KB |
| 266 |
340.48KB |
| 267 |
351.71KB |
| 268 |
585.23KB |
| 269 |
616.91KB |
| 27 |
553.85KB |
| 27.1 String Methods.html |
115B |
| 27.1 The Softmax Function (activation function we use in our model).html |
107B |
| 27.2 Built in Functions.html |
109B |
| 27.2 Step by step breakdown of a convolutional neural network (what MobileNetV2 is made of).html |
172B |
| 27.3 MobileNetV2 (the model we're using) architecture explanation by Sik-Ho Tsang.html |
163B |
| 27. Building A Deep Learning Model 3.mp4 |
105.92MB |
| 27. Building A Deep Learning Model 3.srt |
11.20KB |
| 27. Built-In Functions + Methods.mp4 |
69.39MB |
| 27. Built-In Functions + Methods.srt |
10.27KB |
| 27. Docstrings.mp4 |
17.34MB |
| 27. Docstrings.srt |
4.28KB |
| 27. Evaluating A Classification Model 1 (Accuracy).mp4 |
31.41MB |
| 27. Evaluating A Classification Model 1 (Accuracy).srt |
5.87KB |
| 270 |
725.01KB |
| 271 |
778.12KB |
| 272 |
846.69KB |
| 273 |
873.71KB |
| 274 |
1.01MB |
| 275 |
1.01MB |
| 276 |
1.62MB |
| 277 |
1.75MB |
| 278 |
251.15KB |
| 279 |
677.97KB |
| 28 |
412.44KB |
| 28.1 [Article] How to choose loss & activation functions when building a deep learning model.html |
169B |
| 28. Booleans.mp4 |
16.55MB |
| 28. Booleans.srt |
3.94KB |
| 28. Building A Deep Learning Model 4.mp4 |
86.30MB |
| 28. Building A Deep Learning Model 4.srt |
12.02KB |
| 28. Clean Code.mp4 |
19.66MB |
| 28. Clean Code.srt |
5.36KB |
| 28. Evaluating A Classification Model 2 (ROC Curve).mp4 |
66.03MB |
| 28. Evaluating A Classification Model 2 (ROC Curve).srt |
12.28KB |
| 280 |
1.01MB |
| 281 |
1.08MB |
| 282 |
1.45MB |
| 283 |
1.46MB |
| 284 |
1.61MB |
| 285 |
17.85KB |
| 286 |
588.37KB |
| 287 |
699.48KB |
| 288 |
865.17KB |
| 289 |
1.07MB |
| 29 |
9.24KB |
| 29. args and kwargs.mp4 |
43.02MB |
| 29. args and kwargs.srt |
8.09KB |
| 29. Evaluating A Classification Model 3 (ROC Curve).mp4 |
50.61MB |
| 29. Evaluating A Classification Model 3 (ROC Curve).srt |
10.04KB |
| 29. Exercise Type Conversion.mp4 |
50.34MB |
| 29. Exercise Type Conversion.srt |
8.58KB |
| 29. Summarizing Our Model.mp4 |
45.44MB |
| 29. Summarizing Our Model.srt |
5.98KB |
| 290 |
1.48MB |
| 291 |
1.57MB |
| 292 |
139.63KB |
| 293 |
512.61KB |
| 294 |
669.16KB |
| 295 |
696.36KB |
| 296 |
1.52MB |
| 297 |
1.54MB |
| 298 |
1.75MB |
| 299 |
1.79MB |
| 3 |
261.51KB |
| 3.1 A 6 Step Field Guide for Machine Learning Modelling (blog post).html |
147B |
| 3.1 Getting started with Conda (documentation).html |
139B |
| 3.1 Glot.io.html |
77B |
| 3.1 Introduction to Pandas Jupyter Notebook (from the upcoming videos).html |
191B |
| 3.1 Teachable Machine.html |
101B |
| 3.2 10-minutes to pandas (from the pandas documentation).html |
127B |
| 3.2 conda-cheatsheet.pdf |
211.29KB |
| 3.2 Repl.it.html |
77B |
| 3.3 Getting your computer ready for machine learning How, what and why you should use Anaconda, Miniconda and Conda (blog post).html |
167B |
| 3.3 Pandas Documentation.html |
106B |
| 3.4 Conda documentation.html |
93B |
| 3.4 Introduction to Pandas Jupyter Notebook (with annotations).html |
185B |
| 3. 6 Step Machine Learning Framework.mp4 |
23.46MB |
| 3. 6 Step Machine Learning Framework.srt |
6.63KB |
| 3. Communicating With Managers.mp4 |
18.38MB |
| 3. Communicating With Managers.srt |
4.53KB |
| 3. Course Review.html |
169B |
| 3. Endorsements On LinkedIN.html |
2.05KB |
| 3. Exercise Machine Learning Playground.mp4 |
42.60MB |
| 3. Exercise Machine Learning Playground.srt |
8.09KB |
| 3. Exercise Meet The Community.html |
2.51KB |
| 3. How To Run Python Code.mp4 |
52.86MB |
| 3. How To Run Python Code.srt |
6.56KB |
| 3. Importing And Using Matplotlib.mp4 |
86.45MB |
| 3. Importing And Using Matplotlib.srt |
16.05KB |
| 3. Indentation In Python.mp4 |
28.03MB |
| 3. Indentation In Python.srt |
5.27KB |
| 3. Pandas Introduction.mp4 |
27.44MB |
| 3. Pandas Introduction.srt |
7.01KB |
| 3. Project Environment Setup.mp4 |
101.27MB |
| 3. Project Environment Setup.mp4 |
100.76MB |
| 3. Project Environment Setup.srt |
15.91KB |
| 3. Project Environment Setup.srt |
14.39KB |
| 3. Quick Note Correction In Next Video.html |
1.25KB |
| 3. Quick Note Upcoming Video.html |
390B |
| 3. Setting Up With Google.html |
568B |
| 3. What If I Don't Have Enough Experience.mp4 |
160.95MB |
| 3. What If I Don't Have Enough Experience.srt |
19.98KB |
| 3. What Is A Data Engineer.mp4 |
15.16MB |
| 3. What Is A Data Engineer.srt |
4.90KB |
| 3. What is Conda.mp4 |
12.48MB |
| 3. What is Conda.srt |
3.41KB |
| 30 |
151.94KB |
| 30.1 Python Comments Best Practices.html |
106B |
| 30.1 Solution Repl.html |
108B |
| 30.1 TensorBoard Callback Documentation.html |
134B |
| 30. DEVELOPER FUNDAMENTALS II.mp4 |
29.25MB |
| 30. DEVELOPER FUNDAMENTALS II.srt |
5.30KB |
| 30. Evaluating Our Model.mp4 |
79.29MB |
| 30. Evaluating Our Model.srt |
10.42KB |
| 30. Exercise Functions.mp4 |
21.85MB |
| 30. Exercise Functions.srt |
4.69KB |
| 30. Reading Extension ROC Curve + AUC.html |
1.48KB |
| 300 |
1.97MB |
| 301 |
636.33KB |
| 302 |
882.91KB |
| 303 |
908.84KB |
| 304 |
957.46KB |
| 305 |
1.03MB |
| 306 |
1.08MB |
| 307 |
1.13MB |
| 308 |
1.30MB |
| 309 |
1.80MB |
| 31 |
227.55KB |
| 31.1 Early Stopping Callback (a way to stop your model from training when it stops improving) Documentation.html |
136B |
| 31.1 Notebook from video with updated confusion matrix labels.html |
191B |
| 31. Evaluating A Classification Model 4 (Confusion Matrix).mp4 |
77.73MB |
| 31. Evaluating A Classification Model 4 (Confusion Matrix).srt |
15.11KB |
| 31. Exercise Password Checker.mp4 |
51.10MB |
| 31. Exercise Password Checker.srt |
7.89KB |
| 31. Preventing Overfitting.mp4 |
36.51MB |
| 31. Preventing Overfitting.srt |
5.54KB |
| 31. Scope.mp4 |
20.14MB |
| 31. Scope.srt |
3.82KB |
| 310 |
1.90MB |
| 311 |
259.75KB |
| 312 |
1.04MB |
| 313 |
1.40MB |
| 314 |
76.75KB |
| 315 |
671.02KB |
| 316 |
748.12KB |
| 317 |
1.97MB |
| 318 |
250.08KB |
| 319 |
454.37KB |
| 32 |
678.20KB |
| 32. Evaluating A Classification Model 5 (Confusion Matrix).mp4 |
63.77MB |
| 32. Evaluating A Classification Model 5 (Confusion Matrix).srt |
11.18KB |
| 32. Lists.mp4 |
21.96MB |
| 32. Lists.srt |
5.57KB |
| 32. Scope Rules.mp4 |
37.68MB |
| 32. Scope Rules.srt |
8.48KB |
| 32. Training Your Deep Neural Network.mp4 |
166.60MB |
| 32. Training Your Deep Neural Network.srt |
23.07KB |
| 33 |
253.82KB |
| 33.1 Exercise Repl.html |
92B |
| 33. Evaluating A Classification Model 6 (Classification Report).mp4 |
87.24MB |
| 33. Evaluating A Classification Model 6 (Classification Report).srt |
14.56KB |
| 33. Evaluating Performance With TensorBoard.mp4 |
74.19MB |
| 33. Evaluating Performance With TensorBoard.srt |
9.57KB |
| 33. global Keyword.mp4 |
36.50MB |
| 33. global Keyword.srt |
6.67KB |
| 33. List Slicing.mp4 |
49.86MB |
| 33. List Slicing.srt |
8.50KB |
| 34 |
706.84KB |
| 34.1 Exercise Repl.html |
93B |
| 34.1 Solution Repl.html |
95B |
| 34. Evaluating A Regression Model 1 (R2 Score).mp4 |
70.39MB |
| 34. Evaluating A Regression Model 1 (R2 Score).srt |
12.01KB |
| 34. Make And Transform Predictions.mp4 |
154.98MB |
| 34. Make And Transform Predictions.srt |
19.18KB |
| 34. Matrix.mp4 |
19.15MB |
| 34. Matrix.srt |
4.13KB |
| 34. nonlocal Keyword.mp4 |
18.25MB |
| 34. nonlocal Keyword.srt |
4.07KB |
| 35 |
781.81KB |
| 35.1 List Methods.html |
113B |
| 35.1 TensorFlow documentation for the unbatch() function.html |
127B |
| 35. Evaluating A Regression Model 2 (MAE).mp4 |
28.53MB |
| 35. Evaluating A Regression Model 2 (MAE).srt |
5.70KB |
| 35. List Methods.mp4 |
61.75MB |
| 35. List Methods.srt |
10.75KB |
| 35. Transform Predictions To Text.mp4 |
129.87MB |
| 35. Transform Predictions To Text.srt |
17.58KB |
| 35. Why Do We Need Scope.mp4 |
19.17MB |
| 35. Why Do We Need Scope.srt |
4.77KB |
| 36 |
1.16MB |
| 36.1 Python Keywords.html |
117B |
| 36.2 Exercise Repl.html |
94B |
| 36. Evaluating A Regression Model 3 (MSE).mp4 |
54.90MB |
| 36. Evaluating A Regression Model 3 (MSE).srt |
9.23KB |
| 36. List Methods 2.mp4 |
27.40MB |
| 36. List Methods 2.srt |
4.48KB |
| 36. Pure Functions.mp4 |
67.36MB |
| 36. Pure Functions.srt |
10.06KB |
| 36. Visualizing Model Predictions.mp4 |
119.31MB |
| 36. Visualizing Model Predictions.srt |
17.02KB |
| 37 |
1.65MB |
| 37. List Methods 3.mp4 |
27.67MB |
| 37. List Methods 3.srt |
5.01KB |
| 37. Machine Learning Model Evaluation.html |
7.12KB |
| 37. map().mp4 |
38.38MB |
| 37. map().srt |
6.29KB |
| 37. Visualizing And Evaluate Model Predictions 2.mp4 |
143.78MB |
| 37. Visualizing And Evaluate Model Predictions 2.srt |
17.64KB |
| 38 |
1.15MB |
| 38.1 Exercise Repl.html |
94B |
| 38. Common List Patterns.mp4 |
40.47MB |
| 38. Common List Patterns.srt |
5.83KB |
| 38. Evaluating A Model With Cross Validation and Scoring Parameter.mp4 |
91.49MB |
| 38. Evaluating A Model With Cross Validation and Scoring Parameter.srt |
17.96KB |
| 38. filter().mp4 |
23.56MB |
| 38. filter().srt |
5.05KB |
| 38. Visualizing And Evaluate Model Predictions 3.mp4 |
113.21MB |
| 38. Visualizing And Evaluate Model Predictions 3.srt |
13.82KB |
| 39 |
1.23MB |
| 39. Evaluating A Model With Scikit-learn Functions.mp4 |
94.82MB |
| 39. Evaluating A Model With Scikit-learn Functions.srt |
16.32KB |
| 39. List Unpacking.mp4 |
13.86MB |
| 39. List Unpacking.srt |
2.91KB |
| 39. Saving And Loading A Trained Model.mp4 |
126.98MB |
| 39. Saving And Loading A Trained Model.srt |
16.85KB |
| 39. zip().mp4 |
21.27MB |
| 39. zip().srt |
3.26KB |
| 4 |
1.40MB |
| 4.1 Kaggle Dog Breed Identification Competition (the basis of our upcoming project).html |
119B |
| 4.1 matplotlib-anatomy-of-a-plot-with-code.png |
654.77KB |
| 4.1 pandas-anatomy-of-a-dataframe.png |
333.24KB |
| 4.1 Truthy vs Falsey Stackoverflow.html |
170B |
| 4.2 Google Colab (our workspace for the upcoming project).html |
95B |
| 4.2 matplotlib-anatomy-of-a-plot.png |
369.39KB |
| 4.3 Google Colab IO example (how to get data in and out of your Colab notebook).html |
113B |
| 4.4 Introduction to Google Colab example notebook.html |
116B |
| 4.5 End-to-end Dog Vision Notebook (the project we'll be working through).html |
182B |
| 4. Anatomy Of A Matplotlib Figure.mp4 |
82.15MB |
| 4. Anatomy Of A Matplotlib Figure.srt |
14.16KB |
| 4. Communicating With Co-Workers.mp4 |
18.99MB |
| 4. Communicating With Co-Workers.srt |
5.54KB |
| 4. Conda Environments.mp4 |
30.56MB |
| 4. Conda Environments.srt |
6.15KB |
| 4. How Did We Get Here.mp4 |
30.50MB |
| 4. How Did We Get Here.srt |
7.07KB |
| 4. Learning Guideline.html |
325B |
| 4. NumPy DataTypes and Attributes.mp4 |
78.99MB |
| 4. NumPy DataTypes and Attributes.srt |
19.19KB |
| 4. Optional Windows Project Environment Setup.mp4 |
35.83MB |
| 4. Optional Windows Project Environment Setup.srt |
5.55KB |
| 4. Our First Python Program.mp4 |
47.20MB |
| 4. Our First Python Program.srt |
9.03KB |
| 4. Refresher What Is Machine Learning.mp4 |
88.27MB |
| 4. Refresher What Is Machine Learning.srt |
6.33KB |
| 4. Series, Data Frames and CSVs.mp4 |
95.37MB |
| 4. Series, Data Frames and CSVs.srt |
16.82KB |
| 4. Setting Up Google Colab.mp4 |
74.24MB |
| 4. Setting Up Google Colab.srt |
9.64KB |
| 4. Step 1~4 Framework Setup.mp4 |
85.69MB |
| 4. Step 1~4 Framework Setup.srt |
12.44KB |
| 4. The Final Challenge.html |
169B |
| 4. Truthy vs Falsey.mp4 |
42.82MB |
| 4. Truthy vs Falsey.srt |
5.99KB |
| 4. Types of Machine Learning Problems.mp4 |
60.50MB |
| 4. Types of Machine Learning Problems.srt |
13.98KB |
| 4. What Is A Data Engineer 2.mp4 |
24.24MB |
| 4. What Is A Data Engineer 2.srt |
6.33KB |
| 4. Your First Day.mp4 |
27.92MB |
| 4. Your First Day.srt |
5.27KB |
| 40 |
1.24MB |
| 40. Improving A Machine Learning Model.mp4 |
90.94MB |
| 40. Improving A Machine Learning Model.srt |
14.86KB |
| 40. None.mp4 |
7.93MB |
| 40. None.srt |
2.19KB |
| 40. reduce().mp4 |
52.27MB |
| 40. reduce().srt |
8.39KB |
| 40. Training Model On Full Dataset.mp4 |
139.82MB |
| 40. Training Model On Full Dataset.srt |
19.17KB |
| 41 |
1.18MB |
| 41.1 Dog Vision Prediction Probabilities Array.html |
170B |
| 41. Dictionaries.mp4 |
32.70MB |
| 41. Dictionaries.srt |
7.09KB |
| 41. List Comprehensions.mp4 |
53.34MB |
| 41. List Comprehensions.srt |
9.38KB |
| 41. Making Predictions On Test Images.mp4 |
140.83MB |
| 41. Making Predictions On Test Images.srt |
20.31KB |
| 41. Tuning Hyperparameters.mp4 |
175.74MB |
| 41. Tuning Hyperparameters.srt |
30.61KB |
| 42 |
810.92KB |
| 42.1 Dog Vision Predictions with MobileNetV2 Ready for Kaggle Submission.html |
180B |
| 42. DEVELOPER FUNDAMENTALS III.mp4 |
26.63MB |
| 42. DEVELOPER FUNDAMENTALS III.srt |
3.59KB |
| 42. Set Comprehensions.mp4 |
35.38MB |
| 42. Set Comprehensions.srt |
6.58KB |
| 42. Submitting Model to Kaggle.mp4 |
121.34MB |
| 42. Submitting Model to Kaggle.srt |
16.58KB |
| 42. Tuning Hyperparameters 2.mp4 |
116.77MB |
| 42. Tuning Hyperparameters 2.srt |
16.97KB |
| 43 |
969.49KB |
| 43.1 End-to-end Dog Vision Notebook (with annotations).html |
185B |
| 43.1 Exercise Repl.html |
100B |
| 43.2 End-to-end Dog Vision Notebook (from the videos).html |
191B |
| 43.2 Solution Repl.html |
102B |
| 43. Dictionary Keys.mp4 |
20.37MB |
| 43. Dictionary Keys.srt |
4.17KB |
| 43. Exercise Comprehensions.mp4 |
21.96MB |
| 43. Exercise Comprehensions.srt |
4.94KB |
| 43. Making Predictions On Our Images.mp4 |
119.24MB |
| 43. Making Predictions On Our Images.srt |
18.57KB |
| 43. Tuning Hyperparameters 3.mp4 |
121.78MB |
| 43. Tuning Hyperparameters 3.srt |
18.82KB |
| 44 |
2.00MB |
| 44.1 Dictionary Methods.html |
119B |
| 44. Dictionary Methods.mp4 |
27.16MB |
| 44. Dictionary Methods.srt |
5.26KB |
| 44. Finishing Dog Vision Where to next.html |
3.86KB |
| 44. Note Metric Comparison Improvement.html |
2.18KB |
| 44. Python Exam Testing Your Understanding.html |
1.12KB |
| 45 |
552.38KB |
| 45.1 Exercise Repl.html |
97B |
| 45. Dictionary Methods 2.mp4 |
42.39MB |
| 45. Dictionary Methods 2.srt |
7.14KB |
| 45. Modules in Python.mp4 |
82.18MB |
| 45. Modules in Python.srt |
12.67KB |
| 45. Quick Tip Correlation Analysis.mp4 |
16.92MB |
| 45. Quick Tip Correlation Analysis.srt |
3.09KB |
| 46 |
1.50MB |
| 46. Quick Note Upcoming Videos.html |
448B |
| 46. Saving And Loading A Model.mp4 |
52.60MB |
| 46. Saving And Loading A Model.srt |
9.85KB |
| 46. Tuples.mp4 |
25.65MB |
| 46. Tuples.srt |
5.69KB |
| 47 |
1.66MB |
| 47.1 Tuple Methods.html |
114B |
| 47. Optional PyCharm.mp4 |
53.06MB |
| 47. Optional PyCharm.srt |
10.51KB |
| 47. Saving And Loading A Model 2.mp4 |
56.77MB |
| 47. Saving And Loading A Model 2.srt |
8.98KB |
| 47. Tuples 2.mp4 |
16.99MB |
| 47. Tuples 2.srt |
3.08KB |
| 48 |
82.64KB |
| 48.1 Reading extension Scikit-Learn's Pipeline class explained.html |
146B |
| 48. Packages in Python.mp4 |
72.42MB |
| 48. Packages in Python.srt |
12.45KB |
| 48. Putting It All Together.mp4 |
150.57MB |
| 48. Putting It All Together.srt |
29.62KB |
| 48. Sets.mp4 |
36.98MB |
| 48. Sets.srt |
8.43KB |
| 49 |
105.44KB |
| 49.1 Exercise Repl.html |
91B |
| 49.1 Introduction to Scikit-Learn Jupyter Notebook (from the videos).html |
197B |
| 49.2 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html |
191B |
| 49.2 Sets Methods.html |
112B |
| 49. Different Ways To Import.mp4 |
47.96MB |
| 49. Different Ways To Import.srt |
7.49KB |
| 49. Putting It All Together 2.mp4 |
116.85MB |
| 49. Putting It All Together 2.srt |
16.11KB |
| 49. Sets 2.mp4 |
64.26MB |
| 49. Sets 2.srt |
9.24KB |
| 5 |
1.05MB |
| 5.1 Google Colab FAQ (things you should know about Google Colab).html |
110B |
| 5.1 Machine Learning Playground.html |
88B |
| 5.1 Miniconda download documentation.html |
107B |
| 5.2 Google Colab (our workspace for the upcoming project).html |
95B |
| 5. Creating NumPy Arrays.mp4 |
66.77MB |
| 5. Creating NumPy Arrays.srt |
12.44KB |
| 5. Data from URLs.html |
1.09KB |
| 5. Downloading the data for the next two projects.html |
1.64KB |
| 5. Exercise YouTube Recommendation Engine.mp4 |
19.43MB |
| 5. Exercise YouTube Recommendation Engine.srt |
5.65KB |
| 5. Google Colab Workspace.mp4 |
39.63MB |
| 5. Google Colab Workspace.srt |
6.32KB |
| 5. Latest Version Of Python.mp4 |
10.70MB |
| 5. Latest Version Of Python.srt |
2.69KB |
| 5. Mac Environment Setup.mp4 |
144.39MB |
| 5. Mac Environment Setup.srt |
23.93KB |
| 5. Quick Note Upcoming Videos.html |
1018B |
| 5. Quick Note Upcoming Videos.html |
565B |
| 5. Scatter Plot And Bar Plot.mp4 |
67.03MB |
| 5. Scatter Plot And Bar Plot.srt |
14.67KB |
| 5. Step 1~4 Framework Setup.mp4 |
105.50MB |
| 5. Step 1~4 Framework Setup.srt |
16.60KB |
| 5. Ternary Operator.mp4 |
19.70MB |
| 5. Ternary Operator.srt |
4.81KB |
| 5. Types of Data.mp4 |
29.33MB |
| 5. Types of Data.srt |
6.52KB |
| 5. Weekend Project Principle.mp4 |
23.58MB |
| 5. Weekend Project Principle.srt |
8.98KB |
| 5. What Is A Data Engineer 3.mp4 |
24.29MB |
| 5. What Is A Data Engineer 3.srt |
5.41KB |
| 50 |
507.13KB |
| 50. Next Steps.html |
959B |
| 50. Scikit-Learn Practice.html |
2.07KB |
| 51 |
949.81KB |
| 51. Bonus Resource Python Cheatsheet.html |
489B |
| 52 |
1.01MB |
| 53 |
1.16MB |
| 54 |
1.23MB |
| 55 |
1.88MB |
| 56 |
103.74KB |
| 57 |
662.69KB |
| 58 |
1.22MB |
| 59 |
1.96MB |
| 6 |
876.91KB |
| 6.1 fast_template by fast.ai (a template you can use for your blog on GitHub Pages).html |
106B |
| 6.1 Kaggle Dog Breed Identification Competition Data.html |
115B |
| 6.1 Python 2 vs Python 3.html |
128B |
| 6.1 Scikit-Learn Reference Notebook.html |
194B |
| 6.2 Devblog by Hashnode (an easy and free way to create a blog you own).html |
89B |
| 6.2 Google Colab IO example (how to get data in and out of your Colab notebook).html |
113B |
| 6.2 The Story of Python.html |
104B |
| 6.3 Python 2 vs Python 3 - another one.html |
161B |
| 6. Communicating With Outside World.mp4 |
14.52MB |
| 6. Communicating With Outside World.srt |
4.51KB |
| 6. Describing Data with Pandas.mp4 |
75.56MB |
| 6. Describing Data with Pandas.srt |
13.58KB |
| 6. Exploring Our Data.mp4 |
137.81MB |
| 6. Exploring Our Data.srt |
19.97KB |
| 6. Getting Our Tools Ready.mp4 |
79.36MB |
| 6. Getting Our Tools Ready.srt |
12.78KB |
| 6. Histograms And Subplots.mp4 |
69.75MB |
| 6. Histograms And Subplots.srt |
12.44KB |
| 6. JTS Learn to Learn.mp4 |
11.14MB |
| 6. JTS Learn to Learn.srt |
2.49KB |
| 6. Mac Environment Setup 2.mp4 |
125.46MB |
| 6. Mac Environment Setup 2.srt |
20.69KB |
| 6. NumPy Random Seed.mp4 |
51.92MB |
| 6. NumPy Random Seed.srt |
9.72KB |
| 6. Python 2 vs Python 3.mp4 |
69.49MB |
| 6. Python 2 vs Python 3.srt |
8.43KB |
| 6. Scikit-learn Cheatsheet.mp4 |
75.13MB |
| 6. Scikit-learn Cheatsheet.srt |
10.08KB |
| 6. Short Circuiting.mp4 |
19.40MB |
| 6. Short Circuiting.srt |
4.47KB |
| 6. Types of Evaluation.mp4 |
17.75MB |
| 6. Types of Evaluation.srt |
4.33KB |
| 6. Types of Machine Learning.mp4 |
22.76MB |
| 6. Types of Machine Learning.srt |
5.27KB |
| 6. Uploading Project Data.mp4 |
51.98MB |
| 6. Uploading Project Data.srt |
8.64KB |
| 6. What Is A Data Engineer 4.mp4 |
14.93MB |
| 6. What Is A Data Engineer 4.srt |
3.86KB |
| 60 |
752.19KB |
| 61 |
1.24MB |
| 62 |
85.49KB |
| 63 |
1.20MB |
| 64 |
1.57MB |
| 65 |
25.84KB |
| 66 |
641.40KB |
| 67 |
1.18MB |
| 68 |
452.26KB |
| 69 |
531.71KB |
| 7 |
1.02MB |
| 7.1 car-sales.csv |
369B |
| 7.1 Example Scikit-Learn Workflow Notebook.html |
192B |
| 7.1 heart-disease.csv |
11.06KB |
| 7.1 Miniconda download documentation.html |
107B |
| 7.1 OLTP vs OLAP.html |
126B |
| 7.2 A Primer on ACID Transactions.html |
117B |
| 7. Are You Getting It Yet.html |
160B |
| 7. Exercise How Does Python Work.mp4 |
25.96MB |
| 7. Exercise How Does Python Work.srt |
2.85KB |
| 7. Exploring Our Data.mp4 |
66.88MB |
| 7. Exploring Our Data.srt |
11.40KB |
| 7. Exploring Our Data 2.mp4 |
52.04MB |
| 7. Exploring Our Data 2.srt |
8.60KB |
| 7. Features In Data.mp4 |
36.78MB |
| 7. Features In Data.srt |
6.75KB |
| 7. JTS Start With Why.mp4 |
15.43MB |
| 7. JTS Start With Why.srt |
2.96KB |
| 7. Logical Operators.mp4 |
28.33MB |
| 7. Logical Operators.srt |
8.10KB |
| 7. Selecting and Viewing Data with Pandas.mp4 |
72.35MB |
| 7. Selecting and Viewing Data with Pandas.srt |
14.59KB |
| 7. Setting Up Our Data.mp4 |
42.26MB |
| 7. Setting Up Our Data.srt |
6.38KB |
| 7. Storytelling.mp4 |
12.03MB |
| 7. Storytelling.srt |
4.10KB |
| 7. Subplots Option 2.mp4 |
38.09MB |
| 7. Subplots Option 2.srt |
6.40KB |
| 7. Types Of Databases.mp4 |
32.55MB |
| 7. Types Of Databases.srt |
8.37KB |
| 7. Typical scikit-learn Workflow.mp4 |
190.18MB |
| 7. Typical scikit-learn Workflow.srt |
31.71KB |
| 7. Viewing Arrays and Matrices.mp4 |
70.64MB |
| 7. Viewing Arrays and Matrices.srt |
12.89KB |
| 7. Windows Environment Setup.mp4 |
47.92MB |
| 7. Windows Environment Setup.srt |
7.62KB |
| 70 |
1.79MB |
| 71 |
520.31KB |
| 72 |
696.56KB |
| 73 |
1000.84KB |
| 74 |
1.06MB |
| 75 |
1.90MB |
| 76 |
1.34MB |
| 77 |
1.73MB |
| 78 |
235.38KB |
| 79 |
776.48KB |
| 8 |
1.43MB |
| 8.1 Standard deviation and variance explained.html |
116B |
| 8. Communicating and sharing your work Further reading.html |
3.14KB |
| 8. Exercise Logical Operators.mp4 |
46.62MB |
| 8. Exercise Logical Operators.srt |
8.40KB |
| 8. Feature Engineering.mp4 |
159.14MB |
| 8. Feature Engineering.srt |
22.13KB |
| 8. Finding Patterns.mp4 |
63.34MB |
| 8. Finding Patterns.srt |
13.39KB |
| 8. Learning Python.mp4 |
38.52MB |
| 8. Learning Python.srt |
2.59KB |
| 8. Manipulating Arrays.mp4 |
80.65MB |
| 8. Manipulating Arrays.srt |
16.17KB |
| 8. Modelling - Splitting Data.mp4 |
27.52MB |
| 8. Modelling - Splitting Data.srt |
7.71KB |
| 8. Optional Debugging Warnings In Jupyter.mp4 |
176.13MB |
| 8. Optional Debugging Warnings In Jupyter.srt |
25.51KB |
| 8. Quick Note Upcoming Video.html |
481B |
| 8. Quick Note Upcoming Videos.html |
352B |
| 8. Quick Tip Data Visualizations.mp4 |
12.25MB |
| 8. Quick Tip Data Visualizations.srt |
2.34KB |
| 8. Selecting and Viewing Data with Pandas Part 2.mp4 |
106.50MB |
| 8. Selecting and Viewing Data with Pandas Part 2.srt |
17.92KB |
| 8. Setting Up Our Data 2.mp4 |
20.87MB |
| 8. Setting Up Our Data 2.srt |
2.18KB |
| 8. What Is Machine Learning Round 2.mp4 |
25.51MB |
| 8. What Is Machine Learning Round 2.srt |
6.07KB |
| 8. Windows Environment Setup 2.mp4 |
227.60MB |
| 8. Windows Environment Setup 2.srt |
31.61KB |
| 80 |
881.70KB |
| 81 |
1.08MB |
| 82 |
1.47MB |
| 83 |
1.55MB |
| 84 |
1.70MB |
| 85 |
1.86MB |
| 86 |
93.48KB |
| 87 |
171.84KB |
| 88 |
320.14KB |
| 89 |
67.08KB |
| 9 |
631.48KB |
| 9.1 Jake VanderPlas's Data Manipulation with Pandas.html |
146B |
| 9.1 scikit-learn-data.zip |
20.83KB |
| 9.1 Standard deviation and variance explained.html |
116B |
| 9.2 car-sales-missing-data.csv |
287B |
| 9. CWD Git + Github.mp4 |
176.11MB |
| 9. CWD Git + Github.srt |
21.17KB |
| 9. Finding Patterns 2.mp4 |
99.92MB |
| 9. Finding Patterns 2.srt |
22.32KB |
| 9. Getting Your Data Ready Splitting Your Data.mp4 |
63.66MB |
| 9. Getting Your Data Ready Splitting Your Data.srt |
12.08KB |
| 9. Importing TensorFlow 2.mp4 |
116.76MB |
| 9. Importing TensorFlow 2.srt |
16.79KB |
| 9. is vs ==.mp4 |
33.57MB |
| 9. is vs ==.srt |
8.12KB |
| 9. Linux Environment Setup.html |
1.03KB |
| 9. Manipulating Arrays 2.mp4 |
67.90MB |
| 9. Manipulating Arrays 2.srt |
11.49KB |
| 9. Manipulating Data.mp4 |
104.99MB |
| 9. Manipulating Data.srt |
18.07KB |
| 9. Modelling - Picking the Model.mp4 |
23.24MB |
| 9. Modelling - Picking the Model.srt |
6.21KB |
| 9. Optional OLTP Databases.mp4 |
79.68MB |
| 9. Optional OLTP Databases.srt |
12.11KB |
| 9. Plotting From Pandas DataFrames.mp4 |
60.35MB |
| 9. Plotting From Pandas DataFrames.srt |
9.02KB |
| 9. Python Data Types.mp4 |
28.85MB |
| 9. Python Data Types.srt |
5.22KB |
| 9. Section Review.mp4 |
5.56MB |
| 9. Section Review.srt |
2.34KB |
| 9. Turning Data Into Numbers.mp4 |
146.17MB |
| 9. Turning Data Into Numbers.srt |
22.32KB |
| 90 |
1.32MB |
| 91 |
1.82MB |
| 92 |
1.85MB |
| 93 |
1.96MB |
| 94 |
1.35MB |
| 95 |
1.41MB |
| 96 |
323.31KB |
| 97 |
652.73KB |
| 98 |
724.17KB |
| 99 |
729.93KB |
| TutsNode.com.txt |
63B |