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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. Data Engineering Introduction.mp4 |
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| 1. Endorsements On LinkedIn.html |
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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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| 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-anatomy-of-a-dataframe.png |
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| 10.1 Pandas Categorical Datatype Documentation.html |
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| 10. Filling Missing Numerical Values.mp4 |
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| 10. For Loops.mp4 |
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| 10. How To Succeed.html |
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| 10. Modelling - Tuning.mp4 |
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| 10. Optional Learn SQL.html |
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| 10. Optional TensorFlow 2.0 Default Issue.mp4 |
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| 10. Quick Note Regular Expressions.html |
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| 10. Sharing your Conda Environment.html |
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| 11.1 Introduction to Pandas Jupyter Notebook (with annotations).html |
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| 11. Contributing To Open Source.mp4 |
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| 11. Filling Missing Categorical Values.mp4 |
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| 11. Getting Your Data Ready Convert Data To Numbers.mp4 |
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| 11. Hadoop, HDFS and MapReduce.mp4 |
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| 11. Modelling - Comparison.mp4 |
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| 11. Plotting From Pandas DataFrames 2.mp4 |
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| 11. Preparing Our Data For Machine Learning.mp4 |
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| 12.1 Introduction to Google Colab example notebook.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. Contributing To Open Source 2.mp4 |
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| 12. Dot Product vs Element Wise.mp4 |
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| 12. Exercise Tricky Counter.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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| 12. Optional GPU and Google Colab.mp4 |
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| 12. Overfitting and Underfitting Definitions.html |
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| 13.1 Google Colab.html |
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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. Exercise Nut Butter Store Sales.mp4 |
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| 13. Experimentation.mp4 |
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| 13. Extension Feature Scaling.html |
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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. Exercise Operator Precedence.html |
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| 15. Getting Your Data Ready Handling Missing Values With Scikit-learn.mp4 |
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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. Preparing The Images.mp4 |
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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. Plotting from Pandas DataFrames 7.mp4 |
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| 16. Reducing Data.mp4 |
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| 16. Tuning Hyperparameters 2.mp4 |
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| 16. Turn Images Into NumPy Arrays.mp4 |
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| 16. Turning Data Labels Into Numbers.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. Choosing The Right Model For Your Data 2 (Regression).mp4 |
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| 17. Creating Our Own Validation Set.mp4 |
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| 17. Customizing Your Plots.mp4 |
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| 17. RandomizedSearchCV.mp4 |
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| 17. Tuning Hyperparameters 3.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. Expressions vs Statements.mp4 |
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| 18. Improving Hyperparameters.mp4 |
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| 18. Our First GUI.mp4 |
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| 18. Preprocess Images.mp4 |
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| 19. DEVELOPER FUNDAMENTALS IV.mp4 |
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| 19. Evaluating Our Model.mp4 |
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| 19. Preproccessing Our Data.mp4 |
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| 19. Quick Tip How ML Algorithms Work.mp4 |
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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 |
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| 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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| 2.3 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. 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. Evaluating Our Model 2.mp4 |
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| 20. Exercise Find Duplicates.mp4 |
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| 21.1 End-to-end Bluebook Bulldozer Regression Notebook (with annotations).html |
208б |
| 21.1 Yann LeCun's (OG of deep learning) Tweet on Batch Sizes.html |
118б |
| 21.2 End-to-end Bluebook Bulldozer Regression Notebook (same as in videos).html |
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| 21. Evaluating Our Model 3.mp4 |
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| 21. Feature Importance.mp4 |
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| 22. Making Predictions With Our Model.mp4 |
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| 22. Parameters and Arguments.mp4 |
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| 22. Type Conversion.mp4 |
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| 22. Visualizing Our Data.mp4 |
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1.74Мб |
| 23.1 End-to-end Heart Disease Classification Notebook (with annotations).html |
201б |
| 23.1 TensorFlow Hub (resource for pre-trained deep learning models and more).html |
79б |
| 23.2 End-to-end Heart Disease Classification Notebook (same as in videos).html |
207б |
| 23. Default Parameters and Keyword Arguments.mp4 |
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| 23. Escape Sequences.mp4 |
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| 23. predict() vs predict_proba().mp4 |
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| 23. Preparing Our Inputs and Outputs.mp4 |
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| 23. Reviewing The Project.mp4 |
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| 24.1 Exercise Repl.html |
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| 24. Formatted Strings.mp4 |
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| 24. Making Predictions With Our Model (Regression).mp4 |
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| 24. Optional How machines learn and what's going on behind the scenes.html |
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| 24. return.mp4 |
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521.44Кб |
| 25.1 Andrei Karpathy's talk on AI at Tesla.html |
95б |
| 25.1 Exercise Repl.html |
101б |
| 25.2 Papers with Code (a great resource for some of the best machine learning papers with code examples).html |
88б |
| 25.3 MobileNetV2 (the model we're using) on TensorFlow Hub.html |
132б |
| 25.4 PyTorch Hub (PyTorch version of TensorFlow Hub).html |
85б |
| 25.5 TensorFlow Hub (resource for pre-trained deep learning models and more).html |
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| 25. Building A Deep Learning Model.mp4 |
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| 25. Evaluating A Machine Learning Model (Score).mp4 |
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| 25. Exercise Tesla.html |
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1.02Мб |
| 26.1 Keras in TensorFlow Overview Documentation.html |
108б |
| 26. Building A Deep Learning Model 2.mp4 |
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| 26. Evaluating A Machine Learning Model 2 (Cross Validation).mp4 |
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| 26. Immutability.mp4 |
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| 26. Methods vs Functions.mp4 |
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| 27.1 String Methods.html |
115б |
| 27.1 The Softmax Function (activation function we use in our model).html |
107б |
| 27.2 Built in Functions.html |
109б |
| 27.2 Step by step breakdown of a convolutional neural network (what MobileNetV2 is made of).html |
172б |
| 27.3 MobileNetV2 (the model we're using) architecture explanation by Sik-Ho Tsang.html |
163б |
| 27. Building A Deep Learning Model 3.mp4 |
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| 27. Evaluating A Classification Model 1 (Accuracy).mp4 |
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| 28.1 [Article] How to choose loss & activation functions when building a deep learning model.html |
169б |
| 28. Booleans.mp4 |
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| 28. Booleans.srt |
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| 28. Building A Deep Learning Model 4.mp4 |
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| 28. Clean Code.mp4 |
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| 28. Evaluating A Classification Model 2 (ROC Curve).mp4 |
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| 29. Evaluating A Classification Model 3 (ROC Curve).mp4 |
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| 29. Exercise Type Conversion.mp4 |
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| 29. Summarizing Our Model.mp4 |
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261.51Кб |
| 3.1 A 6 Step Field Guide for Machine Learning Modelling (blog post).html |
147б |
| 3.1 Getting started with Conda (documentation).html |
139б |
| 3.1 Glot.io.html |
77б |
| 3.1 Introduction to Pandas Jupyter Notebook (from the upcoming videos).html |
191б |
| 3.1 Teachable Machine.html |
101б |
| 3.2 10-minutes to pandas (from the pandas documentation).html |
127б |
| 3.2 conda-cheatsheet.pdf |
211.29Кб |
| 3.2 Repl.it.html |
77б |
| 3.3 Getting your computer ready for machine learning How, what and why you should use Anaconda, Miniconda and Conda (blog post).html |
167б |
| 3.3 Pandas Documentation.html |
106б |
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93б |
| 3.4 Introduction to Pandas Jupyter Notebook (with annotations).html |
185б |
| 3. 6 Step Machine Learning Framework.mp4 |
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| 3. Communicating With Managers.mp4 |
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| 3. Course Review.html |
169б |
| 3. Endorsements On LinkedIN.html |
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| 3. Exercise Machine Learning Playground.mp4 |
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| 3. How To Run Python Code.mp4 |
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| 3. Importing And Using Matplotlib.mp4 |
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| 3. Pandas Introduction.mp4 |
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| 3. Project Environment Setup.mp4 |
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| 3. Quick Note Correction In Next Video.html |
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| 3. Quick Note Upcoming Video.html |
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| 3. Setting Up With Google.html |
568б |
| 3. What If I Don't Have Enough Experience.mp4 |
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| 3. What Is A Data Engineer.mp4 |
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| 3. What is Conda.mp4 |
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| 30 |
151.94Кб |
| 30.1 Python Comments Best Practices.html |
106б |
| 30.1 Solution Repl.html |
108б |
| 30.1 TensorBoard Callback Documentation.html |
134б |
| 30. DEVELOPER FUNDAMENTALS II.mp4 |
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| 30. Evaluating Our Model.mp4 |
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| 30. Exercise Functions.mp4 |
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| 30. Reading Extension ROC Curve + AUC.html |
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| 31.1 Early Stopping Callback (a way to stop your model from training when it stops improving) Documentation.html |
136б |
| 31.1 Notebook from video with updated confusion matrix labels.html |
191б |
| 31. Evaluating A Classification Model 4 (Confusion Matrix).mp4 |
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| 31. Exercise Password Checker.mp4 |
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| 31. Preventing Overfitting.mp4 |
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| 32. Evaluating A Classification Model 5 (Confusion Matrix).mp4 |
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| 32. Lists.mp4 |
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| 32. Scope Rules.mp4 |
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| 32. Training Your Deep Neural Network.mp4 |
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| 33 |
253.82Кб |
| 33.1 Exercise Repl.html |
92б |
| 33. Evaluating A Classification Model 6 (Classification Report).mp4 |
87.24Мб |
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| 33. Evaluating Performance With TensorBoard.mp4 |
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| 33. global Keyword.mp4 |
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| 33. List Slicing.mp4 |
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| 34.1 Exercise Repl.html |
93б |
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| 34. Evaluating A Regression Model 1 (R2 Score).mp4 |
70.39Мб |
| 34. Evaluating A Regression Model 1 (R2 Score).srt |
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| 34. Make And Transform Predictions.mp4 |
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| 34. Matrix.mp4 |
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| 34. nonlocal Keyword.mp4 |
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781.81Кб |
| 35.1 List Methods.html |
113б |
| 35.1 TensorFlow documentation for the unbatch() function.html |
127б |
| 35. Evaluating A Regression Model 2 (MAE).mp4 |
28.53Мб |
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| 35. List Methods.mp4 |
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| 35. Transform Predictions To Text.mp4 |
129.87Мб |
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| 35. Why Do We Need Scope.mp4 |
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1.16Мб |
| 36.1 Python Keywords.html |
117б |
| 36.2 Exercise Repl.html |
94б |
| 36. Evaluating A Regression Model 3 (MSE).mp4 |
54.90Мб |
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| 36. List Methods 2.mp4 |
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| 36. Pure Functions.mp4 |
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| 36. Visualizing Model Predictions.mp4 |
119.31Мб |
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1.65Мб |
| 37. List Methods 3.mp4 |
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| 37. map().mp4 |
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| 37. Visualizing And Evaluate Model Predictions 2.mp4 |
143.78Мб |
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1.15Мб |
| 38.1 Exercise Repl.html |
94б |
| 38. Common List Patterns.mp4 |
40.47Мб |
| 38. Common List Patterns.srt |
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| 38. Evaluating A Model With Cross Validation and Scoring Parameter.mp4 |
91.49Мб |
| 38. Evaluating A Model With Cross Validation and Scoring Parameter.srt |
17.96Кб |
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23.56Мб |
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| 38. Visualizing And Evaluate Model Predictions 3.mp4 |
113.21Мб |
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1.23Мб |
| 39. Evaluating A Model With Scikit-learn Functions.mp4 |
94.82Мб |
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16.32Кб |
| 39. List Unpacking.mp4 |
13.86Мб |
| 39. List Unpacking.srt |
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| 39. Saving And Loading A Trained Model.mp4 |
126.98Мб |
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| 39. zip().mp4 |
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| 4 |
1.40Мб |
| 4.1 Kaggle Dog Breed Identification Competition (the basis of our upcoming project).html |
119б |
| 4.1 matplotlib-anatomy-of-a-plot-with-code.png |
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| 4.1 pandas-anatomy-of-a-dataframe.png |
333.24Кб |
| 4.1 Truthy vs Falsey Stackoverflow.html |
170б |
| 4.2 Google Colab (our workspace for the upcoming project).html |
95б |
| 4.2 matplotlib-anatomy-of-a-plot.png |
369.39Кб |
| 4.3 Google Colab IO example (how to get data in and out of your Colab notebook).html |
113б |
| 4.4 Introduction to Google Colab example notebook.html |
116б |
| 4.5 End-to-end Dog Vision Notebook (the project we'll be working through).html |
182б |
| 4. Anatomy Of A Matplotlib Figure.mp4 |
82.15Мб |
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18.99Мб |
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30.56Мб |
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30.50Мб |
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| 4. Learning Guideline.html |
325б |
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78.99Мб |
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| 4. Optional Windows Project Environment Setup.mp4 |
35.83Мб |
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47.20Мб |
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| 4. Refresher What Is Machine Learning.mp4 |
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