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| 1. Become An Alumni.html |
944б |
| 1. Bonus Lecture.html |
3.29Кб |
| 1. Breaking The Flow.mp4 |
20.33Мб |
| 1. Breaking The Flow.srt |
2.98Кб |
| 1. Course Outline.mp4 |
77.26Мб |
| 1. Course Outline.srt |
9.17Кб |
| 1. Data Engineering Introduction.mp4 |
13.50Мб |
| 1. Data Engineering Introduction.srt |
4.25Кб |
| 1. Endorsements On LinkedIn.html |
2.05Кб |
| 1. Milestone Projects!.html |
738б |
| 1. Section Overview.mp4 |
13.35Мб |
| 1. Section Overview.mp4 |
6.03Мб |
| 1. Section Overview.mp4 |
10.87Мб |
| 1. Section Overview.mp4 |
13.32Мб |
| 1. Section Overview.mp4 |
8.60Мб |
| 1. Section Overview.mp4 |
12.46Мб |
| 1. Section Overview.mp4 |
10.20Мб |
| 1. Section Overview.mp4 |
8.96Мб |
| 1. Section Overview.mp4 |
12.21Мб |
| 1. Section Overview.mp4 |
10.92Мб |
| 1. Section Overview.srt |
4.65Кб |
| 1. Section Overview.srt |
2.12Кб |
| 1. Section Overview.srt |
3.75Кб |
| 1. Section Overview.srt |
3.11Кб |
| 1. Section Overview.srt |
2.69Кб |
| 1. Section Overview.srt |
4.10Кб |
| 1. Section Overview.srt |
3.11Кб |
| 1. Section Overview.srt |
1.84Кб |
| 1. Section Overview.srt |
2.77Кб |
| 1. Section Overview.srt |
4.89Мб |
| 1. Statistics and Mathematics.html |
710б |
| 1. The 2 Paths.mp4 |
9.75Мб |
| 1. The 2 Paths.srt |
4.71Кб |
| 1. What Is A Programming Language.mp4 |
104.77Мб |
| 1. What Is A Programming Language.srt |
7.04Кб |
| 1. What Is Machine Learning.mp4 |
28.33Мб |
| 1. What Is Machine Learning.srt |
8.67Кб |
| 10.1 Conda documentation on sharing an environment.html |
172б |
| 10.1 Loading TensorFlow 2.0 into a Colab Notebook (if it isn't the default).html |
129б |
| 10.1 pandas-anatomy-of-a-dataframe.png |
333.24Кб |
| 10.1 Pandas Categorical Datatype Documentation.html |
143б |
| 10.1 Standard deviation and variance explained.html |
116б |
| 10. CWD Git + Github 2.mp4 |
118.35Мб |
| 10. CWD Git + Github 2.srt |
18.25Кб |
| 10. Filling Missing Numerical Values.mp4 |
106.34Мб |
| 10. Filling Missing Numerical Values.srt |
16.94Кб |
| 10. Finding Patterns 3.mp4 |
137.86Мб |
| 10. Finding Patterns 3.srt |
18.88Кб |
| 10. For Loops.mp4 |
34.31Мб |
| 10. For Loops.srt |
7.53Кб |
| 10. How To Succeed.html |
280б |
| 10. Manipulating Data 2.mp4 |
86.53Мб |
| 10. Manipulating Data 2.srt |
13.85Кб |
| 10. Modelling - Tuning.mp4 |
15.98Мб |
| 10. Modelling - Tuning.srt |
4.86Кб |
| 10. Optional Learn SQL.html |
410б |
| 10. Optional TensorFlow 2.0 Default Issue.mp4 |
28.11Мб |
| 10. Optional TensorFlow 2.0 Default Issue.srt |
4.48Кб |
| 10. Quick Note Regular Expressions.html |
632б |
| 10. Quick Tip Clean, Transform, Reduce.mp4 |
16.54Мб |
| 10. Quick Tip Clean, Transform, Reduce.srt |
6.42Кб |
| 10. Sharing your Conda Environment.html |
2.41Кб |
| 10. Standard Deviation and Variance.mp4 |
51.16Мб |
| 10. Standard Deviation and Variance.srt |
9.35Кб |
| 11.1 6-step-ml-framework.png |
324.24Кб |
| 11.1 Floating point numbers.html |
104б |
| 11.1 Google Colab example GPU usage.html |
114б |
| 11.1 Introduction to Pandas Jupyter Notebook (with annotations).html |
185б |
| 11.2 heart-disease.csv |
11.06Кб |
| 11.2 Introduction to Pandas Jupyter Notebook (from the videos).html |
191б |
| 11.3 Dataquest Jupyter Notebook for Beginners Tutorial.html |
117б |
| 11.4 Jupyter Notebook documentation.html |
111б |
| 11. Contributing To Open Source.mp4 |
130.26Мб |
| 11. Contributing To Open Source.srt |
17.13Кб |
| 11. Filling Missing Categorical Values.mp4 |
66.92Мб |
| 11. Filling Missing Categorical Values.srt |
11.20Кб |
| 11. Getting Your Data Ready Convert Data To Numbers.mp4 |
135.02Мб |
| 11. Getting Your Data Ready Convert Data To Numbers.srt |
22.71Кб |
| 11. Hadoop, HDFS and MapReduce.mp4 |
10.10Мб |
| 11. Hadoop, HDFS and MapReduce.srt |
4.70Кб |
| 11. Iterables.mp4 |
43.21Мб |
| 11. Iterables.srt |
6.85Кб |
| 11. Jupyter Notebook Walkthrough.mp4 |
67.35Мб |
| 11. Jupyter Notebook Walkthrough.srt |
15.14Кб |
| 11. Manipulating Data 3.mp4 |
91.02Мб |
| 11. Manipulating Data 3.srt |
13.71Кб |
| 11. Modelling - Comparison.mp4 |
44.88Мб |
| 11. Modelling - Comparison.srt |
13.09Кб |
| 11. Numbers.mp4 |
72.71Мб |
| 11. Numbers.srt |
11.13Кб |
| 11. Plotting From Pandas DataFrames 2.mp4 |
98.80Мб |
| 11. Plotting From Pandas DataFrames 2.srt |
13.63Кб |
| 11. Preparing Our Data For Machine Learning.mp4 |
72.60Мб |
| 11. Preparing Our Data For Machine Learning.srt |
12.02Кб |
| 11. Reshape and Transpose.mp4 |
53.53Мб |
| 11. Reshape and Transpose.srt |
9.53Кб |
| 11. Using A GPU.mp4 |
80.59Мб |
| 11. Using A GPU.srt |
12.11Кб |
| 12.1 Introduction to Google Colab example notebook.html |
116б |
| 12.1 Matrix Multiplication Explained.html |
119б |
| 12.1 Solution Repl.html |
92б |
| 12.2 Google Colab Example of GPU speed up versus CPU.html |
114б |
| 12. Apache Spark and Apache Flink.mp4 |
5.76Мб |
| 12. Apache Spark and Apache Flink.srt |
2.31Кб |
| 12. Assignment Pandas Practice.html |
2.05Кб |
| 12. Choosing The Right Models.mp4 |
96.43Мб |
| 12. Choosing The Right Models.srt |
12.97Кб |
| 12. Contributing To Open Source 2.mp4 |
113.05Мб |
| 12. Contributing To Open Source 2.srt |
10.18Кб |
| 12. Dot Product vs Element Wise.mp4 |
83.93Мб |
| 12. Dot Product vs Element Wise.srt |
15.34Кб |
| 12. Exercise Tricky Counter.mp4 |
16.39Мб |
| 12. Exercise Tricky Counter.srt |
3.58Кб |
| 12. Fitting A Machine Learning Model.mp4 |
55.52Мб |
| 12. Fitting A Machine Learning Model.srt |
10.47Кб |
| 12. Getting Your Data Ready Handling Missing Values With Pandas.mp4 |
104.84Мб |
| 12. Getting Your Data Ready Handling Missing Values With Pandas.srt |
16.94Кб |
| 12. Jupyter Notebook Walkthrough 2.mp4 |
103.90Мб |
| 12. Jupyter Notebook Walkthrough 2.srt |
22.48Кб |
| 12. Math Functions.mp4 |
41.82Мб |
| 12. Math Functions.srt |
5.43Кб |
| 12. Optional GPU and Google Colab.mp4 |
45.88Мб |
| 12. Optional GPU and Google Colab.srt |
5.99Кб |
| 12. Overfitting and Underfitting Definitions.html |
1.97Кб |
| 12. Plotting from Pandas DataFrames 3.mp4 |
74.71Мб |
| 12. Plotting from Pandas DataFrames 3.srt |
11.46Кб |
| 13.1 Google Colab.html |
95б |
| 13.1 heart-disease.csv |
11.06Кб |
| 13.2 Course notebooks - Github.html |
108б |
| 13. Coding Challenges.html |
948б |
| 13. DEVELOPER FUNDAMENTALS I.mp4 |
59.71Мб |
| 13. DEVELOPER FUNDAMENTALS I.srt |
5.22Кб |
| 13. Exercise Nut Butter Store Sales.mp4 |
91.32Мб |
| 13. Exercise Nut Butter Store Sales.srt |
16.96Кб |
| 13. Experimentation.mp4 |
21.33Мб |
| 13. Experimentation.srt |
4.98Кб |
| 13. Experimenting With Machine Learning Models.mp4 |
55.35Мб |
| 13. Experimenting With Machine Learning Models.srt |
9.63Кб |
| 13. Extension Feature Scaling.html |
2.93Кб |
| 13. How To Download The Course Assignments.mp4 |
66.78Мб |
| 13. How To Download The Course Assignments.srt |
11.06Кб |
| 13. Jupyter Notebook Walkthrough 3.mp4 |
71.42Мб |
| 13. Jupyter Notebook Walkthrough 3.srt |
11.49Кб |
| 13. Kafka and Stream Processing.mp4 |
19.24Мб |
| 13. Kafka and Stream Processing.srt |
5.05Кб |
| 13. Optional Reloading Colab Notebook.mp4 |
88.66Мб |
| 13. Optional Reloading Colab Notebook.srt |
7.77Кб |
| 13. Plotting from Pandas DataFrames 4.mp4 |
49.00Мб |
| 13. Plotting from Pandas DataFrames 4.srt |
9.41Кб |
| 13. range().mp4 |
28.33Мб |
| 13. range().srt |
5.86Кб |
| 13. Splitting Data.mp4 |
82.68Мб |
| 13. Splitting Data.srt |
13.51Кб |
| 14.1 Documentation on how many images Google recommends for image problems.html |
129б |
| 14.1 Exercise Repl.html |
106б |
| 14. Challenge What's wrong with splitting data after filling it.html |
1.72Кб |
| 14. Comparison Operators.mp4 |
26.38Мб |
| 14. Comparison Operators.srt |
5.26Кб |
| 14. enumerate().mp4 |
24.80Мб |
| 14. enumerate().srt |
4.56Кб |
| 14. Exercise Contribute To Open Source.html |
1.45Кб |
| 14. Loading Our Data Labels.mp4 |
114.82Мб |
| 14. Loading Our Data Labels.srt |
16.08Кб |
| 14. Note Correction in the upcoming video (splitting data).html |
2.16Кб |
| 14. Operator Precedence.mp4 |
14.43Мб |
| 14. Operator Precedence.srt |
3.50Кб |
| 14. Plotting from Pandas DataFrames 5.mp4 |
56.96Мб |
| 14. Plotting from Pandas DataFrames 5.srt |
11.63Кб |
| 14. Tools We Will Use.mp4 |
27.33Мб |
| 14. Tools We Will Use.srt |
5.99Кб |
| 14. TuningImproving Our Model.mp4 |
102.78Мб |
| 14. TuningImproving Our Model.srt |
17.64Кб |
| 15.1 Exercise Repl.html |
106б |
| 15. Custom Evaluation Function.mp4 |
103.35Мб |
| 15. Custom Evaluation Function.srt |
16.11Кб |
| 15. Exercise Operator Precedence.html |
683б |
| 15. Getting Your Data Ready Handling Missing Values With Scikit-learn.mp4 |
136.89Мб |
| 15. Getting Your Data Ready Handling Missing Values With Scikit-learn.srt |
23.13Кб |
| 15. Optional Elements of AI.html |
975б |
| 15. Plotting from Pandas DataFrames 6.mp4 |
82.04Мб |
| 15. Plotting from Pandas DataFrames 6.srt |
11.08Кб |
| 15. Preparing The Images.mp4 |
133.89Мб |
| 15. Preparing The Images.srt |
15.12Кб |
| 15. Sorting Arrays.mp4 |
32.83Мб |
| 15. Sorting Arrays.srt |
8.80Кб |
| 15. Tuning Hyperparameters.mp4 |
108.00Мб |
| 15. Tuning Hyperparameters.srt |
15.67Кб |
| 15. While Loops.mp4 |
28.32Мб |
| 15. While Loops.srt |
7.36Кб |
| 16.1 Base Numbers.html |
111б |
| 16.1 Introduction to NumPy Jupyter Notebook (from the videos).html |
190б |
| 16.1 Scikit-Learn machine learning map (how to choose the right machine learning model).html |
133б |
| 16.2 Introduction to NumPy Jupyter Notebook (with annotations).html |
184б |
| 16.3 numpy-images.zip |
7.27Мб |
| 16. Choosing The Right Model For Your Data.mp4 |
143.26Мб |
| 16. Choosing The Right Model For Your Data.srt |
21.38Кб |
| 16. Optional bin() and complex.mp4 |
21.90Мб |
| 16. Optional bin() and complex.srt |
4.80Кб |
| 16. Plotting from Pandas DataFrames 7.mp4 |
119.75Мб |
| 16. Plotting from Pandas DataFrames 7.srt |
14.95Кб |
| 16. Reducing Data.mp4 |
93.48Мб |
| 16. Reducing Data.srt |
14.62Кб |
| 16. Tuning Hyperparameters 2.mp4 |
104.12Мб |
| 16. Tuning Hyperparameters 2.srt |
15.10Кб |
| 16. Turn Images Into NumPy Arrays.mp4 |
85.91Мб |
| 16. Turn Images Into NumPy Arrays.srt |
10.42Кб |
| 16. Turning Data Labels Into Numbers.mp4 |
107.46Мб |
| 16. Turning Data Labels Into Numbers.srt |
13.76Кб |
| 16. While Loops 2.mp4 |
25.93Мб |
| 16. While Loops 2.srt |
6.42Кб |
| 17.1 Blog post by Rachel Thomas (of fast.ai) on how and why you should create a validation set.html |
108б |
| 17.1 Python Keywords.html |
117б |
| 17. Assignment NumPy Practice.html |
2.17Кб |
| 17. break, continue, pass.mp4 |
22.22Мб |
| 17. break, continue, pass.srt |
5.25Кб |
| 17. Choosing The Right Model For Your Data 2 (Regression).mp4 |
86.92Мб |
| 17. Choosing The Right Model For Your Data 2 (Regression).srt |
11.98Кб |
| 17. Creating Our Own Validation Set.mp4 |
66.44Мб |
| 17. Creating Our Own Validation Set.srt |
11.32Кб |
| 17. Customizing Your Plots.mp4 |
92.21Мб |
| 17. Customizing Your Plots.srt |
13.95Кб |
| 17. RandomizedSearchCV.mp4 |
85.83Мб |
| 17. RandomizedSearchCV.srt |
12.65Кб |
| 17. Tuning Hyperparameters 3.mp4 |
63.02Мб |
| 17. Tuning Hyperparameters 3.srt |
9.92Кб |
| 17. Variables.mp4 |
93.56Мб |
| 17. Variables.srt |
16.04Кб |
| 18.1 Documentation for loading images in TensorFlow.html |
114б |
| 18.1 Exercise Repl.html |
99б |
| 18.2 Solution Repl.html |
99б |
| 18.2 TensorFlow guidelines for loading all kinds of data (turning your data into Tensors).html |
98б |
| 18. Customizing Your Plots 2.mp4 |
123.60Мб |
| 18. Customizing Your Plots 2.srt |
13.29Кб |
| 18. Expressions vs Statements.mp4 |
10.97Мб |
| 18. Expressions vs Statements.srt |
1.72Кб |
| 18. Improving Hyperparameters.mp4 |
79.29Мб |
| 18. Improving Hyperparameters.srt |
11.03Кб |
| 18. Optional Extra NumPy resources.html |
1.02Кб |
| 18. Our First GUI.mp4 |
49.63Мб |
| 18. Our First GUI.srt |
10.37Кб |
| 18. Preprocess Images.mp4 |
90.10Мб |
| 18. Preprocess Images.srt |
12.93Кб |
| 18. Quick Note Confusion Matrix Labels.html |
1.10Кб |
| 18. Quick Note Decision Trees.html |
221б |
| 19.1 Exercise Repl.html |
116б |
| 19.1 Introduction to Matplotlib Notebook (from the videos).html |
195б |
| 19. Augmented Assignment Operator.mp4 |
15.32Мб |
| 19. Augmented Assignment Operator.srt |
2.95Кб |
| 19. DEVELOPER FUNDAMENTALS IV.mp4 |
50.22Мб |
| 19. DEVELOPER FUNDAMENTALS IV.srt |
7.82Кб |
| 19. Evaluating Our Model.mp4 |
71.60Мб |
| 19. Evaluating Our Model.srt |
15.11Кб |
| 19. Preproccessing Our Data.mp4 |
139.30Мб |
| 19. Preproccessing Our Data.srt |
17.80Кб |
| 19. Preprocess Images 2.mp4 |
105.07Мб |
| 19. Preprocess Images 2.srt |
12.89Кб |
| 19. Quick Tip How ML Algorithms Work.mp4 |
11.06Мб |
| 19. Quick Tip How ML Algorithms Work.srt |
1.91Кб |
| 19. Saving And Sharing Your Plots.mp4 |
49.52Мб |
| 19. Saving And Sharing Your Plots.srt |
5.83Кб |
| 2.1 End-to-end Heart Disease Classification Notebook (with annotations).html |
201б |
| 2.1 How to Think About Communicating and Sharing Your Work (blog post).html |
142б |
| 2.1 Introduction to Matplotlib Jupyter Notebook (from the upcoming videos).html |
195б |
| 2.1 Introduction to NumPy Jupyter Notebook (with annotations).html |
184б |
| 2.1 Introduction to Scikit-Learn Jupyter Notebook (from the upcoming videos).html |
197б |
| 2.1 Kaggle.html |
92б |
| 2.1 python.org.html |
84б |
| 2.1 Structured Data Projects on GitHub.html |
155б |
| 2.2 End-to-end Bluebook Bulldozer Regression Notebook (same as in videos).html |
214б |
| 2.2 Matplotlib Documentation.html |
103б |
| 2.2 NumPy Documentation.html |
83б |
| 2.2 Scikit-Learn Documentation.html |
108б |
| 2.2 Structured Data Projects on GitHub.html |
155б |
| 2.3 End-to-end Bluebook Bulldozer Regression Notebook (with annotations).html |
208б |
| 2.3 End-to-end Heart Disease Classification Notebook (same as in videos).html |
207б |
| 2.3 Introduction to NumPy Jupyter Notebook (from the upcoming videos).html |
190б |
| 2.3 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html |
191б |
| 2.4 Kaggle Bluebook for Bulldozers Competition.html |
118б |
| 2. AIMachine LearningData Science.mp4 |
19.67Мб |
| 2. AIMachine LearningData Science.srt |
6.36Кб |
| 2. Communicating Your Work.mp4 |
20.20Мб |
| 2. Communicating Your Work.srt |
4.84Кб |
| 2. Conditional Logic.mp4 |
74.58Мб |
| 2. Conditional Logic.srt |
15.66Кб |
| 2. Deep Learning and Unstructured Data.mp4 |
102.04Мб |
| 2. Deep Learning and Unstructured Data.srt |
20.20Кб |
| 2. Downloading Workbooks and Assignments.html |
967б |
| 2. Introducing Our Framework.mp4 |
11.38Мб |
| 2. Introducing Our Framework.srt |
3.70Кб |
| 2. Introducing Our Tools.mp4 |
19.29Мб |
| 2. Introducing Our Tools.srt |
4.34Кб |
| 2. Join Our Online Classroom!.html |
2.53Кб |
| 2. Matplotlib Introduction.mp4 |
31.51Мб |
| 2. Matplotlib Introduction.srt |
8.03Кб |
| 2. NumPy Introduction.mp4 |
26.84Мб |
| 2. NumPy Introduction.srt |
7.50Кб |
| 2. Project Overview.mp4 |
34.44Мб |
| 2. Project Overview.mp4 |
32.94Мб |
| 2. Project Overview.srt |
10.02Кб |
| 2. Project Overview.srt |
6.66Кб |
| 2. Python + Machine Learning Monthly.html |
917б |
| 2. Python Interpreter.mp4 |
78.01Мб |
| 2. Python Interpreter.srt |
8.47Кб |
| 2. Quick Note Upcoming Video.html |
587б |
| 2. Scikit-learn Introduction.mp4 |
40.63Мб |
| 2. Scikit-learn Introduction.srt |
10.60Кб |
| 2. Thank You.mp4 |
11.11Мб |
| 2. Thank You.srt |
3.64Кб |
| 2. What Is Data.mp4 |
42.22Мб |
| 2. What Is Data.srt |
7.62Кб |
| 20.1 Solution Repl.html |
102б |
| 20. Assignment Matplotlib Practice.html |
2.05Кб |
| 20. Choosing The Right Model For Your Data 3 (Classification).mp4 |
118.84Мб |
| 20. Choosing The Right Model For Your Data 3 (Classification).srt |
17.13Кб |
| 20. Evaluating Our Model 2.mp4 |
41.53Мб |
| 20. Evaluating Our Model 2.srt |
7.41Кб |
| 20. Exercise Find Duplicates.mp4 |
20.26Мб |
| 20. Exercise Find Duplicates.srt |
4.39Кб |
| 20. Making Predictions.mp4 |
79.21Мб |
| 20. Making Predictions.srt |
11.37Кб |
| 20. Strings.mp4 |
30.98Мб |
| 20. Strings.srt |
6.29Кб |
| 20. Turning Data Into Batches.mp4 |
87.77Мб |
| 20. Turning Data Into Batches.srt |
11.61Кб |
| 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 |
214б |
| 21. Evaluating Our Model 3.mp4 |
64.84Мб |
| 21. Evaluating Our Model 3.srt |
11.55Кб |
| 21. Feature Importance.mp4 |
142.30Мб |
| 21. Feature Importance.srt |
17.26Кб |
| 21. Fitting A Model To The Data.mp4 |
56.56Мб |
| 21. Fitting A Model To The Data.srt |
9.33Кб |
| 21. Functions.mp4 |
48.60Мб |
| 21. Functions.srt |
9.20Кб |
| 21. String Concatenation.mp4 |
7.34Мб |
| 21. String Concatenation.srt |
1.42Кб |
| 21. Turning Data Into Batches 2.mp4 |
149.38Мб |
| 21. Turning Data Into Batches 2.srt |
20.15Кб |
| 22. Finding The Most Important Features.mp4 |
127.49Мб |
| 22. Finding The Most Important Features.srt |
22.33Кб |
| 22. Making Predictions With Our Model.mp4 |
66.50Мб |
| 22. Making Predictions With Our Model.srt |
12.08Кб |
| 22. Parameters and Arguments.mp4 |
23.14Мб |
| 22. Parameters and Arguments.srt |
4.88Кб |
| 22. Type Conversion.mp4 |
18.99Мб |
| 22. Type Conversion.srt |
3.09Кб |
| 22. Visualizing Our Data.mp4 |
121.99Мб |
| 22. Visualizing Our Data.srt |
15.66Кб |
| 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 |
38.15Мб |
| 23. Default Parameters and Keyword Arguments.srt |
5.98Кб |
| 23. Escape Sequences.mp4 |
23.16Мб |
| 23. Escape Sequences.srt |
5.01Кб |
| 23. predict() vs predict_proba().mp4 |
54.33Мб |
| 23. predict() vs predict_proba().srt |
11.56Кб |
| 23. Preparing Our Inputs and Outputs.mp4 |
50.07Мб |
| 23. Preparing Our Inputs and Outputs.srt |
7.78Кб |
| 23. Reviewing The Project.mp4 |
86.14Мб |
| 23. Reviewing The Project.srt |
13.81Кб |
| 24.1 Exercise Repl.html |
104б |
| 24. Formatted Strings.mp4 |
49.25Мб |
| 24. Formatted Strings.srt |
8.84Кб |
| 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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| 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 |
79б |
| 25. Building A Deep Learning Model.mp4 |
121.85Мб |
| 25. Building A Deep Learning Model.srt |
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| 25. Evaluating A Machine Learning Model (Score).mp4 |
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| 25. Exercise Tesla.html |
402б |
| 25. String Indexes.mp4 |
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| 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 |
30.69Мб |
| 26. Methods vs Functions.srt |
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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 |
105.92Мб |
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| 27. Evaluating A Classification Model 1 (Accuracy).mp4 |
31.41Мб |
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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. Building A Deep Learning Model 4.mp4 |
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| 28. Clean Code.mp4 |
19.66Мб |
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| 28. Evaluating A Classification Model 2 (ROC Curve).mp4 |
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| 29. args and kwargs.mp4 |
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| 29. Evaluating A Classification Model 3 (ROC Curve).mp4 |
50.61Мб |
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| 29. Exercise Type Conversion.mp4 |
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| 29. Summarizing Our Model.mp4 |
45.44Мб |
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| 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 |
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211.29Кб |
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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 |
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106б |
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93б |
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185б |
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23.46Мб |
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169б |
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100.76Мб |
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390б |
| 3. Setting Up With Google.html |
568б |
| 3. What If I Don't Have Enough Experience.mp4 |
160.95Мб |
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| 3. What Is A Data Engineer.mp4 |
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| 30.1 Python Comments Best Practices.html |
106б |
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108б |
| 30.1 TensorBoard Callback Documentation.html |
134б |
| 30. DEVELOPER FUNDAMENTALS II.mp4 |
29.25Мб |
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| 30. Evaluating Our Model.mp4 |
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| 30. Exercise Functions.mp4 |
21.85Мб |
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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 |
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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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| 31. Scope.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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92б |
| 33. Evaluating A Classification Model 6 (Classification Report).mp4 |
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| 33. Evaluating Performance With TensorBoard.mp4 |
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| 33. global Keyword.mp4 |
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93б |
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| 34. Evaluating A Regression Model 1 (R2 Score).mp4 |
70.39Мб |
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| 34. Make And Transform Predictions.mp4 |
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| 34. Matrix.mp4 |
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| 35.1 List Methods.html |
113б |
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127б |
| 35. Evaluating A Regression Model 2 (MAE).mp4 |
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| 35. List Methods.mp4 |
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129.87Мб |
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| 35. Why Do We Need Scope.mp4 |
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| 36.1 Python Keywords.html |
117б |
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94б |
| 36. Evaluating A Regression Model 3 (MSE).mp4 |
54.90Мб |
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| 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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| 38.1 Exercise Repl.html |
94б |
| 38. Common List Patterns.mp4 |
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| 38. Evaluating A Model With Cross Validation and Scoring Parameter.mp4 |
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| 39. Evaluating A Model With Scikit-learn Functions.mp4 |
94.82Мб |
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| 39. List Unpacking.mp4 |
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| 39. Saving And Loading A Trained Model.mp4 |
126.98Мб |
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| 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 |
654.77Кб |
| 4.1 pandas-anatomy-of-a-dataframe.png |
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| 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 |
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| 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 |
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| 4. Communicating With Co-Workers.mp4 |
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| 4. Learning Guideline.html |
325б |
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| 4. Optional Windows Project Environment Setup.mp4 |
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| 4. Refresher What Is Machine Learning.mp4 |
88.27Мб |
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| 4. Series, Data Frames and CSVs.mp4 |
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| 4. Setting Up Google Colab.mp4 |
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| 4. Step 1~4 Framework Setup.mp4 |
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| 4. The Final Challenge.html |
169б |
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42.82Мб |
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| 4. Types of Machine Learning Problems.mp4 |
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| 4. Your First Day.mp4 |
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| 40. Improving A Machine Learning Model.mp4 |
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| 40. None.mp4 |
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| 40. Training Model On Full Dataset.mp4 |
139.82Мб |
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| 41.1 Dog Vision Prediction Probabilities Array.html |
170б |
| 41. Dictionaries.mp4 |
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| 41. List Comprehensions.mp4 |
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| 41. Making Predictions On Test Images.mp4 |
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| 41. Tuning Hyperparameters.mp4 |
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| 42.1 Dog Vision Predictions with MobileNetV2 Ready for Kaggle Submission.html |
180б |
| 42. DEVELOPER FUNDAMENTALS III.mp4 |
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| 42. Set Comprehensions.mp4 |
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| 42. Submitting Model to Kaggle.mp4 |
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116.77Мб |
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| 43.1 End-to-end Dog Vision Notebook (with annotations).html |
185б |
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100б |
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191б |
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102б |
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20.37Мб |
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| 43. Making Predictions On Our Images.mp4 |
119.24Мб |
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| 43. Tuning Hyperparameters 3.mp4 |
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| 44.1 Dictionary Methods.html |
119б |
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| 44. Finishing Dog Vision Where to next.html |
3.86Кб |
| 44. Note Metric Comparison Improvement.html |
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| 45.1 Exercise Repl.html |
97б |
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| 45. Modules in Python.mp4 |
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| 46. Quick Note Upcoming Videos.html |
448б |
| 46. Saving And Loading A Model.mp4 |
52.60Мб |
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| 46. Tuples.mp4 |
25.65Мб |
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| 47.1 Tuple Methods.html |
114б |
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| 47. Saving And Loading A Model 2.mp4 |
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| 48.1 Reading extension Scikit-Learn's Pipeline class explained.html |
146б |
| 48. Packages in Python.mp4 |
72.42Мб |
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| 48. Putting It All Together.mp4 |
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| 48. Sets.mp4 |
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91б |
| 49.1 Introduction to Scikit-Learn Jupyter Notebook (from the videos).html |
197б |
| 49.2 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html |
191б |
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112б |
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| 5.1 Google Colab FAQ (things you should know about Google Colab).html |
110б |
| 5.1 Machine Learning Playground.html |
88б |
| 5.1 Miniconda download documentation.html |
107б |
| 5.2 Google Colab (our workspace for the upcoming project).html |
95б |
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66.77Мб |
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| 5. Data from URLs.html |
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19.43Мб |
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| 5. Latest Version Of Python.mp4 |
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| 5. Mac Environment Setup.mp4 |
144.39Мб |
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| 5. Quick Note Upcoming Videos.html |
1018б |
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565б |
| 5. Scatter Plot And Bar Plot.mp4 |
67.03Мб |
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| 5. Step 1~4 Framework Setup.mp4 |
105.50Мб |
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| 5. Ternary Operator.mp4 |
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| 5. Types of Data.mp4 |
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| 50. Next Steps.html |
959б |
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2.07Кб |
| 51. Bonus Resource Python Cheatsheet.html |
489б |
| 6.1 fast_template by fast.ai (a template you can use for your blog on GitHub Pages).html |
106б |
| 6.1 Kaggle Dog Breed Identification Competition Data.html |
115б |
| 6.1 Python 2 vs Python 3.html |
128б |
| 6.1 Scikit-Learn Reference Notebook.html |
194б |
| 6.2 Devblog by Hashnode (an easy and free way to create a blog you own).html |
89б |
| 6.2 Google Colab IO example (how to get data in and out of your Colab notebook).html |
113б |
| 6.2 The Story of Python.html |
104б |
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161б |
| 6. Communicating With Outside World.mp4 |
14.52Мб |
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75.56Мб |
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| 6. Exploring Our Data.mp4 |
137.81Мб |
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| 6. Getting Our Tools Ready.mp4 |
79.36Мб |
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69.75Мб |
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125.46Мб |
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51.92Мб |
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| 6. Python 2 vs Python 3.mp4 |
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| 6. Scikit-learn Cheatsheet.mp4 |
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| 6. Short Circuiting.mp4 |
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| 6. Types of Evaluation.mp4 |
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| 6. Types of Machine Learning.mp4 |
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| 6. Uploading Project Data.mp4 |
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| 6. What Is A Data Engineer 4.mp4 |
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| 7.1 car-sales.csv |
369б |
| 7.1 Example Scikit-Learn Workflow Notebook.html |
192б |
| 7.1 heart-disease.csv |
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| 7.1 Miniconda download documentation.html |
107б |
| 7.1 OLTP vs OLAP.html |
126б |
| 7.2 A Primer on ACID Transactions.html |
117б |
| 7. Are You Getting It Yet.html |
160б |
| 7. Exercise How Does Python Work.mp4 |
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| 7. Exercise How Does Python Work.srt |
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| 7. Exploring Our Data.mp4 |
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| 7. Exploring Our Data 2.mp4 |
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| 7. Features In Data.mp4 |
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| 7. JTS Start With Why.mp4 |
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| 7. Logical Operators.mp4 |
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| 7. Selecting and Viewing Data with Pandas.mp4 |
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| 7. Setting Up Our Data.mp4 |
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| 7. Storytelling.mp4 |
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| 7. Types Of Databases.mp4 |
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| 7. Typical scikit-learn Workflow.mp4 |
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| 7. Viewing Arrays and Matrices.mp4 |
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| 7. Windows Environment Setup.mp4 |
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| 8.1 Standard deviation and variance explained.html |
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| 8. Communicating and sharing your work Further reading.html |
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| 8. Exercise Logical Operators.mp4 |
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| 8. Feature Engineering.mp4 |
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| 8. Learning Python.mp4 |
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| 8. Manipulating Arrays.mp4 |
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| 8. Optional Debugging Warnings In Jupyter.mp4 |
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| 8. Quick Note Upcoming Video.html |
481б |
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| 8. Quick Tip Data Visualizations.mp4 |
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| 8. Selecting and Viewing Data with Pandas Part 2.mp4 |
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| 8. Setting Up Our Data 2.mp4 |
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| 8. What Is Machine Learning Round 2.mp4 |
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| 8. Windows Environment Setup 2.mp4 |
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| 9.1 Jake VanderPlas's Data Manipulation with Pandas.html |
146б |
| 9.1 scikit-learn-data.zip |
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| 9.1 Standard deviation and variance explained.html |
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| 9.2 car-sales-missing-data.csv |
287б |
| 9. CWD Git + Github.mp4 |
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| 9. Finding Patterns 2.mp4 |
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| 9. Getting Your Data Ready Splitting Your Data.mp4 |
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| 9. Importing TensorFlow 2.mp4 |
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| 9. Manipulating Data.mp4 |
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| 9. Modelling - Picking the Model.mp4 |
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| 9. Optional OLTP Databases.mp4 |
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| 9. Plotting From Pandas DataFrames.mp4 |
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| 9. Python Data Types.mp4 |
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| 9. Section Review.mp4 |
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| 9. Turning Data Into Numbers.mp4 |
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| car-sales-extended.csv |
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| car-sales-extended-missing-data.csv |
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| car-sales-missing-data.csv |
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| heart-disease.csv |
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| READ_ME.txt |
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| READ_ME.txt |
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| READ_ME.txt |
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