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| [CourseClub.ME].url |
122б |
| [FCS Forum].url |
133б |
| [FreeCourseSite.com].url |
127б |
| 1. Become An Alumni.html |
1.72Кб |
| 1. Bonus Special Thank You Gift!.html |
1.59Кб |
| 1. Breaking The Flow.mp4 |
20.34Мб |
| 1. Breaking The Flow.srt |
2.98Кб |
| 1. Course Outline.mp4 |
77.27Мб |
| 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 |
2.08Кб |
| 1. Milestone Projects!.html |
738б |
| 1. Section Overview.mp4 |
10.20Мб |
| 1. Section Overview.mp4 |
8.96Мб |
| 1. Section Overview.mp4 |
12.20Мб |
| 1. Section Overview.mp4 |
10.93Мб |
| 1. Section Overview.mp4 |
13.35Мб |
| 1. Section Overview.mp4 |
6.03Мб |
| 1. Section Overview.mp4 |
10.87Мб |
| 1. Section Overview.mp4 |
13.33Мб |
| 1. Section Overview.mp4 |
8.60Мб |
| 1. Section Overview.mp4 |
12.47Мб |
| 1. Section Overview.srt |
3.11Кб |
| 1. Section Overview.srt |
1.84Кб |
| 1. Section Overview.srt |
2.77Кб |
| 1. Section Overview.srt |
3.29Кб |
| 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. 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 |
104.78Мб |
| 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 Floating point numbers.html |
104б |
| 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 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 Categorical Values.mp4 |
66.92Мб |
| 10. Filling Missing Categorical Values.srt |
11.20Кб |
| 10. For Loops.mp4 |
34.32Мб |
| 10. For Loops.srt |
7.53Кб |
| 10. Manipulating Data 2.mp4 |
86.53Мб |
| 10. Manipulating Data 2.srt |
13.85Кб |
| 10. Modelling - Tuning.mp4 |
15.99Мб |
| 10. Modelling - Tuning.srt |
4.86Кб |
| 10. Numbers.mp4 |
72.71Мб |
| 10. Numbers.srt |
11.13Кб |
| 10. Optional Learn SQL.html |
410б |
| 10. Optional TensorFlow 2.0 Default Issue.mp4 |
28.10Мб |
| 10. Optional TensorFlow 2.0 Default Issue.srt |
28.12Мб |
| 10. Preparing Our Data For Machine Learning.mp4 |
72.61Мб |
| 10. Preparing Our Data For Machine Learning.srt |
12.02Кб |
| 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 |
51.17Мб |
| 10. Standard Deviation and Variance.srt |
9.35Кб |
| 11.1 Dataquest Jupyter Notebook for Beginners Tutorial.html |
117б |
| 11.1 Google Colab example GPU usage.html |
114б |
| 11.1 Introduction to Pandas Jupyter Notebook (with annotations).html |
185б |
| 11.2 Introduction to Pandas Jupyter Notebook (from the videos).html |
191б |
| 11.2 Jupyter Notebook documentation.html |
111б |
| 11.3 heart-disease.csv |
11.06Кб |
| 11.4 6-step-ml-framework.png |
324.24Кб |
| 11. Choosing The Right Models.mp4 |
96.43Мб |
| 11. Choosing The Right Models.srt |
12.97Кб |
| 11. Contributing To Open Source.mp4 |
130.26Мб |
| 11. Contributing To Open Source.srt |
17.13Кб |
| 11. Fitting A Machine Learning Model.mp4 |
55.53Мб |
| 11. Fitting A Machine Learning Model.srt |
10.47Кб |
| 11. Getting Your Data Ready Convert Data To Numbers.mp4 |
135.03Мб |
| 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 |
43.20Мб |
| 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. Math Functions.mp4 |
41.82Мб |
| 11. Math Functions.srt |
5.43Кб |
| 11. Modelling - Comparison.mp4 |
44.89Мб |
| 11. Modelling - Comparison.srt |
13.09Кб |
| 11. Plotting From Pandas DataFrames 2.mp4 |
98.81Мб |
| 11. Plotting From Pandas DataFrames 2.srt |
13.63Кб |
| 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. Contributing To Open Source 2.mp4 |
113.04Мб |
| 12. Contributing To Open Source 2.srt |
10.18Кб |
| 12. DEVELOPER FUNDAMENTALS I.mp4 |
59.71Мб |
| 12. DEVELOPER FUNDAMENTALS I.srt |
5.22Кб |
| 12. Dot Product vs Element Wise.mp4 |
83.94Мб |
| 12. Dot Product vs Element Wise.srt |
15.34Кб |
| 12. Exercise Tricky Counter.mp4 |
16.39Мб |
| 12. Exercise Tricky Counter.srt |
3.58Кб |
| 12. Experimenting With Machine Learning Models.mp4 |
55.35Мб |
| 12. Experimenting With Machine Learning Models.srt |
9.63Кб |
| 12. Getting Your Data Ready Handling Missing Values With Pandas.mp4 |
104.85Мб |
| 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. Optional GPU and Google Colab.mp4 |
45.88Мб |
| 12. Optional GPU and Google Colab.srt |
5.99Кб |
| 12. Overfitting and Underfitting Definitions.html |
1.74Кб |
| 12. Plotting from Pandas DataFrames 3.mp4 |
74.72Мб |
| 12. Plotting from Pandas DataFrames 3.srt |
74.73Мб |
| 12. Splitting Data.mp4 |
82.68Мб |
| 12. Splitting Data.srt |
13.51Кб |
| 13.1 Course notebooks - Github.html |
108б |
| 13.1 Exercise Repl.html |
106б |
| 13.1 heart-disease.csv |
11.06Кб |
| 13.2 Google Colab.html |
95б |
| 13. Challenge What's wrong with splitting data after filling it.html |
1.50Кб |
| 13. Coding Challenges.html |
948б |
| 13. Exercise Nut Butter Store Sales.mp4 |
91.33Мб |
| 13. Exercise Nut Butter Store Sales.srt |
16.96Кб |
| 13. Experimentation.mp4 |
21.33Мб |
| 13. Experimentation.srt |
4.98Кб |
| 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 |
19.25Мб |
| 13. Kafka and Stream Processing.srt |
5.05Кб |
| 13. Note Correction in the upcoming video (splitting data).html |
2.16Кб |
| 13. Operator Precedence.mp4 |
14.42Мб |
| 13. Operator Precedence.srt |
3.50Кб |
| 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 |
28.32Мб |
| 13. range().srt |
5.86Кб |
| 13. TuningImproving Our Model.mp4 |
102.78Мб |
| 13. TuningImproving Our Model.srt |
17.64Кб |
| 14.1 Documentation on how many images Google recommends for image problems.html |
129б |
| 14.1 Exercise Repl.html |
106б |
| 14. Comparison Operators.mp4 |
26.38Мб |
| 14. Comparison Operators.srt |
5.26Кб |
| 14. Custom Evaluation Function.mp4 |
103.34Мб |
| 14. Custom Evaluation Function.srt |
16.11Кб |
| 14. enumerate().mp4 |
24.81Мб |
| 14. enumerate().srt |
4.56Кб |
| 14. Exercise Contribute To Open Source.html |
1.43Кб |
| 14. Exercise Operator Precedence.html |
683б |
| 14. Getting Your Data Ready Handling Missing Values With Scikit-learn.mp4 |
136.90Мб |
| 14. Getting Your Data Ready Handling Missing Values With Scikit-learn.srt |
23.13Кб |
| 14. Loading Our Data Labels.mp4 |
114.83Мб |
| 14. Loading Our Data Labels.srt |
16.08Кб |
| 14. Plotting from Pandas DataFrames 5.mp4 |
56.96Мб |
| 14. Plotting from Pandas DataFrames 5.srt |
11.63Кб |
| 14. Tools We Will Use.mp4 |
27.34Мб |
| 14. Tools We Will Use.srt |
5.99Кб |
| 14. Tuning Hyperparameters.mp4 |
108.01Мб |
| 14. Tuning Hyperparameters.srt |
15.67Кб |
| 15.1 Base Numbers.html |
111б |
| 15.1 Scikit-Learn machine learning map (how to choose the right machine learning model).html |
133б |
| 15. Choosing The Right Model For Your Data.mp4 |
143.27Мб |
| 15. Choosing The Right Model For Your Data.srt |
21.38Кб |
| 15. Optional bin() and complex.mp4 |
21.90Мб |
| 15. Optional bin() and complex.srt |
4.80Кб |
| 15. Optional Elements of AI.html |
975б |
| 15. Plotting from Pandas DataFrames 6.mp4 |
82.05Мб |
| 15. Plotting from Pandas DataFrames 6.srt |
11.08Кб |
| 15. Preparing The Images.mp4 |
133.89Мб |
| 15. Preparing The Images.srt |
15.12Кб |
| 15. Reducing Data.mp4 |
93.48Мб |
| 15. Reducing Data.srt |
14.62Кб |
| 15. Sorting Arrays.mp4 |
32.83Мб |
| 15. Sorting Arrays.srt |
8.80Кб |
| 15. Tuning Hyperparameters 2.mp4 |
104.12Мб |
| 15. Tuning Hyperparameters 2.srt |
15.10Кб |
| 15. While Loops.mp4 |
28.32Мб |
| 15. While Loops.srt |
7.36Кб |
| 16.1 Introduction to NumPy Jupyter Notebook (from the videos).html |
190б |
| 16.1 Python Keywords.html |
117б |
| 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 2 (Regression).mp4 |
86.93Мб |
| 16. Choosing The Right Model For Your Data 2 (Regression).srt |
11.98Кб |
| 16. Plotting from Pandas DataFrames 7.mp4 |
119.76Мб |
| 16. Plotting from Pandas DataFrames 7.srt |
14.95Кб |
| 16. RandomizedSearchCV.mp4 |
85.83Мб |
| 16. RandomizedSearchCV.srt |
12.65Кб |
| 16. Tuning Hyperparameters 3.mp4 |
63.02Мб |
| 16. Tuning Hyperparameters 3.srt |
63.03Мб |
| 16. Turn Images Into NumPy Arrays.mp4 |
85.92Мб |
| 16. Turn Images Into NumPy Arrays.srt |
10.42Кб |
| 16. Turning Data Labels Into Numbers.mp4 |
107.47Мб |
| 16. Turning Data Labels Into Numbers.srt |
13.76Кб |
| 16. Variables.mp4 |
93.56Мб |
| 16. Variables.srt |
16.04Кб |
| 16. While Loops 2.mp4 |
25.94Мб |
| 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. Assignment NumPy Practice.html |
2.17Кб |
| 17. break, continue, pass.mp4 |
22.22Мб |
| 17. break, continue, pass.srt |
5.25Кб |
| 17. Creating Our Own Validation Set.mp4 |
66.44Мб |
| 17. Creating Our Own Validation Set.srt |
11.32Кб |
| 17. Customizing Your Plots.mp4 |
92.22Мб |
| 17. Customizing Your Plots.srt |
13.95Кб |
| 17. Evaluating Our Model.mp4 |
71.60Мб |
| 17. Evaluating Our Model.srt |
15.11Кб |
| 17. Expressions vs Statements.mp4 |
10.97Мб |
| 17. Expressions vs Statements.srt |
1.72Кб |
| 17. Improving Hyperparameters.mp4 |
79.29Мб |
| 17. Improving Hyperparameters.srt |
11.03Кб |
| 17. Quick Note Decision Trees.html |
221б |
| 18.1 Documentation for loading images in TensorFlow.html |
114б |
| 18.1 Exercise Repl.html |
116б |
| 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. Augmented Assignment Operator.mp4 |
15.33Мб |
| 18. Augmented Assignment Operator.srt |
2.95Кб |
| 18. Customizing Your Plots 2.mp4 |
123.60Мб |
| 18. Customizing Your Plots 2.srt |
13.29Кб |
| 18. Evaluating Our Model 2.mp4 |
41.54Мб |
| 18. Evaluating Our Model 2.srt |
7.41Кб |
| 18. Optional Extra NumPy resources.html |
1.02Кб |
| 18. Our First GUI.mp4 |
49.64Мб |
| 18. Our First GUI.srt |
10.37Кб |
| 18. Preproccessing Our Data.mp4 |
139.30Мб |
| 18. Preproccessing Our Data.srt |
17.80Кб |
| 18. Preprocess Images.mp4 |
90.10Мб |
| 18. Preprocess Images.srt |
12.93Кб |
| 18. Quick Tip How ML Algorithms Work.mp4 |
11.07Мб |
| 18. Quick Tip How ML Algorithms Work.srt |
1.91Кб |
| 19.1 Introduction to Matplotlib Notebook (from the videos).html |
195б |
| 19. Choosing The Right Model For Your Data 3 (Classification).mp4 |
118.85Мб |
| 19. Choosing The Right Model For Your Data 3 (Classification).srt |
17.13Кб |
| 19. DEVELOPER FUNDAMENTALS IV.mp4 |
50.22Мб |
| 19. DEVELOPER FUNDAMENTALS IV.srt |
7.82Кб |
| 19. Evaluating Our Model 3.mp4 |
64.84Мб |
| 19. Evaluating Our Model 3.srt |
11.55Кб |
| 19. Making Predictions.mp4 |
79.22Мб |
| 19. Making Predictions.srt |
11.37Кб |
| 19. Preprocess Images 2.mp4 |
105.08Мб |
| 19. Preprocess Images 2.srt |
12.89Кб |
| 19. Saving And Sharing Your Plots.mp4 |
49.52Мб |
| 19. Saving And Sharing Your Plots.srt |
5.83Кб |
| 19. Strings.mp4 |
30.99Мб |
| 19. Strings.srt |
6.29Кб |
| 2.1 End-to-end Bluebook Bulldozer Regression Notebook (with annotations).html |
208б |
| 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 Kaggle.html |
92б |
| 2.1 NumPy Documentation.html |
83б |
| 2.1 Scikit-Learn Documentation.html |
108б |
| 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 End-to-end Heart Disease Classification Notebook (with annotations).html |
201б |
| 2.2 Introduction to NumPy Jupyter Notebook (from the upcoming videos).html |
190б |
| 2.2 Introduction to Scikit-Learn Jupyter Notebook (from the upcoming videos).html |
197б |
| 2.2 Matplotlib Documentation.html |
103б |
| 2.3 End-to-end Heart Disease Classification Notebook (same as in videos).html |
207б |
| 2.3 Introduction to NumPy Jupyter Notebook (with annotations).html |
184б |
| 2.3 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html |
191б |
| 2.3 Kaggle Bluebook for Bulldozers Competition.html |
118б |
| 2.4 Structured Data Projects on GitHub.html |
155б |
| 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 |
2.31Кб |
| 2. Matplotlib Introduction.mp4 |
31.51Мб |
| 2. Matplotlib Introduction.srt |
8.03Кб |
| 2. NumPy Introduction.mp4 |
26.85Мб |
| 2. NumPy Introduction.srt |
7.50Кб |
| 2. Project Overview.mp4 |
34.45Мб |
| 2. Project Overview.mp4 |
32.95Мб |
| 2. Project Overview.srt |
10.02Кб |
| 2. Project Overview.srt |
6.66Кб |
| 2. Python + Machine Learning Monthly.html |
734б |
| 2. Python Interpreter.mp4 |
93.47Мб |
| 2. Python Interpreter.srt |
8.30Кб |
| 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 |
11.12Мб |
| 2. Thank You.srt |
3.64Кб |
| 2. What Is Data.mp4 |
42.22Мб |
| 2. What Is Data.srt |
7.62Кб |
| 20.1 End-to-end Bluebook Bulldozer Regression Notebook (with annotations).html |
208б |
| 20.1 Solution Repl.html |
102б |
| 20.2 End-to-end Bluebook Bulldozer Regression Notebook (same as in videos).html |
214б |
| 20. Assignment Matplotlib Practice.html |
2.05Кб |
| 20. Exercise Find Duplicates.mp4 |
20.26Мб |
| 20. Exercise Find Duplicates.srt |
4.39Кб |
| 20. Feature Importance.mp4 |
142.31Мб |
| 20. Feature Importance.srt |
17.26Кб |
| 20. Finding The Most Important Features.mp4 |
127.49Мб |
| 20. Finding The Most Important Features.srt |
22.33Кб |
| 20. Fitting A Model To The Data.mp4 |
56.57Мб |
| 20. Fitting A Model To The Data.srt |
9.33Кб |
| 20. String Concatenation.mp4 |
7.34Мб |
| 20. String Concatenation.srt |
1.42Кб |
| 20. Turning Data Into Batches.mp4 |
87.78Мб |
| 20. Turning Data Into Batches.srt |
11.61Кб |
| 21.1 End-to-end Heart Disease Classification Notebook (same as in videos).html |
207б |
| 21.1 Yann LeCun's (OG of deep learning) Tweet on Batch Sizes.html |
118б |
| 21.2 End-to-end Heart Disease Classification Notebook (with annotations).html |
201б |
| 21. Functions.mp4 |
48.60Мб |
| 21. Functions.srt |
9.20Кб |
| 21. Making Predictions With Our Model.mp4 |
66.50Мб |
| 21. Making Predictions With Our Model.srt |
66.52Мб |
| 21. Reviewing The Project.mp4 |
86.14Мб |
| 21. Reviewing The Project.srt |
86.16Мб |
| 21. Turning Data Into Batches 2.mp4 |
149.39Мб |
| 21. Turning Data Into Batches 2.srt |
20.15Кб |
| 21. Type Conversion.mp4 |
19.00Мб |
| 21. Type Conversion.srt |
3.09Кб |
| 22. Escape Sequences.mp4 |
23.16Мб |
| 22. Escape Sequences.srt |
23.13Мб |
| 22. Parameters and Arguments.mp4 |
23.15Мб |
| 22. Parameters and Arguments.srt |
4.88Кб |
| 22. predict() vs predict_proba().mp4 |
54.33Мб |
| 22. predict() vs predict_proba().srt |
11.56Кб |
| 22. Visualizing Our Data.mp4 |
121.99Мб |
| 22. Visualizing Our Data.srt |
15.66Кб |
| 23.1 Exercise Repl.html |
104б |
| 23.1 TensorFlow Hub (resource for pre-trained deep learning models and more).html |
79б |
| 23. Default Parameters and Keyword Arguments.mp4 |
38.15Мб |
| 23. Default Parameters and Keyword Arguments.srt |
5.98Кб |
| 23. Formatted Strings.mp4 |
49.25Мб |
| 23. Formatted Strings.srt |
8.84Кб |
| 23. Making Predictions With Our Model (Regression).mp4 |
44.92Мб |
| 23. Making Predictions With Our Model (Regression).srt |
9.13Кб |
| 23. Preparing Our Inputs and Outputs.mp4 |
50.08Мб |
| 23. Preparing Our Inputs and Outputs.srt |
7.78Кб |
| 24.1 Exercise Repl.html |
101б |
| 24. Evaluating A Machine Learning Model (Score).mp4 |
87.14Мб |
| 24. Evaluating A Machine Learning Model (Score).srt |
12.86Кб |
| 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 TensorFlow Hub (resource for pre-trained deep learning models and more).html |
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| 25.2 MobileNetV2 (the model we're using) on TensorFlow Hub.html |
132б |
| 25.3 Andrei Karpathy's talk on AI at Tesla.html |
95б |
| 25.4 Papers with Code (a great resource for some of the best machine learning papers with code examples).html |
88б |
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| 27.1 Step by step breakdown of a convolutional neural network (what MobileNetV2 is made of).html |
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| 27.2 The Softmax Function (activation function we use in our model).html |
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| 29.1 Python Comments Best Practices.html |
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| 4.3 Kaggle Dog Breed Identification Competition (the basis of our upcoming project).html |
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| 4.4 End-to-end Dog Vision Notebook (the project we'll be working through).html |
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| 40. Dictionaries.mp4 |
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| 41. List Comprehensions.mp4 |
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| 42.1 Dog Vision Predictions with MobileNetV2 Ready for Kaggle Submission.html |
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| 43.1 End-to-end Dog Vision Notebook (with annotations).html |
185б |
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| 45. Modules in Python.mp4 |
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| 46.1 Introduction to Scikit-Learn Jupyter Notebook (from the videos).html |
197б |
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114б |
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110б |
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107б |
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161б |
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95б |
| 5.2 The Story of Python.html |
104б |
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| 5. Exploring Our Data.mp4 |
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| 5. Getting Our Tools Ready.mp4 |
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| 50. Next Steps.html |
959б |
| 6.1 Devblog by Hashnode (an easy and free way to create a blog you own).html |
89б |
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| 6.1 Kaggle Dog Breed Identification Competition Data.html |
115б |
| 6.1 Scikit-Learn Reference Notebook.html |
194б |
| 6.2 fast_template by fast.ai (a template you can use for your blog on GitHub Pages).html |
106б |
| 6.2 Google Colab IO example (how to get data in and out of your Colab notebook).html |
113б |
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| 7.1 A Primer on ACID Transactions.html |
117б |
| 7.1 car-sales.csv |
369б |
| 7.1 Example Scikit-Learn Workflow Notebook.html |
192б |
| 7.1 Miniconda download documentation.html |
107б |
| 7.2 OLTP vs OLAP.html |
126б |
| 7. Are You Getting It Yet.html |
160б |
| 7. Feature Engineering.mp4 |
159.14Мб |
| 7. Feature Engineering.srt |
22.13Кб |
| 7. Features In Data.mp4 |
36.78Мб |
| 7. Features In Data.srt |
6.75Кб |
| 7. Finding Patterns.mp4 |
63.35Мб |
| 7. Finding Patterns.srt |
13.39Кб |
| 7. JTS Start With Why.mp4 |
15.44Мб |
| 7. JTS Start With Why.srt |
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| 7. Learning Python.mp4 |
38.53Мб |
| 7. Learning Python.srt |
2.59Кб |
| 7. Logical Operators.mp4 |
28.34Мб |
| 7. Logical Operators.srt |
8.10Кб |
| 7. Selecting and Viewing Data with Pandas.mp4 |
72.36Мб |
| 7. Selecting and Viewing Data with Pandas.srt |
14.59Кб |
| 7. Setting Up Our Data.mp4 |
42.26Мб |
| 7. Setting Up Our Data.srt |
6.38Кб |
| 7. Storytelling.mp4 |
12.03Мб |
| 7. Storytelling.srt |
4.10Кб |
| 7. Subplots Option 2.mp4 |
38.09Мб |
| 7. Subplots Option 2.srt |
6.40Кб |
| 7. Types Of Databases.mp4 |
32.56Мб |
| 7. Types Of Databases.srt |
8.37Кб |
| 7. Typical scikit-learn Workflow.mp4 |
190.19Мб |
| 7. Typical scikit-learn Workflow.srt |
31.71Кб |
| 7. Viewing Arrays and Matrices.mp4 |
70.65Мб |
| 7. Viewing Arrays and Matrices.srt |
12.89Кб |
| 7. Windows Environment Setup.mp4 |
47.92Мб |
| 7. Windows Environment Setup.srt |
7.62Кб |
| 8.1 Standard deviation and variance explained.html |
116б |
| 8. Communicating and sharing your work Further reading.html |
3.14Кб |
| 8. Exercise Logical Operators.mp4 |
46.62Мб |
| 8. Exercise Logical Operators.srt |
8.40Кб |
| 8. Finding Patterns 2.mp4 |
99.93Мб |
| 8. Finding Patterns 2.srt |
22.32Кб |
| 8. Manipulating Arrays.mp4 |
80.65Мб |
| 8. Manipulating Arrays.srt |
16.17Кб |
| 8. Modelling - Splitting Data.mp4 |
27.51Мб |
| 8. Modelling - Splitting Data.srt |
7.71Кб |
| 8. Optional Debugging Warnings In Jupyter.mp4 |
176.14Мб |
| 8. Optional Debugging Warnings In Jupyter.srt |
25.51Кб |
| 8. Python Data Types.mp4 |
28.85Мб |
| 8. Python Data Types.srt |
5.22Кб |
| 8. Quick Note Upcoming Video.html |
481б |
| 8. Quick Note Upcoming Videos.html |
352б |
| 8. Quick Tip Data Visualizations.mp4 |
12.25Мб |
| 8. Quick Tip Data Visualizations.srt |
2.34Кб |
| 8. Selecting and Viewing Data with Pandas Part 2.mp4 |
106.51Мб |
| 8. Selecting and Viewing Data with Pandas Part 2.srt |
17.92Кб |
| 8. Setting Up Our Data 2.mp4 |
20.87Мб |
| 8. Setting Up Our Data 2.srt |
2.18Кб |
| 8. Turning Data Into Numbers.mp4 |
146.17Мб |
| 8. Turning Data Into Numbers.srt |
22.32Кб |
| 8. What Is Machine Learning Round 2.mp4 |
25.52Мб |
| 8. What Is Machine Learning Round 2.srt |
6.07Кб |
| 8. Windows Environment Setup 2.mp4 |
227.61Мб |
| 8. Windows Environment Setup 2.srt |
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| 9.1 Jake VanderPlas's Data Manipulation with Pandas.html |
146б |
| 9.1 Pandas Categorical Datatype Documentation.html |
143б |
| 9.1 scikit-learn-data.zip |
20.83Кб |
| 9.1 Standard deviation and variance explained.html |
116б |
| 9.2 car-sales-missing-data.csv |
287б |
| 9. CWD Git + Github.mp4 |
176.11Мб |
| 9. CWD Git + Github.srt |
21.17Кб |
| 9. Filling Missing Numerical Values.mp4 |
106.34Мб |
| 9. Filling Missing Numerical Values.srt |
16.94Кб |
| 9. Finding Patterns 3.mp4 |
137.87Мб |
| 9. Finding Patterns 3.srt |
18.88Кб |
| 9. Getting Your Data Ready Splitting Your Data.mp4 |
63.67Мб |
| 9. Getting Your Data Ready Splitting Your Data.srt |
12.08Кб |
| 9. How To Succeed.html |
280б |
| 9. Importing TensorFlow 2.mp4 |
116.77Мб |
| 9. Importing TensorFlow 2.srt |
16.79Кб |
| 9. is vs ==.mp4 |
33.57Мб |
| 9. is vs ==.srt |
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| 9. Linux Environment Setup.html |
1.03Кб |
| 9. Manipulating Arrays 2.mp4 |
67.91Мб |
| 9. Manipulating Arrays 2.srt |
11.49Кб |
| 9. Manipulating Data.mp4 |
104.99Мб |
| 9. Manipulating Data.srt |
18.07Кб |
| 9. Modelling - Picking the Model.mp4 |
23.25Мб |
| 9. Modelling - Picking the Model.srt |
6.21Кб |
| 9. Optional OLTP Databases.mp4 |
79.68Мб |
| 9. Optional OLTP Databases.srt |
12.11Кб |
| 9. Plotting From Pandas DataFrames.mp4 |
60.35Мб |
| 9. Plotting From Pandas DataFrames.srt |
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| 9. Section Review.mp4 |
5.56Мб |
| 9. Section Review.srt |
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