|
Обратите внимание, что наш сайт не размещает какие-либо файлы из списка. Вы не можете скачать
эти файлы или скачать torrent-файл.
|
| 1. Become An Alumni.html |
944б |
| 1. Bonus Special Thank You Gift.html |
1.59Кб |
| 1. Breaking The Flow.srt |
2.98Кб |
| 1. Course Outline.mp4 |
40.73Мб |
| 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 |
688б |
| 1. Milestone Projects!.html |
738б |
| 1. Section Overview.mp4 |
10.19Мб |
| 1. Section Overview.mp4 |
8.95Мб |
| 1. Section Overview.mp4 |
13.35Мб |
| 1. Section Overview.mp4 |
2.27Мб |
| 1. Section Overview.mp4 |
10.88Мб |
| 1. Section Overview.mp4 |
13.32Мб |
| 1. Section Overview.mp4 |
8.60Мб |
| 1. Section Overview.mp4 |
12.46Мб |
| 1. Section Overview.srt |
3.11Кб |
| 1. Section Overview.srt |
1.84Кб |
| 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 |
9.76Мб |
| 1. The 2 Paths.srt |
4.71Кб |
| 1. This section will be done by FEB 14th.html |
203б |
| 1. This section will be done by FEB 7th.html |
202б |
| 1. This section will be done by FEB 7th.html |
202б |
| 1. What Is A Programming Language.mp4 |
104.77Мб |
| 1. What Is A Programming Language.srt |
7.04Кб |
| 1. What Is Machine Learning.mp4 |
16.92Мб |
| 1. What Is Machine Learning.srt |
8.67Кб |
| 10.1 Floating point numbers.html |
104б |
| 10.1 heart-disease.csv.csv |
11.06Кб |
| 10.1 pandas-anatomy-of-a-dataframe.png.png |
333.24Кб |
| 10.1 Standard deviation and variance explained.html |
116б |
| 10.2 Dataquest Jupyter Notebook for Beginners Tutorial.html |
117б |
| 10.3 Jupyter Notebook documentation.html |
111б |
| 10. CWD Git + Github 2.mp4 |
118.35Мб |
| 10. CWD Git + Github 2.srt |
18.25Кб |
| 10. Filling Missing Categorical Values.mp4 |
66.91Мб |
| 10. Filling Missing Categorical Values.srt |
11.20Кб |
| 10. For Loops.srt |
7.53Кб |
| 10. Jupyter Notebook Walkthrough.mp4 |
67.35Мб |
| 10. Jupyter Notebook Walkthrough.srt |
15.14Кб |
| 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. Numbers.mp4 |
72.71Мб |
| 10. Numbers.srt |
11.13Кб |
| 10. Optional Learn SQL.html |
410б |
| 10. Preparing Our Data For Machine Learning.mp4 |
72.60Мб |
| 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. Standard Deviation and Variance.mp4 |
51.16Мб |
| 10. Standard Deviation and Variance.srt |
9.35Кб |
| 11.1 Introduction to Pandas Jupyter Notebook (with annotations).html |
185б |
| 11.2 Introduction to Pandas Jupyter Notebook (from the videos).html |
191б |
| 11. Choosing The Right Models.mp4 |
96.42Мб |
| 11. Choosing The Right Models.srt |
12.97Кб |
| 11. Contributing To Open Source.mp4 |
130.25Мб |
| 11. Contributing To Open Source.srt |
17.13Кб |
| 11. Fitting A Machine Learning Model.mp4 |
55.52Мб |
| 11. Fitting A Machine Learning Model.srt |
10.47Кб |
| 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 |
10.11Мб |
| 11. Hadoop, HDFS and MapReduce.srt |
4.70Кб |
| 11. Iterables.srt |
6.85Кб |
| 11. Jupyter Notebook Walkthrough 2.mp4 |
103.90Мб |
| 11. Jupyter Notebook Walkthrough 2.srt |
22.48Кб |
| 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 |
44.88Мб |
| 11. Modelling - Comparison.srt |
13.09Кб |
| 11. Plotting From Pandas DataFrames 2.mp4 |
98.80Мб |
| 11. Plotting From Pandas DataFrames 2.srt |
13.63Кб |
| 11. Reshape and Transpose.mp4 |
53.53Мб |
| 11. Reshape and Transpose.srt |
9.53Кб |
| 12.1 Matrix Multiplication Explained.html |
119б |
| 12.1 Solution Repl.html |
92б |
| 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 |
113.05Мб |
| 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 |
83.93Мб |
| 12. Dot Product vs Element Wise.srt |
15.34Кб |
| 12. Exercise Tricky Counter.srt |
3.58Кб |
| 12. Experimentation.mp4 |
21.33Мб |
| 12. Experimentation.srt |
4.98Кб |
| 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 |
104.84Мб |
| 12. Getting Your Data Ready Handling Missing Values With Pandas.srt |
16.94Кб |
| 12. Jupyter Notebook Walkthrough 3.mp4 |
37.94Мб |
| 12. Jupyter Notebook Walkthrough 3.srt |
11.49Кб |
| 12. Plotting from Pandas DataFrames 3.mp4 |
74.71Мб |
| 12. Plotting from Pandas DataFrames 3.srt |
11.46Кб |
| 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.csv |
11.06Кб |
| 13.2 Google Colab.html |
95б |
| 13. Coding Challenges.html |
948б |
| 13. Custom Evaluation Function.mp4 |
103.34Мб |
| 13. Custom Evaluation Function.srt |
16.11Кб |
| 13. Exercise Nut Butter Store Sales.mp4 |
91.32Мб |
| 13. Exercise Nut Butter Store Sales.srt |
16.96Кб |
| 13. Getting Your Data Ready Handling Missing Values With Scikit-learn.mp4 |
136.89Мб |
| 13. Getting Your Data Ready Handling Missing Values With Scikit-learn.srt |
23.13Кб |
| 13. How To Download The Course Assignments.mp4 |
66.78Мб |
| 13. How To Download The Course Assignments.srt |
11.06Кб |
| 13. Kafka and Stream Processing.mp4 |
19.25Мб |
| 13. Kafka and Stream Processing.srt |
5.05Кб |
| 13. Operator Precedence.mp4 |
14.43Мб |
| 13. Operator Precedence.srt |
3.50Кб |
| 13. Plotting from Pandas DataFrames 4.mp4 |
49.00Мб |
| 13. Plotting from Pandas DataFrames 4.srt |
9.41Кб |
| 13. range().srt |
5.86Кб |
| 13. Tools We Will Use.mp4 |
27.33Мб |
| 13. Tools We Will Use.srt |
5.99Кб |
| 13. TuningImproving Our Model.mp4 |
102.78Мб |
| 13. TuningImproving Our Model.srt |
17.64Кб |
| 14.1 Exercise Repl.html |
106б |
| 14.1 Scikit-Learn machine learning map (how to choose the right machine learning model).html |
133б |
| 14. Choosing The Right Model For Your Data.mp4 |
143.26Мб |
| 14. Choosing The Right Model For Your Data.srt |
21.38Кб |
| 14. Comparison Operators.mp4 |
26.37Мб |
| 14. Comparison Operators.srt |
5.26Кб |
| 14. enumerate().srt |
4.56Кб |
| 14. Exercise Contribute To Open Source.html |
1.43Кб |
| 14. Exercise Operator Precedence.html |
683б |
| 14. Optional Elements of AI.html |
975б |
| 14. Plotting from Pandas DataFrames 5.mp4 |
56.97Мб |
| 14. Plotting from Pandas DataFrames 5.srt |
11.63Кб |
| 14. Reducing Data.mp4 |
93.47Мб |
| 14. Reducing Data.srt |
14.62Кб |
| 14. Tuning Hyperparameters.mp4 |
108.00Мб |
| 14. Tuning Hyperparameters.srt |
15.67Кб |
| 15.1 Base Numbers.html |
111б |
| 15. Choosing The Right Model For Your Data 2 (Regression).mp4 |
86.92Мб |
| 15. Choosing The Right Model For Your Data 2 (Regression).srt |
11.98Кб |
| 15. Optional bin() and complex.mp4 |
21.90Мб |
| 15. Optional bin() and complex.srt |
4.80Кб |
| 15. Plotting from Pandas DataFrames 6.mp4 |
82.04Мб |
| 15. Plotting from Pandas DataFrames 6.srt |
11.08Кб |
| 15. RandomizedSearchCV.mp4 |
85.83Мб |
| 15. RandomizedSearchCV.srt |
12.65Кб |
| 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.srt |
7.36Кб |
| 16.1 Introduction to NumPy Jupyter Notebook (with annotations).html |
184б |
| 16.1 Python Keywords.html |
117б |
| 16.2 numpy-images.zip.zip |
7.27Мб |
| 16.3 Introduction to NumPy Jupyter Notebook (from the videos).html |
190б |
| 16. Improving Hyperparameters.mp4 |
79.29Мб |
| 16. Improving Hyperparameters.srt |
11.03Кб |
| 16. Plotting from Pandas DataFrames 7.mp4 |
119.75Мб |
| 16. Plotting from Pandas DataFrames 7.srt |
14.95Кб |
| 16. Quick Note Decision Trees.html |
221б |
| 16. Tuning Hyperparameters 3.mp4 |
63.01Мб |
| 16. Tuning Hyperparameters 3.srt |
9.92Кб |
| 16. Turn Images Into NumPy Arrays.mp4 |
85.91Мб |
| 16. Turn Images Into NumPy Arrays.srt |
10.42Кб |
| 16. Variables.mp4 |
93.56Мб |
| 16. Variables.srt |
16.04Кб |
| 16. While Loops 2.srt |
6.42Кб |
| 17. Assignment NumPy Practice.html |
2.17Кб |
| 17. break, continue, pass.srt |
5.25Кб |
| 17. Customizing Your Plots.mp4 |
92.21Мб |
| 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. Preproccessing Our Data.mp4 |
139.30Мб |
| 17. Preproccessing Our Data.srt |
17.80Кб |
| 17. Quick Tip How ML Algorithms Work.srt |
1.91Кб |
| 18.1 Exercise Repl.html |
116б |
| 18.1 Solution Repl.html |
99б |
| 18.2 Exercise Repl.html |
99б |
| 18. Augmented Assignment Operator.mp4 |
15.32Мб |
| 18. Augmented Assignment Operator.srt |
2.95Кб |
| 18. Choosing The Right Model For Your Data 3 (Classification).mp4 |
118.84Мб |
| 18. Choosing The Right Model For Your Data 3 (Classification).srt |
17.13Кб |
| 18. Customizing Your Plots 2.mp4 |
123.66Мб |
| 18. Customizing Your Plots 2.srt |
13.29Кб |
| 18. Evaluating Our Model 2.mp4 |
41.53Мб |
| 18. Evaluating Our Model 2.srt |
7.41Кб |
| 18. Making Predictions.mp4 |
79.21Мб |
| 18. Making Predictions.srt |
11.37Кб |
| 18. Optional Extra NumPy resources.html |
1.02Кб |
| 18. Our First GUI.srt |
10.37Кб |
| 19.1 End-to-end Bluebook Bulldozer Regression Notebook (with annotations).html |
208б |
| 19.1 Introduction to Matplotlib Notebook (from the videos).html |
195б |
| 19.2 End-to-end Bluebook Bulldozer Regression Notebook (same as in videos).html |
214б |
| 19. DEVELOPER FUNDAMENTALS IV.srt |
7.82Кб |
| 19. Evaluating Our Model 3.mp4 |
64.84Мб |
| 19. Evaluating Our Model 3.srt |
11.55Кб |
| 19. Feature Importance.mp4 |
142.30Мб |
| 19. Feature Importance.srt |
17.26Кб |
| 19. Fitting A Model To The Data.mp4 |
56.56Мб |
| 19. Fitting A Model To The Data.srt |
9.33Кб |
| 19. Saving And Sharing Your Plots.mp4 |
49.52Мб |
| 19. Saving And Sharing Your Plots.srt |
5.83Кб |
| 19. Strings.mp4 |
30.98Мб |
| 19. Strings.srt |
6.29Кб |
| 2.1 End-to-end Bluebook Bulldozer Regression Notebook (same as in videos).html |
214б |
| 2.1 End-to-end Heart Disease Classification Notebook (same as in videos).html |
207б |
| 2.1 Introduction to Matplotlib Jupyter Notebook (from the upcoming videos).html |
195б |
| 2.1 Introduction to Scikit-Learn Jupyter Notebook (from the upcoming videos).html |
197б |
| 2.1 Kaggle.html |
92б |
| 2.1 NumPy Documentation.html |
83б |
| 2.2 Introduction to NumPy Jupyter Notebook (from the upcoming videos).html |
190б |
| 2.2 Kaggle Bluebook for Bulldozers Competition.html |
118б |
| 2.2 Matplotlib Documentation.html |
103б |
| 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 (with annotations).html |
201б |
| 2.3 Introduction to NumPy Jupyter Notebook (with annotations).html |
184б |
| 2.3 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html |
191б |
| 2.4 Structured Data Projects on GitHub.html |
155б |
| 2. AIMachine LearningData Science.mp4 |
19.67Мб |
| 2. AIMachine LearningData Science.srt |
6.36Кб |
| 2. Conditional Logic.srt |
15.66Кб |
| 2. Downloading Workbooks and Assignments.html |
774б |
| 2. Introducing Our Framework.mp4 |
11.39Мб |
| 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 |
2.17Кб |
| 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 Developer Monthly.html |
476б |
| 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 Solution Repl.html |
102б |
| 20. Assignment Matplotlib Practice.html |
2.05Кб |
| 20. Exercise Find Duplicates.srt |
4.39Кб |
| 20. Finding The Most Important Features.mp4 |
127.49Мб |
| 20. Finding The Most Important Features.srt |
22.33Кб |
| 20. Making Predictions With Our Model.mp4 |
66.50Мб |
| 20. Making Predictions With Our Model.srt |
12.08Кб |
| 20. String Concatenation.mp4 |
7.34Мб |
| 20. String Concatenation.srt |
1.42Кб |
| 21.1 End-to-end Heart Disease Classification Notebook (with annotations).html |
201б |
| 21.2 End-to-end Heart Disease Classification Notebook (same as in videos).html |
207б |
| 21. Functions.srt |
9.20Кб |
| 21. predict() vs predict_proba().mp4 |
54.33Мб |
| 21. predict() vs predict_proba().srt |
11.56Кб |
| 21. Reviewing The Project.mp4 |
86.14Мб |
| 21. Reviewing The Project.srt |
13.81Кб |
| 21. Type Conversion.mp4 |
18.99Мб |
| 21. Type Conversion.srt |
3.09Кб |
| 22. Escape Sequences.mp4 |
23.15Мб |
| 22. Escape Sequences.srt |
5.01Кб |
| 22. Making Predictions With Our Model (Regression).mp4 |
44.91Мб |
| 22. Making Predictions With Our Model (Regression).srt |
9.13Кб |
| 22. Parameters and Arguments.srt |
4.88Кб |
| 23.1 Exercise Repl.html |
104б |
| 23. Default Parameters and Keyword Arguments.srt |
5.98Кб |
| 23. Evaluating A Machine Learning Model (Score).mp4 |
87.13Мб |
| 23. Evaluating A Machine Learning Model (Score).srt |
12.86Кб |
| 23. Formatted Strings.mp4 |
49.26Мб |
| 23. Formatted Strings.srt |
8.84Кб |
| 24.1 Exercise Repl.html |
101б |
| 24. Evaluating A Machine Learning Model 2 (Cross Validation).mp4 |
95.97Мб |
| 24. Evaluating A Machine Learning Model 2 (Cross Validation).srt |
17.25Кб |
| 24. return.srt |
14.97Кб |
| 24. String Indexes.mp4 |
49.15Мб |
| 24. String Indexes.srt |
9.21Кб |
| 25. Evaluating A Classification Model 1 (Accuracy).mp4 |
31.41Мб |
| 25. Evaluating A Classification Model 1 (Accuracy).srt |
5.87Кб |
| 25. Exercise Tesla.html |
402б |
| 25. Immutability.mp4 |
20.80Мб |
| 25. Immutability.srt |
3.50Кб |
| 26.1 Built in Functions.html |
109б |
| 26.2 String Methods.html |
115б |
| 26. Built-In Functions + Methods.mp4 |
69.39Мб |
| 26. Built-In Functions + Methods.srt |
10.27Кб |
| 26. Evaluating A Classification Model 2 (ROC Curve).mp4 |
66.03Мб |
| 26. Evaluating A Classification Model 2 (ROC Curve).srt |
12.28Кб |
| 26. Methods vs Functions.srt |
5.25Кб |
| 27. Booleans.mp4 |
16.55Мб |
| 27. Booleans.srt |
3.94Кб |
| 27. Docstrings.srt |
4.28Кб |
| 27. Evaluating A Classification Model 3 (ROC Curve).mp4 |
50.61Мб |
| 27. Evaluating A Classification Model 3 (ROC Curve).srt |
10.04Кб |
| 28. Clean Code.srt |
5.36Кб |
| 28. Evaluating A Classification Model 4 (Confusion Matrix).mp4 |
77.72Мб |
| 28. Evaluating A Classification Model 4 (Confusion Matrix).srt |
15.11Кб |
| 28. Exercise Type Conversion.mp4 |
50.34Мб |
| 28. Exercise Type Conversion.srt |
8.58Кб |
| 29.1 Python Comments Best Practices.html |
106б |
| 29. args and kwargs.srt |
8.09Кб |
| 29. DEVELOPER FUNDAMENTALS II.mp4 |
29.25Мб |
| 29. DEVELOPER FUNDAMENTALS II.srt |
5.30Кб |
| 29. Evaluating A Classification Model 5 (Confusion Matrix).mp4 |
63.59Мб |
| 29. Evaluating A Classification Model 5 (Confusion Matrix).srt |
11.20Кб |
| 3.1 A 6 Step Field Guide for Machine Learning Modelling (blog post).html |
147б |
| 3.1 Getting your computer ready for machine learning How.html |
167б |
| 3.1 Pandas Documentation.html |
106б |
| 3.1 Teachable Machine.html |
101б |
| 3.2 Conda documentation.html |
93б |
| 3.2 Introduction to Pandas Jupyter Notebook (with annotations).html |
185б |
| 3.3 Getting started with Conda (documentation).html |
139б |
| 3.3 Introduction to Pandas Jupyter Notebook (from the upcoming videos).html |
191б |
| 3.4 10-minutes to pandas (from the pandas documentation).html |
132б |
| 3.4 conda-cheatsheet.pdf.pdf |
201.29Кб |
| 3. 6 Step Machine Learning Framework.mp4 |
23.46Мб |
| 3. 6 Step Machine Learning Framework.srt |
6.63Кб |
| 3. Exercise Machine Learning Playground.mp4 |
42.60Мб |
| 3. Exercise Machine Learning Playground.srt |
8.09Кб |
| 3. Exercise Meet The Community.html |
2.51Кб |
| 3. How To Run Python Code.mp4 |
63.90Мб |
| 3. How To Run Python Code.srt |
6.46Кб |
| 3. Importing And Using Matplotlib.mp4 |
86.45Мб |
| 3. Importing And Using Matplotlib.srt |
16.05Кб |
| 3. Indentation In Python.srt |
5.27Кб |
| 3. Pandas Introduction.mp4 |
27.44Мб |
| 3. Project Environment Setup.mp4 |
100.76Мб |
| 3. Project Environment Setup.mp4 |
101.27Мб |
| 3. Project Environment Setup.mp4.jpg |
214.51Кб |
| 3. Project Environment Setup.srt |
14.39Кб |
| 3. Project Environment Setup.srt |
15.91Кб |
| 3. Project Environment Setup.txt |
239б |
| 3. Quick Note Correction In Next Video.html |
1.25Кб |
| 3. Quick Note Upcoming Video.html |
390б |
| 3. What If I Don_t Have Enough Experience.mp4 |
160.95Мб |
| 3. What If I Don_t Have Enough Experience.srt |
19.98Кб |
| 3. What Is A Data Engineer.mp4 |
15.16Мб |
| 3. What Is A Data Engineer.srt |
4.90Кб |
| 3. What is Conda.mp4 |
12.49Мб |
| 3. What is Conda.srt |
3.41Кб |
| 30.1 Solution Repl.html |
108б |
| 30. Evaluating A Classification Model 6 (Classification Report).mp4 |
87.24Мб |
| 30. Evaluating A Classification Model 6 (Classification Report).srt |
14.56Кб |
| 30. Exercise Functions.mp4 |
21.85Мб |
| 30. Exercise Functions.srt |
4.69Кб |
| 30. Exercise Password Checker.mp4 |
51.09Мб |
| 30. Exercise Password Checker.srt |
7.89Кб |
| 31. Evaluating A Regression Model 1 (R2 Score).mp4 |
70.39Мб |
| 31. Evaluating A Regression Model 1 (R2 Score).srt |
12.01Кб |
| 31. Lists.mp4 |
21.96Мб |
| 31. Lists.srt |
5.57Кб |
| 31. Scope.srt |
3.82Кб |
| 32.1 Exercise Repl.html |
92б |
| 32. Evaluating A Regression Model 2 (MAE).srt |
5.70Кб |
| 32. List Slicing.mp4 |
49.86Мб |
| 32. List Slicing.srt |
8.50Кб |
| 32. Scope Rules.srt |
8.48Кб |
| 33.1 Exercise Repl.html |
93б |
| 33. Evaluating A Regression Model 3 (MSE).mp4 |
54.90Мб |
| 33. Evaluating A Regression Model 3 (MSE).srt |
9.23Кб |
| 33. global Keyword.srt |
6.67Кб |
| 33. Matrix.mp4 |
19.15Мб |
| 33. Matrix.srt |
4.13Кб |
| 34.1 List Methods.html |
113б |
| 34.1 Solution Repl.html |
95б |
| 34. List Methods.mp4 |
61.75Мб |
| 34. List Methods.srt |
10.75Кб |
| 34. Machine Learning Model Evaluation.html |
7.12Кб |
| 34. nonlocal Keyword.srt |
4.07Кб |
| 35.1 Exercise Repl.html |
94б |
| 35.2 Python Keywords.html |
117б |
| 35. Evaluating A Model With Cross Validation and Scoring Parameter.mp4 |
91.49Мб |
| 35. Evaluating A Model With Cross Validation and Scoring Parameter.srt |
17.96Кб |
| 35. List Methods 2.mp4 |
27.41Мб |
| 35. List Methods 2.srt |
4.48Кб |
| 35. Why Do We Need Scope.srt |
4.77Кб |
| 36. Evaluating A Model With Scikit-learn Functions.mp4 |
94.82Мб |
| 36. Evaluating A Model With Scikit-learn Functions.srt |
16.32Кб |
| 36. List Methods 3.mp4 |
27.66Мб |
| 36. List Methods 3.srt |
5.01Кб |
| 36. Pure Functions.srt |
10.06Кб |
| 37.1 Exercise Repl.html |
94б |
| 37. Common List Patterns.mp4 |
40.47Мб |
| 37. Common List Patterns.srt |
5.83Кб |
| 37. Improving A Machine Learning Model.mp4 |
90.93Мб |
| 37. Improving A Machine Learning Model.srt |
14.86Кб |
| 37. map().srt |
6.29Кб |
| 38. filter().srt |
5.05Кб |
| 38. List Unpacking.mp4 |
13.87Мб |
| 38. List Unpacking.srt |
2.91Кб |
| 38. Tuning Hyperparameters.mp4 |
175.53Мб |
| 38. Tuning Hyperparameters.srt |
30.54Кб |
| 39. None.mp4 |
7.93Мб |
| 39. None.srt |
2.19Кб |
| 39. Tuning Hyperparameters 2.mp4 |
116.77Мб |
| 39. Tuning Hyperparameters 2.srt |
16.97Кб |
| 39. zip().srt |
3.26Кб |
| 4.1 matplotlib-anatomy-of-a-plot.png.png |
369.39Кб |
| 4.1 pandas-anatomy-of-a-dataframe.png.png |
333.24Кб |
| 4.1 Truthy vs Falsey Stackoverflow.html |
170б |
| 4.2 matplotlib-anatomy-of-a-plot-with-code.png.png |
654.77Кб |
| 4. Anatomy Of A Matplotlib Figure.mp4 |
82.15Мб |
| 4. Anatomy Of A Matplotlib Figure.srt |
14.16Кб |
| 4. Conda Environments.mp4 |
30.56Мб |
| 4. Conda Environments.srt |
6.15Кб |
| 4. How Did We Get Here.mp4 |
30.50Мб |
| 4. How Did We Get Here.srt |
7.07Кб |
| 4. Learning Guideline.html |
310б |
| 4. NumPy DataTypes and Attributes.mp4 |
78.99Мб |
| 4. NumPy DataTypes and Attributes.srt |
19.19Кб |
| 4. Our First Python Program.mp4 |
47.20Мб |
| 4. Our First Python Program.srt |
9.03Кб |
| 4. Refresher What Is Machine Learning.mp4 |
88.27Мб |
| 4. Refresher What Is Machine Learning.srt |
6.33Кб |
| 4. Series, Data Frames and CSVs.srt |
16.82Кб |
| 4. Step 1~4 Framework Setup.mp4 |
105.50Мб |
| 4. Step 1~4 Framework Setup.mp4 |
85.69Мб |
| 4. Step 1~4 Framework Setup.srt |
16.60Кб |
| 4. Step 1~4 Framework Setup.srt |
12.44Кб |
| 4. Truthy vs Falsey.srt |
5.99Кб |
| 4. Types of Machine Learning Problems.mp4 |
60.50Мб |
| 4. Types of Machine Learning Problems.srt |
13.98Кб |
| 4. What Is A Data Engineer 2.mp4 |
24.24Мб |
| 4. What Is A Data Engineer 2.srt |
6.33Кб |
| 4. Your First Day.mp4 |
8.24Мб |
| 4. Your First Day.srt |
5.27Кб |
| 40. Dictionaries.mp4 |
32.70Мб |
| 40. Dictionaries.srt |
7.09Кб |
| 40. reduce().srt |
8.39Кб |
| 40. Tuning Hyperparameters 3.mp4 |
121.76Мб |
| 40. Tuning Hyperparameters 3.srt |
18.78Кб |
| 41. DEVELOPER FUNDAMENTALS III.mp4 |
26.63Мб |
| 41. DEVELOPER FUNDAMENTALS III.srt |
3.59Кб |
| 41. List Comprehensions.srt |
9.38Кб |
| 41. Quick Tip Correlation Analysis.srt |
3.09Кб |
| 42. Dictionary Keys.mp4 |
20.37Мб |
| 42. Dictionary Keys.srt |
4.17Кб |
| 42. Saving And Loading A Model.mp4 |
52.60Мб |
| 42. Saving And Loading A Model.srt |
9.85Кб |
| 42. Set Comprehensions.srt |
6.58Кб |
| 43.1 Dictionary Methods.html |
119б |
| 43.1 Solution Repl.html |
102б |
| 43.2 Exercise Repl.html |
100б |
| 43. Dictionary Methods.mp4 |
27.17Мб |
| 43. Dictionary Methods.srt |
5.26Кб |
| 43. Exercise Comprehensions.mp4 |
21.96Мб |
| 43. Exercise Comprehensions.srt |
4.94Кб |
| 43. Saving And Loading A Model 2.mp4 |
56.77Мб |
| 43. Saving And Loading A Model 2.srt |
8.98Кб |
| 44.1 Exercise Repl.html |
97б |
| 44. Dictionary Methods 2.mp4 |
42.39Мб |
| 44. Dictionary Methods 2.srt |
7.14Кб |
| 44. Putting It All Together.mp4 |
158.35Мб |
| 44. Putting It All Together.srt |
26.43Кб |
| 44. Python Exam Testing Your Understanding.html |
1.12Кб |
| 45.1 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html |
191б |
| 45.2 Introduction to Scikit-Learn Jupyter Notebook (from the videos).html |
197б |
| 45. Modules in Python.srt |
12.67Кб |
| 45. Putting It All Together 2.mp4 |
116.85Мб |
| 45. Putting It All Together 2.srt |
16.11Кб |
| 45. Tuples.mp4 |
25.65Мб |
| 45. Tuples.srt |
5.69Кб |
| 46.1 Tuple Methods.html |
114б |
| 46. Quick Note Upcoming Videos.html |
448б |
| 46. Scikit-Learn Practice.html |
2.07Кб |
| 46. Tuples 2.mp4 |
16.99Мб |
| 46. Tuples 2.srt |
3.08Кб |
| 47. Optional PyCharm.srt |
10.51Кб |
| 47. Sets.mp4 |
36.98Мб |
| 47. Sets.srt |
8.43Кб |
| 48.1 Sets Methods.html |
112б |
| 48.2 Exercise Repl.html |
91б |
| 48. Packages in Python.srt |
12.45Кб |
| 48. Sets 2.mp4 |
64.26Мб |
| 48. Sets 2.srt |
9.24Кб |
| 49. Different Ways To Import.srt |
7.49Кб |
| 5.1 Machine Learning Playground.html |
88б |
| 5.1 Miniconda download documentation.html |
107б |
| 5.1 Python 2 vs Python 3.html |
161б |
| 5.2 The Story of Python.html |
104б |
| 5. Creating NumPy Arrays.mp4 |
66.77Мб |
| 5. Creating NumPy Arrays.srt |
12.44Кб |
| 5. Data from URLs.html |
1.09Кб |
| 5. Exercise YouTube Recommendation Engine.mp4 |
19.43Мб |
| 5. Exercise YouTube Recommendation Engine.srt |
5.65Кб |
| 5. Exploring Our Data.mp4 |
137.81Мб |
| 5. Exploring Our Data.srt |
19.97Кб |
| 5. Getting Our Tools Ready.mp4 |
79.36Мб |
| 5. Getting Our Tools Ready.srt |
12.78Кб |
| 5. Mac Environment Setup.mp4 |
144.39Мб |
| 5. Mac Environment Setup.srt |
23.93Кб |
| 5. Python 2 vs Python 3.mp4 |
82.14Мб |
| 5. Python 2 vs Python 3.srt |
8.17Кб |
| 5. Quick Note Upcoming Videos.html |
565б |
| 5. Quick Note Upcoming Videos.html |
1018б |
| 5. Scatter Plot And Bar Plot.mp4 |
67.03Мб |
| 5. Scatter Plot And Bar Plot.srt |
14.67Кб |
| 5. Ternary Operator.srt |
4.81Кб |
| 5. Types of Data.mp4 |
29.33Мб |
| 5. Types of Data.srt |
6.52Кб |
| 5. What Is A Data Engineer 3.mp4 |
24.29Мб |
| 5. What Is A Data Engineer 3.srt |
5.41Кб |
| 50. Next Steps.html |
959б |
| 6.1 heart-disease.csv.csv |
11.06Кб |
| 6.1 Scikit-Learn Reference Notebook.html |
194б |
| 6. Describing Data with Pandas.srt |
13.58Кб |
| 6. Exercise How Does Python Work.mp4 |
25.96Мб |
| 6. Exercise How Does Python Work.srt |
2.85Кб |
| 6. Exploring Our Data.mp4 |
66.88Мб |
| 6. Exploring Our Data.srt |
11.40Кб |
| 6. Exploring Our Data 2.mp4 |
52.04Мб |
| 6. Exploring Our Data 2.srt |
8.60Кб |
| 6. Histograms And Subplots.mp4 |
69.75Мб |
| 6. Histograms And Subplots.srt |
12.44Кб |
| 6. JTS Learn to Learn.mp4 |
11.14Мб |
| 6. JTS Learn to Learn.srt |
2.49Кб |
| 6. Mac Environment Setup 2.mp4 |
125.46Мб |
| 6. Mac Environment Setup 2.srt |
20.69Кб |
| 6. NumPy Random Seed.mp4 |
51.92Мб |
| 6. NumPy Random Seed.srt |
9.72Кб |
| 6. Scikit-learn Cheatsheet.mp4 |
75.13Мб |
| 6. Scikit-learn Cheatsheet.srt |
10.08Кб |
| 6. Short Circuiting.srt |
4.47Кб |
| 6. Types of Evaluation.mp4 |
17.76Мб |
| 6. Types of Evaluation.srt |
4.33Кб |
| 6. Types of Machine Learning.mp4 |
22.75Мб |
| 6. Types of Machine Learning.srt |
5.27Кб |
| 6. What Is A Data Engineer 4.mp4 |
14.93Мб |
| 6. What Is A Data Engineer 4.srt |
3.86Кб |
| 7.1 A Primer on ACID Transactions.html |
117б |
| 7.1 car-sales.csv.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 |
63.34Мб |
| 7. Finding Patterns.srt |
13.39Кб |
| 7. JTS Start With Why.mp4 |
15.43Мб |
| 7. JTS Start With Why.srt |
2.96Кб |
| 7. Learning Python.mp4 |
38.52Мб |
| 7. Learning Python.srt |
2.59Кб |
| 7. Logical Operators.srt |
8.10Кб |
| 7. Selecting and Viewing Data with Pandas.srt |
14.59Кб |
| 7. Subplots Option 2.mp4 |
38.09Мб |
| 7. Subplots Option 2.srt |
6.40Кб |
| 7. Types Of Databases.mp4 |
32.55Мб |
| 7. Types Of Databases.srt |
8.37Кб |
| 7. Typical scikit-learn Workflow.mp4 |
190.18Мб |
| 7. Typical scikit-learn Workflow.srt |
31.71Кб |
| 7. Viewing Arrays and Matrices.mp4 |
70.64Мб |
| 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. Exercise Logical Operators.srt |
8.40Кб |
| 8. Finding Patterns 2.mp4 |
99.92Мб |
| 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 |
176.13Мб |
| 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.srt |
17.92Кб |
| 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 |
227.60Мб |
| 8. Windows Environment Setup 2.srt |
31.61Кб |
| 9.1 car-sales-extended-missing-data.csv.csv |
30.20Кб |
| 9.1 Pandas Categorical Datatype Documentation.html |
143б |
| 9.1 Standard deviation and variance explained.html |
116б |
| 9.2 Jake VanderPlas_s Data Manipulation with Pandas.html |
146б |
| 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 |
137.86Мб |
| 9. Finding Patterns 3.srt |
18.88Кб |
| 9. Getting Your Data Ready Splitting Your Data.mp4 |
63.66Мб |
| 9. Getting Your Data Ready Splitting Your Data.srt |
12.08Кб |
| 9. How To Succeed.html |
280б |
| 9. is vs ==.srt |
8.12Кб |
| 9. Linux Environment Setup.html |
1.03Кб |
| 9. Manipulating Arrays 2.mp4 |
67.90Мб |
| 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 |
23.24Мб |
| 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 |
9.02Кб |
| 9. Section Review.mp4 |
2.52Мб |
| 9. Section Review.srt |
2.34Кб |
| GetFreeCourses.Me.url |
116б |
| GetFreeCourses.Me.url |
116б |
| How you can help GetFreeCourses.Me.txt |
182б |
| How you can help GetFreeCourses.Me.txt |
182б |
| Tutnetflix.com - Telegram @FTUplusrip.txt |
37б |