[UdemyCourseDownloader] Complete Data Science & Machine Learning Bootcamp – Python 3

1. 04. Introduction to Optimisation and the Gradient Descent Algorithm/8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 291.34 MB
2. 04. Introduction to Optimisation and the Gradient Descent Algorithm/6. [Python] - Loops and the Gradient Descent Algorithm.mp4 287.45 MB
3. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.mp4 251.84 MB
4. 05. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 244.16 MB
5. 04. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.mp4 236.59 MB
6. 03. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.mp4 232.08 MB
7. 04. Introduction to Optimisation and the Gradient Descent Algorithm/9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 219.02 MB
8. 05. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 214.40 MB
9. 11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.mp4 213.68 MB
10. 08. Test and Evaluate a Naive Bayes Classifier Part 3/6. Visualising the Decision Boundary.mp4 205.31 MB
11. 08. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.mp4 195.10 MB
12. 04. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.mp4 193.48 MB
13. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4 191.53 MB
14. 03. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.mp4 171.46 MB
15. 05. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.mp4 168.65 MB
16. 03. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.mp4 156.77 MB
17. 11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.mp4 155.37 MB
18. 03. Python Programming for Data Science and Machine Learning/9. [Python & Pandas] - Dataframes and Series.mp4 153.21 MB
19. 05. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 153.02 MB
20. 05. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.mp4 152.68 MB
21. 11. Use Tensorflow to Classify Handwritten Digits/6. Creating Tensors and Setting up the Neural Network Architecture.mp4 150.86 MB
22. 05. Predict House Prices with Multivariable Linear Regression/23. Model Simiplication & Baysian Information Criterion.mp4 150.15 MB
23. 02. Predict Movie Box Office Revenue with Linear Regression/3. Explore & Visualise the Data with Python.mp4 148.16 MB
24. 09. Introduction to Neural Networks and How to Use Pre-Trained Models/2. Layers, Feature Generation and Learning.mp4 146.70 MB
25. 05. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.mp4 143.83 MB
26. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/6. Joint & Conditional Probability.mp4 141.82 MB
27. 04. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.mp4 140.82 MB
28. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/35. Sparse Matrix (Part 2) Data Munging with Nested Loops.mp4 137.23 MB
29. 05. Predict House Prices with Multivariable Linear Regression/4. Clean and Explore the Data (Part 2) Find Missing Values.mp4 135.03 MB
30. 09. Introduction to Neural Networks and How to Use Pre-Trained Models/6. Making Predictions using InceptionResNet.mp4 134.58 MB
31. 05. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 134.39 MB
32. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/7. Interacting with the Operating System and the Python Try-Catch Block.mp4 133.41 MB
33. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.mp4 133.16 MB
34. 04. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.mp4 132.82 MB
35. 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/2. Create a Full Matrix.mp4 132.24 MB
36. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/28. Styling the Word Cloud with a Mask.mp4 131.37 MB
37. 05. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 131.31 MB
38. 04. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.mp4 131.08 MB
39. 05. Predict House Prices with Multivariable Linear Regression/12. Techniques to Style Scatter Plots.mp4 128.53 MB
40. 11. Use Tensorflow to Classify Handwritten Digits/9. Tensorboard Summaries and the Filewriter.mp4 128.29 MB
41. 03. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.mp4 128.20 MB
42. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/30. Styling Word Clouds with Custom Fonts.mp4 127.30 MB
43. 05. Predict House Prices with Multivariable Linear Regression/20. Improving the Model by Transforming the Data.mp4 126.87 MB
44. 04. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4 124.88 MB
45. 05. Predict House Prices with Multivariable Linear Regression/25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4 124.42 MB
46. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.mp4 121.93 MB
47. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/19. Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4 117.76 MB
48. 11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.mp4 115.74 MB
49. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/2. Gathering Email Data and Working with Archives & Text Editors.mp4 112.05 MB
50. 05. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.mp4 111.44 MB