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[FreeCourseSite.com] Udemy - Complete 2020 Data Science & Machine Learning Bootcamp

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  3. Creat Time：2024-05-30
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  5. Last Active：2024-11-02
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    [FreeCourseSite.com] Udemy - Complete 2020 Data Science & Machine Learning Bootcamp

  1. 4. Introduction to Optimisation and the Gradient Descent Algorithm/8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 298.32 MB
  2. 4. Introduction to Optimisation and the Gradient Descent Algorithm/6. [Python] - Loops and the Gradient Descent Algorithm.mp4 294.35 MB
  3. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.mp4 257.88 MB
  4. 5. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 250.02 MB
  5. 4. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.mp4 242.27 MB
  6. 12. Serving a Tensorflow Model through a Website/12. Introduction to OpenCV.mp4 240.98 MB
  7. 3. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.mp4 237.64 MB
  8. 12. Serving a Tensorflow Model through a Website/14. Calculating the Centre of Mass and Shifting the Image.mp4 228.62 MB
  9. 4. Introduction to Optimisation and the Gradient Descent Algorithm/9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 224.27 MB
  10. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/10. Use the Model to Make Predictions.mp4 223.49 MB
  11. 5. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 219.55 MB
  12. 11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.mp4 218.80 MB
  13. 8. Test and Evaluate a Naive Bayes Classifier Part 3/6. Visualising the Decision Boundary.mp4 210.24 MB
  14. 8. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.mp4 199.77 MB
  15. 4. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.mp4 198.13 MB
  16. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4 196.12 MB
  17. 12. Serving a Tensorflow Model through a Website/7. Loading a Tensorflow.js Model and Starting your own Server.mp4 192.55 MB
  18. 12. Serving a Tensorflow Model through a Website/9. Styling an HTML Canvas.mp4 191.86 MB
  19. 12. Serving a Tensorflow Model through a Website/16. Adding the Game Logic.mp4 176.98 MB
  20. 12. Serving a Tensorflow Model through a Website/10. Drawing on an HTML Canvas.mp4 176.09 MB
  21. 3. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.mp4 175.57 MB
  22. 3. Python Programming for Data Science and Machine Learning/19. Working with Python Objects to Analyse Data.mp4 174.06 MB
  23. 5. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.mp4 172.70 MB
  24. 12. Serving a Tensorflow Model through a Website/13. Resizing and Addign Padding to Images.mp4 161.28 MB
  25. 3. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.mp4 160.53 MB
  26. 11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.mp4 159.10 MB
  27. 3. Python Programming for Data Science and Machine Learning/9. [Python & Pandas] - Dataframes and Series.mp4 156.88 MB
  28. 5. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 156.68 MB
  29. 5. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.mp4 156.34 MB
  30. 11. Use Tensorflow to Classify Handwritten Digits/6. Creating Tensors and Setting up the Neural Network Architecture.mp4 154.47 MB
  31. 12. Serving a Tensorflow Model through a Website/6. HTML and CSS Styling.mp4 153.83 MB
  32. 5. Predict House Prices with Multivariable Linear Regression/23. Model Simiplication & Baysian Information Criterion.mp4 153.75 MB
  33. 2. Predict Movie Box Office Revenue with Linear Regression/3. Explore & Visualise the Data with Python.mp4 151.70 MB
  34. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/2. Layers, Feature Generation and Learning.mp4 150.22 MB
  35. 5. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.mp4 147.27 MB
  36. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/6. Joint & Conditional Probability.mp4 145.22 MB
  37. 4. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.mp4 144.19 MB
  38. 5. Predict House Prices with Multivariable Linear Regression/7. Working with Index Data, Pandas Series, and Dummy Variables.mp4 144.14 MB
  39. 6. 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 140.53 MB
  40. 5. Predict House Prices with Multivariable Linear Regression/4. Clean and Explore the Data (Part 2) Find Missing Values.mp4 138.26 MB
  41. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/6. Making Predictions using InceptionResNet.mp4 137.81 MB
  42. 5. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 137.61 MB
  43. 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 136.61 MB
  44. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.mp4 136.36 MB
  45. 4. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.mp4 135.99 MB
  46. 12. Serving a Tensorflow Model through a Website/4. Converting a Model to Tensorflow.js.mp4 135.67 MB
  47. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/2. Create a Full Matrix.mp4 135.41 MB
  48. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/28. Styling the Word Cloud with a Mask.mp4 134.52 MB
  49. 5. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 134.46 MB
  50. 4. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.mp4 134.21 MB
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