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2. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4 129.8 MB
5. Data Preprocessing/7. EDD in Python.mp4 101.8 MB
5. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4 91.9 MB
6. Classification Models/21. K-Nearest Neighbors classifier.mp4 87.6 MB
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4. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp4 77.2 MB
4. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4 76.6 MB
6. Classification Models/11. Making Confusion Matrix in Python.mp4 67.9 MB
6. Classification Models/4. Training a Simple Logistic Model in Python.mp4 64.2 MB
5. Data Preprocessing/10. Outlier treatment in Python.mp4 61.3 MB
4. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp4 59.1 MB
4. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4 56.7 MB
6. Classification Models/23. K-Nearest Neighbors in Python Part 2.mp4 54.5 MB
4. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4 53.8 MB
4. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp4 51.2 MB
6. Classification Models/15. Linear Discriminant Analysis.mp4 51.0 MB
6. Classification Models/25. Understanding the results of classification models.mp4 48.2 MB
6. Classification Models/22. K-Nearest Neighbors in Python Part 1.mp4 48.1 MB
6. Classification Models/18. Test-Train Split.mp4 47.9 MB