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1. Basics
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0. Important Info
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1. Logisitcs and Basics Info
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2. Basic Info-2
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3. Jargons and Basic ML Problem Setup
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4. Information, Entropy and Cost Functions
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2. Supervised Learning - Regression and Classification
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1. Different Classification of ML Algo and Our first ML Algo: KNN
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2. KNN: Algorithm and Code
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3. KNN: Decision Boundary, Value of K, Pros, Cons and Summary
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4. Decision Trees - Introduction (Corrupted)
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5. Revision until now
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6. Revision-2
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7. Revision-3
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KNN Sheet
(12 pages)
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8. KNN Sheet Discussion
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9. CART/Decision Tree Intro
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10. Decision Tree Classification
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11. Practice Questions on DT
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12. Regression Tree
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13. Decision Tree Summary
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14. Naive Bayes Classifier : Introduction
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16. Bayes Classifier vs Naive Bayes Classifier
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17. Coding DT and NB
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18. Summarizing NB
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19. Follow-Up lecture
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20. Bias Vriance Trade-Off
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21. Understanding Intuition and Use of Bias-Variance
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22. Cross Validation
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23. Practice Problems on Bias-Variance and Cross-Validation
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24. Basics for ROC and AUC
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25. Practice Questions and R-Squared Metric for regression
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26. LDA, QDA and FLDA
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27. Practice Problems
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28. SVM Introduction
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29. Understanding Basic Intuition and Maths behind SVM
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30. Hard and Soft SVM
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31. Soft SVM and Regularization
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32. Practice Questions
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33. Practice Questions-2
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34. Linear Regression
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35. Regularization
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36. Summarizing LR
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37. Linear Regression: Probabilistic Point of View
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38. Practice Questions
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39. Logistic Regression
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40. Problem Solving
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41. Problem Solving-2
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42. Problem Solving -3
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43. Problem Solving-4
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3. Neural Networks
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1. Basics for NN: Perceptron
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2. MLP and Problems
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3. Problem Discussion
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4. Unsupervised Learning
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1. Clustering Basics and KMeans
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2. Problem Solving
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3. Hierarchical Clustering and Problems
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4. PCA Basics
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5. PCA by Hand and Problem Solving
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PCA Questions
(6 pages)
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6. Problems Discussion
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5. Revision and Problem Solving
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1. Session-1
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2. Problem Solving Session-2
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Launch your Graphy