1. Lecture 1: Introduction to Machine Learning in Layman Terms | GO Classes.html
2. Annotated Notes Lecture 1 Introduction Machine Learning in Layman Terms.pdf
3. Lecture 2: An Optimisation View to Fit the Best Line | GO Classes.mp4
4. Annotated Notes Lecture 2 An Optimisation View to Fit the Best Line.pdf
5. Lecture 3: A Linear Algebra View to Fit the Best Line | GO Classes.mp4
6. Annotated Notes Lecture 3 Linear Algebra View to Fit the Best Line.pdf
7. Lecture 4: The First Class on Machine Learning | Simple Linear Regression.mp4
8. Annotated Notes Lecture 4 Machine Learning Introduction and Simple Linear Regression.pdf
9. Lecture 5: Many Questions on Simple Linear Regression | Multiple Linear Regression.mp4
10. Annotated Notes Lecture 5 Many Questions on Simple Linear Regression.pdf
11. Lecture 6: Multiple Linear Regression.mp4
12. Annotated Notes Lecture 6 Multiple Linear Regression.pdf
13. Lecture 7: More Questions on Multiple Linear Regression and Gradient Descent.mp4
14. Annotated Notes Lecture 7 More Question on Multiple Linear Regression.pdf
15. Lecture From Calculus Module 2 : Gradient Descent Algorithm.mp4
16. Annotated Notes Gradient Descent.pdf
17. Lecture 8: Linear Regression and Gradient Descent.mp4
18. Annotated Notes Lecture 8 Gradient Descent and Linear Regression.pdf
19. Lecture 9: 30 Questions on Batch GD, Mini-Batch GD, and SGD.mp4
20. Annotated Notes Lecture 9: 30 Questions on Batch GD, Mini-Batch GD, and SGD.pdf
21. Lecture 10: Polynomial Regression.mp4
22. Annotated Notes Lecture 10 Polynomial Regression.pdf
23. Lecture 11: Overfitting and Underfitting Definitions.mp4
24. Annotated Notes Lecture 11 OverFitting and UnderFitting.pdf
25. Lecture 12: Regularization and Ridge Regression.mp4
26. Annotated NotesLecture 12 Regularisation.pdf
27. Lecture 13: Ridge and LASSO Regression.mp4
28. Annotated NotesLecture Lecture 13 Ridge Regression.pdf
29. Lecture 14: Gradient Descent and More on Ridge and LASSO Regression.mp4
30. Annotated NotesLecture 14 Gradient Descent LAsso Ridge.pdf
31. Lecture 15: Logistic Regression.mp4
32. Annotated Notes Lecture 15 Logistic Regression.pdf
33. Lecture 16: More on Logistic Regression and Cross Entropy Loss.mp4
34. Annotated Notes Lecture 16 Logistic Regression and CE loss.pdf
35. Lecture 17: Maximum Likelihood Estimate and Cross Entropy Loss Differentiation.mp4
36. Annotated Notes Lecture 17 MLE and CE loss derivative.pdf
37. Lecture 18 Softmax Regression or MultiClass Logistic Regression.mp4
38. Annotated Notes Lecture 18 Softmax Regression.pdf
39. Lecture 19 More about Logistic Regression | Categorical Cross-Entropy Loss | More Interpretations.mp4
40. Annotated Notes Lecture 19 Logistic Regression.pdf
41. Lecture 20: K-Nearest Neighbors (K-NN).mp4
42. Annotated Notes Lecture 20 KNN.pdf
43. Lecture 21: Naive Bayes Classifier and Fifteen Questions on Naive Bayes.mp4
44. Annotated Notes Lecture 21 Naive Bayes.pdf
45. Lecture 22: Support Vector Machine (Hard Margin) and Twenty Questions on SVM.mp4
46. Annotated Notes Lecture 22 SVM.pdf
47. Lectures on Optimisation.html
48. Lecture 23: Soft Margin Support Vector Machine.mp4
49. Annotated Notes Lecture 23 Soft Margin SVM.pdf
50. Lecture 24: Questions on Soft SVM and Solution of Soft SVM.mp4
51. Annotated Notes Lecture 24 More on Soft Margin SVM.pdf
52. Lecture 24 (Part B): Solution of Soft SVM and Hinge Loss.mp4
53. Annotated Notes Lecture 24 Part 2 Hing Loss Soft Margin SVM.pdf
54. Lecture 25: Cross-validation Methods (Leave-one-out (LOO) and k-folds ).mp4
55. Annotated Notes Lecture 25 Cross Validation.pdf
56. Lecture 26: 25 More Questions on Cross-validation Method.mp4
57. Annotated Notes Lecture 26 More Questions on Cross Validation.pdf
58. Lecture 27: Precision Recall and F1 Score.mp4
59. Annotated Notes Lecture 27 Classification Evaluation Metrics.pdf
60. Lecture 28: ROC Curve AUC Classification Metric.mp4
61. Annotated Notes Lecture 28 ROC Curve.pdf
62. Lecture 29: Bias Variance Tradeoff.mp4
63. Annotated Notes Lecture 29 Bias Variance Tradeoff.pdf
64. Lecture 30: Many Questions on Bias Variance Tradeoff And Error Decomposition.mp4
65. Annotated Notes Lecture 30 Bias Variance Tradeoff.pdf
66. Lecture 31: Perceptron.mp4
67. Annotated Notes Lecture 31 Perceptron.pdf
68. Lecture 32: Principal Component Analysis (PCA) | With Geometric Interpretation | Two ways to Derive.mp4
69. Annotated Notes Lecture 32 PCA.pdf
70. Lecture 33: PCA Reconstruction Error and Other Insights.mp4
71. Annotated Notes Lecture 33 PCA-2.pdf
72. Lecture 34a: Questions on Reconstruction Error.mp4
73. Lecture 34b: PCA Interpretations and Relation to SVD.mp4
74. Annotated Notes Lecture 34 PCA and SVD.pdf
75. Lecture 35 Decision tree | Entropy.mp4
76. Annotated Notes Lecture 35 Decision Tree.pdf
77. Lecture 36 Decision Trees Gini Impurity and Regression with Decision Trees.mp4
78. Annotated Notes Lecture 36 Gini Impurity and Regression Tree.pdf
79. Lecture 37 K means Clustering Method | Loss Function | Many Questions.mp4
80. Annotated Notes Lecture 37 K means Clustering.pdf
81. Lecture 38 Hierarchical clustering | Top Down | Bottom up.mp4
82. Annotated Notes Lecture 38 Hierarchical Clustering.pdf
83. Lecture 39: Feed Forward Neural Networks.mp4
84. Annotated Notes Lecture 39 Neural Networks.pdf
85. Lecture 40 : BackPropagation - 1.mp4
86. Annotated Notes Lecture 40 Backpropagation - 1.pdf
87. Lecture 41 : BackPropagation - 2.mp4
88. Lecture 42 : Backpropogation - 3.mp4
89. Annotated Notes : Lecture 40, 41, 42 - BackPropagation.pdf
90. Lecture 43 : Linear Discriminant Analysis - 1.mp4
91. Annotated Notes Lecture 43 : Linear Discriminant Analysis - 1.pdf
92. Lecture 44 : LDA - Dimensionality reduction POV.mp4
93. Annotated Notes Lecture 44 : LDA - Dimensionality reduction POV.pdf