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Introduction to Machine Learning
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Lecture 1: Introduction to Machine Learning in Layman Terms | GO Classes
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Annotated Notes Lecture 1 Introduction Machine Learning in Layman Terms
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Lecture 2: An Optimisation View to Fit the Best Line | GO Classes
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Annotated Notes Lecture 2 An Optimisation View to Fit the Best Line
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Lecture 3: A Linear Algebra View to Fit the Best Line | GO Classes
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Annotated Notes Lecture 3 Linear Algebra View to Fit the Best Line
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Lecture 4: The First Class on Machine Learning | Simple Linear Regression
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Annotated Notes Lecture 4 Machine Learning Introduction and Simple Linear Regression
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Lecture 5: Many Questions on Simple Linear Regression | Multiple Linear Regression
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Annotated Notes Lecture 5 Many Questions on Simple Linear Regression
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Lecture 6: Multiple Linear Regression
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Annotated Notes Lecture 6 Multiple Linear Regression
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Lecture 7: More Questions on Multiple Linear Regression and Gradient Descent
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Annotated Notes Lecture 7 More Question on Multiple Linear Regression
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Lecture From Calculus Module 2 : Gradient Descent Algorithm
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Annotated Notes Gradient Descent
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Lecture 8: Linear Regression and Gradient Descent
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Annotated Notes Lecture 8 Gradient Descent and Linear Regression
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Lecture 9: 30 Questions on Batch GD, Mini-Batch GD, and SGD
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Annotated Notes Lecture 9: 30 Questions on Batch GD, Mini-Batch GD, and SGD
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Lecture 10: Polynomial Regression
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Annotated Notes Lecture 10 Polynomial Regression
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Lecture 11: Overfitting and Underfitting Definitions
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Annotated Notes Lecture 11 OverFitting and UnderFitting
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Lecture 12: Regularization and Ridge Regression
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Annotated NotesLecture 12 Regularisation
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Lecture 13: Ridge and LASSO Regression
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Annotated NotesLecture Lecture 13 Ridge Regression
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Lecture 14: Gradient Descent and More on Ridge and LASSO Regression
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Annotated NotesLecture 14 Gradient Descent LAsso Ridge
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Lecture 15: Logistic Regression
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Annotated Notes Lecture 15 Logistic Regression
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Lecture 16: More on Logistic Regression andCross Entropy Loss
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Annotated Notes Lecture 16 Logistic Regression and CE loss
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Lecture 17: Maximum Likelihood Estimate and Cross Entropy Loss Differentiation
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Annotated Notes Lecture 17 MLE and CE loss derivative
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Lecture 18 Softmax Regression or MultiClass Logistic Regression
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Annotated Notes Lecture 18 Softmax Regression
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Lecture 19 More about Logistic Regression | Categorical Cross-Entropy Loss | More Interpretations
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Annotated Notes Lecture 19 Logistic Regression
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Lecture 20: K-Nearest Neighbors (K-NN)
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Annotated Notes Lecture 20 KNN
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Lecture 21: Naive Bayes Classifier and Fifteen Questions on Naive Bayes
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Annotated Notes Lecture 21 Naive Bayes
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Lecture 22: Support Vector Machine (Hard Margin) and Twenty Questions on SVM
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Annotated Notes Lecture 22 SVM
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Lectures on Optimisation
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Lecture 23: Soft Margin Support Vector Machine
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Annotated Notes Lecture 23 Soft Margin SVM
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Lecture 24: Questions on Soft SVM and Solution of Soft SVM
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Annotated Notes Lecture 24 More on Soft Margin SVM
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Lecture 24 (Part B): Solution of Soft SVM and Hinge Loss
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Annotated Notes Lecture 24 Part 2 Hing Loss Soft Margin SVM
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Lecture 25: Cross-validation Methods (Leave-one-out (LOO) and k-folds )
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Annotated Notes Lecture 25 Cross Validation
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Lecture 26: 25 More Questions on Cross-validation Method
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Annotated Notes Lecture 26 More Questions on Cross Validation
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Lecture 27: Precision Recall and F1 Score
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Annotated Notes Lecture 27 Classification Evaluation Metrics
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Lecture 28: ROC Curve AUC Classification Metric
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Annotated Notes Lecture 28 ROC Curve
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Lecture 29: Bias Variance Tradeoff
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Annotated Notes Lecture 29 Bias Variance Tradeoff
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Lecture 30: Many Questions on Bias Variance Tradeoff And Error Decomposition
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Annotated Notes Lecture 30 Bias Variance Tradeoff
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Lecture 31: Perceptron
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Annotated Notes Lecture 31 Perceptron
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Lecture 32: Principal Component Analysis (PCA) | With Geometric Interpretation | Two ways to Derive
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Annotated Notes Lecture 32 PCA
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Lecture 33: PCA Reconstruction Error and Other Insights
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Annotated Notes Lecture 33 PCA-2
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Lecture 34a: Questions on Reconstruction Error
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Lecture 34b: PCA Interpretations and Relation to SVD
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Annotated Notes Lecture 34 PCA and SVD
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Lecture 35 Decision tree | Entropy
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Annotated Notes Lecture 35 Decision Tree
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Lecture 36 Decision Trees Gini Impurity and Regression with Decision Trees
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Annotated Notes Lecture 36 Gini Impurity and Regression Tree
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Lecture 37 K means Clustering Method | Loss Function | Many Questions
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Annotated Notes Lecture 37 K means Clustering
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Lecture 38 Hierarchical clustering | Top Down | Bottom up
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Annotated Notes Lecture 38 Hierarchical Clustering
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Lecture 39: Feed Forward Neural Networks
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Annotated Notes Lecture 39 Neural Networks
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Practical Notebooks
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Linear Regression Practical Implementation
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NoteBook Files Linear Regression
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Overfitting and Underfitting
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NoteBook Files Overfitting and UnderFitting
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Logistic Regression Practical
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NoteBook Files Logistic Regression
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Polynomial Regression , Ridge & Lasso Regression
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Practical Session : Polynomial Regression , Ridge and Lasso Regression
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MultiClass Logistic Regression Practical
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ML Practical Session : K Nearest Neighbour
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Annotated Notes Lecture 14 : Linked List Insertion Deletion
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Lecture 17 : Questions on Recursion
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Support Vector Machine(SVM) Practical Session
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ML Practical Sessions : Cross Validation Techniques
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Naive Bayes Practical Implementation
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