Machine Learning Supervised Learning
Curated Problem Sets & Detailed Solutions
1
Evaluation Metrics: Confusion Matrix (Q1-11)
2
Linear Regression & Standard Errors (Practice Set)
3
Multiple Linear Regression
4
Ridge Regression
5
Logistic Regression
6
k-NN Algorithm
7
Naive Bayes Classifiers
8
Fisher Linear Discriminant
9
Bayesian Linear Discriminant Analysis
10
CV & ROC AUC Problem Solving
11
Decision Tree: Gini & Entropy (L.19)
12
SVM & Kernel Trick
13
ReLU & Neural Network Basics (226-250)
