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# COURSE SYLLABUS

• Linear Algebra and Probability
• Linear Algebra Review and Reference
• Matrices and Vectors
• Matrix Vector Multiplication
• Matrix Matrix Multiplication
• Matrix Multiplication Properties
• Inverse and Transpose
• Weighted Least Squares. Logistic Regression. Netwon’s Method Perceptron. Exponential Family. Generalized Linear Models
• How to get the dataset
• Missing Data
• Categorical Data
• Splitting the Dataset into the Training set and Test set
• Feature Scaling
• Simple Linear Regression in Python
• Multiple Linear Regression
• What is the P-Value?
• Multiple Linear Regression Intuition
• Multiple Linear Regression in Python
• Polynomial Regression Intuition
• Polynomial Regression in Python
• Python Regression Template
• Support Vector Regression (SVR)
• Decision Tree Regression
• Random Forest Regression
• Evaluating Regression Models Performance
• Logistic Regression
• K-Nearest Neighbors (K-NN)
• Support Vector Machine (SVM)
• Kernel SVM
• Naive Bayes
• Decision Tree Classification
• Random Forest Classification
• Evaluating Classification Models Performance
• K-Means Clustering
• Hierarchical Clustering​
• Multilayered Perceptron
• Classifier Evaluation
• Ensemble Learning, Boosting
• Unsupervised Learning, Clustering
• Dimensionality Reduction
• Reinforcement Learning
• Introduction to Learning Theory
• Anomaly Detection
• Problem Motivation
• Gaussian Distribution
• Recommender System
• Need to list assignments on each algorithm.

Sidharth
Neeraj
Harleen Kaur