1. ML Intro & Algorithms

• Introduction to Machine Learning
• Application of Machine Learning
• Real World Examples of Machine Learning and Use
• Types of Machine Learning Approaches
• Supervised Machine Learning Algorithms
• Unsupervised Machine Learning Algorithms
• Semi-supervised Machine Learning Algorithms
• Reinforcement Machine Learning Algorithms
• Types of Supervised Algorithms
• Classification Algorithms
• Linear Regression
• Logistic Regression
• Polynomial Regression
• Support Vector Machines(SVM)
• Naive Bayes Method
• Types of Unsupervised Algorithms
• Clustering and its types
• Decision Trees
• Random Forest
• Introduction to Python(Loops, conditions )

2. Regression

• Linear Regression
• Non-linear Regression
• Model evaluation methods

3. Classification

• K-Nearest Neighbour
• Decision Trees
• Logistic Regression
• Support Vector Machines
• Model Evaluation

4. Unsupervised Learning

• K-Means Clustering
• Hierarchical Clustering
• Density-Based Clustering

5. Recommender Systems

• Content-based recommender systems
• Collaborative Filtering