Unit 1 : Introduction to Machine Learning

* Introduction to Machine Learning
* Python for Machine Learning
* Supervised vs Unsupervised ML
* Applications of Machine Learning
* Advantages of using Python libraries for ML

Unit 2 : Regression

* Introduction to Regression
* Simple Linear Regression
* Model Evaluation in Regression Models
* Evaluation Metrics in Regression Models
* Multiple Linear Regression
* Non-Linear Regression
* Applications of Regression

Unit 3 : Classification

* Introduction to Classification
* K-Nearest Neighbours
* Evaluation Metrics in Classification
* Introduction to Decision Trees
* Building Decision Trees

Unit 4 : Logistic Regression

* Intro to Logistic Regression
* Logistic regression vs Linear regression
* Logistic Regression Training
* Support Vector Machine

Unit 5 : Clustering

* Intro to Clustering
* Intro to k-Means
* More on k-Means
* Intro to Hierarchical Clustering
* More on Hierarchical Clustering

Unit 6 : Recommender Systems

* Intro to Recommender Systems
* Content-based Recommender Systems
* Collaborative Filtering