This repository contains the contents of the 'Practically ML' Workshop.
Installations of required libraries can be done from requirements.txt.
Session 1:
Part1: Introduction to Python for Machine Learning (Introduction to Numpy, Pandas and Matplotlib)
Part2: Introduction to Machine Learning (Terminologies, types of learning)
Session 2: Linear Regression (Theory, code from scratch, sklearn implementation)
Session 3: Logistic Regression (Theory, Code from scratch, Concepts, Sklearn implementation)
Session 4: Unsupervised Learning - K-Means Clustering (Theory, Code from scratch, Concepts, Sklearn implementation) http://stanford.edu/class/ee103/visualizations/kmeans/kmeans.html
- Resources: 100 days of ML Code - Incomplete but a good start: https://github.com/Avik-Jain/100-Days-Of-ML-Code
Incomplete README, completion after the session.