Introduction To Machine Learning

Course Description

This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences.

What you will learn

• You will gain confidence when working with 2 of the leading ML packages - stats models and sklearn
• You will learn how to perform a linear regression
• You will become familiar with the ins and outs of a logistic regression
• You will excel at carrying out cluster analysis (both flat and hierarchical)
• You will learn how to apply your skills to real-life business cases
• You will be able to comprehend the underlying ideas behind ML models

1. Introduction
2. Setting Up the Working Environment
3. Linear Regression with Sklearn
4. Linear Regression –Practical Example
5. Logistic Regression
6. Cluster Analysis
7. Cluster Analysis: Additional Topics

Requirements

Basic coding skills in Python