Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. It is the science of getting computers to act without being explicitly programmed.

Here are a few materials:

• Statistics Book - Business Statistics - A First Course by David M. Levine, David F. Stephan, Kathryn A. Szabat, P.K. Viswanatha

• Head First Statistics by Dawn Griffiths - Available at Safari Books Online

• YouTube Channel - StatQuest with Josh Starmer is a good place to start.

• Introduction to Statistical Learning in R (ISLR) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

• YouTube video series of ISLR

• The Hundred-Page Machine Learning Book by Andriy Burkov. This book will provide a concise understanding of various ML Algorithms. It comes with a wiki with additional information like Q&A, code snippets, further reading, tools, and other relevant resources. Having a prior understanding of various ML Techniques is advised.

• Python for Data Science. Taking some Udemy course will help.

Table of Contents

1. Missing Value Treatment

2. Scaling & Normalization

3. Hypothesis Testing

4. Exploratory Data Analysis - Titanic data set(Kaggle)