A typical reader will have a basic to intermediate knowledge of undergraduate mathematics, such as probabilities, statistics, calculus, and linear algebra. No advanced mathematics is required as the book will be mostly self-contained. Basic to intermediate knowledge of Machine Learning (ML) algorithms is required. No advance concepts of machine learning are required as the book will be mostly self-contained. A decent knowledge in Python is required too as it would be out-of-scope to go through an introduction to Python but each procedure will be explained step-by-step to be reproducible.

This book is full if practical examples. The reader wants to know about how to apply the Machine Learning (ML) algorithms for real life data science applications efficiently. This book starts from the basic ML techniques which can be used to develop base-line approach. After that readers learn how to apply optimization techniques for each application in order to achieve the state-of-the-art result. For each application, I have specified the basic concepts, tips and tricks along with the code.