Part 1 Basics

Machine learning and AI are not magic. Neither are they a secret art that can be understood only by a select few. At their core, they are simply a way for us to help the algorithms assimilate historical datasets and generate insights for us. These insights help us to initiate better, faster, and more influential business effects We give clear, logical instructions that guide the algorithms to do what we want.

But, like any art, learning machine learning and AI take practice. It’s not about memorizing Python or R or a programming language syntax or learning some commands to run the code or cut-paste the code. It’s about solving pragmatic business problems, thinking about the business objectives critically, and breaking down those complex tasks meticulously into smaller and manageable steps and hence achieving the business objective.

This book isn’t just about writing code; it’s about learning how to think like a data scientist.

If you’ve never studied unsupervised learning or you’ve never written a single line of Python code, that’s perfectly fine. It is much easier than you think. We start with simple unsupervised learning algorithms.

All the very best on this journey. Let’s start with the basics, one step at a time.