Python Machine Learning Projects: Learn how to build Machine Learning projects from scratch (English Edition)

A complete guide that will help you get familiar with Machine Learning models, algorithms, and optimization techniques

Key Features
● Understand the core concepts and algorithms of Machine Learning.
● Get started with your Machine Learning career with this easy-to-understand guide.
● Discover different Machine Learning use cases across different domains.

Description
Since the last two decades, there have been many advancements in the field of Machine Learning. If you are new or want a comprehensive understanding of Machine Learning, then this book is for you.

The book starts by explaining how important Machine Learning is today and the technology required to make it work. The book then helps you get familiar with basic concepts that underlie Machine Learning, including basic Python Programming. It explains different types of Machine Learning algorithms and how they can be applied in various domains like Recommendation Systems, Text Analysis and Mining, Image Processing, and Social Media Analytics. Towards the end, the book briefly introduces you to the most popular metaheuristic algorithms for optimization.

By the end of the book, you will develop the skills to use Machine Learning effectively in various application domains.

What you will learn
● Discover various applications of Machine Learning in social media.
● Explore image processing techniques that can be used in Machine Learning.
● Learn how to use text mining to extract valuable insights from text data.
● Learn how to measure the performance of Machine Learning algorithms.
● Get familiar with the optimization algorithms in Machine Learning.

Who this book is for
This book delivers an excellent introduction to Machine Learning for beginners with no prior knowledge of coding, maths, or statistics. It is also helpful for existing and aspiring data professionals, students, and anyone who wishes to expand their Machine Learning knowledge.

Table of Contents
1. Introduction to ML
2. Python Basics for ML
3. An Overview of ML Algorithms
4. Case Studies and Projects in Machine Learning
5. Optimization in ML Algorithms