Machine Learning for Beginners: A Complete and Phased Beginner's Guide to Learning and Understanding Machine Learning and Artificial Intelligence

Imagine a world where computing systems understand people and the world around us them to a point where they can notice patterns, collect data, interpret it and give recommendations to solve real world problems with high level of precision. It sounds like science fiction but it is happening in healthcare, agriculture, cyber security, facial recognition, targeting and retargeting customers in online advertising, recommending specific products, stories, videos, text etc., self-driving cars, real time pricing, predicting human behavior and much more. Now imagine you being one of the people behind the code; the people who get these advanced systems to work the way they do.

Would it be a dream come true for you?

By virtue that you are reading this, it is clear that you have some special liking for this advanced tech and would want to learn how you can be one of the people behind the code. Even if not, you probably want to be able to understand the inner workings of these systems.

The concept may sound extremely out there and advanced but it won't be if you follow this guide, which takes an easy to follow, beginner friendly language to help you to understand the ins and outs of machine learning

Here is a summary of what this book will teach you:

• The basics of machine learning, including what it is, how machine learning has evolved over the years, the application of machine learning in today's world and the future of machine learning
• How machine learning is beneficial in today's world
• The different approaches to machine learning, including unsupervised, supervised, reinforcement learning method, semi-supervised machine learning and many others
• The concept of big data analysis, including what is big data, why big data is important, the application of big data in today's world as well as the different data analysis tools that you can use
• The link between big data and machine learning
• The different machine learning algorithms, including what machine-learning algorithms are and how and when the different learning algorithms are used
• The concept of artificial neural networks, including how they work, when to use neural networks and more
• How decision trees are used in machine learning, including what decision trees are (in respect to machine learning), how they work, how the decision tree is read, the different nodes in decision trees and when to use them
• The ins and outs of linear and logistic regression in machine learning, including what linear regression is, different types of regression, how linear regression works, how linear regression is used and much more
• And much more

Even if this is your first encounter with the concept of machine learning, this book will uncover everything you need to know to master machine learning and possibly get started in this field of advanced computing knowing very well what you are venturing into.

And the good thing is that the book takes a beginner friendly approach to help you to apply what you learn right away