Book Image

Machine Learning for Developers

By : Rodolfo Bonnin
Book Image

Machine Learning for Developers

By: Rodolfo Bonnin

Overview of this book

Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the globe is, “How do I get started in Machine Learning?”. One reason could be attributed to the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This book is a systematic guide teaching you how to implement various Machine Learning techniques and their day-to-day application and development. You will start with the very basics of data and mathematical models in easy-to-follow language that you are familiar with; you will feel at home while implementing the examples. The book will introduce you to various libraries and frameworks used in the world of Machine Learning, and then, without wasting any time, you will get to the point and implement Regression, Clustering, classification, Neural networks, and more with fun examples. As you get to grips with the techniques, you’ll learn to implement those concepts to solve real-world scenarios for ML applications such as image analysis, Natural Language processing, and anomaly detections of time series data. By the end of the book, you will have learned various ML techniques to develop more efficient and intelligent applications.
Table of Contents (10 chapters)


Machine learning is one of the star disciplines at present. Acclaimed by the media as the future of work, it is said to be part of any significant tech investment in recent months, in a world where everything is driven by data and automation. It is used extensively across many fields such as image understanding, robotics, search engines, self-driving cars, and so on and the number of areas of application increases almost daily. In this book we will study the motivations and current techniques of machine learning using code and diagrams as the main conceptual vehicles, omitting outside the fundamental mathematical results.

We will start talking about the fundamental machine learning concepts, its branches, and types of problems. Then, there will be an explanatory chapter about the fundamental mathematical concepts required to grasp upcoming techniques. As we advance through the chapters, models of increasing complexity and sophistication are explained, starting with linear regression, then logistic regression, neural networks and its more recent variants (CNNs, RNNs),concluding with a synthetic introduction to more advanced machine learning techniques, such as GANs and reinforcement learning.

This book is aimed at developers looking to finally grasp what that machine learning hype is all about, and understand the main fundamental concepts, using an algorithmic point of view, along with more formal mathematical definitions.This book implements code concepts in Python, considering the simplicity of its interface, and the fact that Python offers an unmatched set of tools to continue learning from the book’s code. So, familiarity with Python programming would certainly be helpful for playing around with the code, but it should be manageable from programmers experienced in other languages.

You will learn how to make informed decisions about the types of algorithms you need to solve your own machine learning problems, and a knowledge of how those algorithms work to get the best possible results. If you want to understand machine learning in day-to-day, coder-friendly language, and have just the right amount of information to be able to do the jump into the discipline, this book will definitely come to your rescue!