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)

Logistic regression

The way of this book is one of generalizations. In the first chapter, we began with simpler representations of the reality, and so simpler criteria for grouping or predicting information structures.

After having reviewed linear regression, which is used mainly to predict a real value following a modeled linear function, we will advance to a generalization of it, which will allow us to separate binary outcomes (indicating that a sample belongs to a class), starting from a previously fitted linear function. So let's get started with this technique, which will be of fundamental use in almost all the following chapters of this book.

Problem domain of linear regression and logistic regression

To intuitively...