This book is for developers/tech enthusiasts who want to understand the basics of machine learning concepts through a computationally-intensive approach. This book should be suited to people who have programmed in any scripting language, but being familiar with Python will be useful to play around with the code. It will also be useful for current data scientists to get back to the basic concepts, and understand them using a novel, hands-on approach.

#### Machine Learning for Developers

##### By :

#### Machine Learning for Developers

##### By:

#### 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)

Preface

Free Chapter

Introduction - Machine Learning and Statistical Science

The Learning Process

Clustering

Linear and Logistic Regression

Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

Recent Models and Developments

Software Installation and Configuration

Customer Reviews