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)


Congratulations! You have finished this book's introductory section, in which you have explored a great number of topics, and if you were able to follow it, you are prepared to start the journey of understanding the inner workings of many machine learning models.

In this chapter, we will explore some effective and simple approaches for automatically finding interesting data conglomerates, and so begin to research the reasons for natural groupings in data.

This chapter will covers the following topics:

  • A line-by-line implementation of an example of the K-means algorithm, with explanations of the data structures and routines
  • A thorough explanation of the k-nearest neighbors (K-NN) algorithm, using a code example to explain the whole process
  • Additional methods of determining the optimal number of groups representing a set of samples