Book Image

Machine Learning for Developers

By : Rodolfo Bonnin, Md Mahmudul Hasan
Book Image

Machine Learning for Developers

By: Rodolfo Bonnin, Md Mahmudul Hasan

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)

Feature engineering

Feature engineering is in some ways one of the most underrated parts of the machine learning process, even though it is considered the cornerstone of the learning process by many prominent figures of the community.

What's the purpose of this process? In short, it takes the raw data from databases, sensors, archives, and so on, and transforms it in a way that makes it easy for the model to generalize. This discipline takes criteria from many sources, including common sense. It's indeed more like an art than a rigid science. It is a manual process, even when some parts of it can be automatized via a group of techniques grouped in the feature extraction field.

As part of this process we also have many powerful mathematical tools and dimensionality reduction techniques, such as Principal Component Analysis (PCA) and Autoencoders, that allow data scientists...