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)

What this book covers

Chapter 1, Introduction - Machine Learning and Statistical Science, covers various introductory concepts in machine learning. It talks about the history, branches and general discipline concepts. It also gives an introduction to the base mathematical concepts needed to understand most of the techniques developed afterward.

Chapter 2, The Learning Process, covers all the steps in the workflow of a machine learning process and shows useful tools and concept definitions for all those stages.

Chapter 3, Clustering, covers several techniques for unsupervised learning, specially K-Means, and K-NN clustering.

Chapter 4, Linear and Logistic Regression, covers two pretty different supervised learning algorithms, which go under a similar name: linear regression (which we will use to perform time series predictions), and logistic regression (which we will use for classification purposes).

Chapter 5, Neural Networks, covers one of the basic building blocks of modern machine learning Applications, and ends with the practical step-by-step building of a neural network.

Chapter 6, Convolutional Neural Networks, covers this powerful variation of neural networks, and ends with a practical tour of the internals of a very well known architecture of CNN, called VGG16, in a practical application.

Chapter 7, Recurrent Neural Networks, covers an overview of the RNN concept and a complete depiction of all the stages of the most used architecture, the LSTM. Finally, a practical exercise in time series prediction is shared.

Chapter 8, Recent Models and Developments, covers two upcoming techniques that have engaged huge interest in the field: generative adversarial networks, and the whole reinforcement learning field.

Chapter 9, Software Installation and Configuration, It covers the installation of all the necessary software packages, for three operative systems: Linux, macOS, and Windows.