Machine Learning for Developers (Ebook, PDF)

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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.

What you will learn

  • Learn the math and mechanics of Machine Learning via a developer-friendly approach
  • Get to grips with widely used Machine Learning algorithms/techniques and how to use them to solve real problems
  • Get a feel for advanced concepts, using popular programming frameworks.
  • Prepare yourself and other developers for working in the new ubiquitous field of Machine Learning
  • Get an overview of the most well known and powerful tools, to solve computing problems using Machine Learning.
  • Get an intuitive and down-to-earth introduction to current Machine Learning areas, and apply these concepts on interesting and cutting-edge problems.

About the Author

Rodolfo Bonnin is a systems engineer and Ph.D. student at Universidad Tecnológica Nacional, Argentina. He has also pursued parallel programming and image understanding postgraduate courses at Universität Stuttgart, Germany.

He has been doing research on high-performance computing since 2005 and began studying and implementing convolutional neural networks in 2008, writing a CPU- and GPU-supporting neural network feedforward stage. More recently he’s been working in the field of fraud pattern detection with Neural Networks and is currently working on signal classification using machine learning techniques.

He is also the author of Building Machine Learning Projects with Tensorflow, by Packt Publishing.

Table of Contents

  1. Introduction
  2. The learning process
  3. Clustering
  4. Linear and Logistic Regression
  5. Neural Networks
  6. CNN
  7. RNN
  8. Recent models and developments
  9. Software Installation and Configuration
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