Machine Learning for Developers

Your one-stop guide to becoming a Machine Learning expert.
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Machine Learning for Developers

Rodolfo Bonnin

Your one-stop guide to becoming a Machine Learning expert.
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Book Details

ISBN 139781786469878
Paperback270 pages

Book Description

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

Chapter 1: Introduction - Machine Learning and Statistical Science
Machine learning in the bigger picture
Tools of the trade–programming language and libraries
Basic mathematical concepts
Summary
Chapter 2: The Learning Process
Understanding the problem
Dataset definition and retrieval
Feature engineering
Dataset preprocessing
Model definition
Loss function definition
Model fitting and evaluation
Model implementation and results interpretation
Summary
References
Chapter 3: Clustering
Grouping as a human activity
Automating the clustering process
Finding a common center - K-means
Nearest neighbors
K-NN sample implementation
Summary
References
Chapter 4: Linear and Logistic Regression
Regression analysis
Linear regression
Data exploration and linear regression in practice
Logistic regression
Summary
References
Chapter 5: Neural Networks
History of neural models
Implementing a simple function with a single-layer perceptron
Summary
References
Chapter 6: Convolutional Neural Networks
Origin of convolutional neural networks
Deep neural networks
Deploying a deep neural network with Keras
Exploring a convolutional model with Quiver
References
Summary
Chapter 7: Recurrent Neural Networks
Solving problems with order — RNNs
LSTM
Univariate time series prediction with energy consumption data
Summary
References
Chapter 8: Recent Models and Developments
GANs
Reinforcement learning
Basic RL techniques: Q-learning
References
Summary
Chapter 9: Software Installation and Configuration
Linux installation
macOS X environment installation
Windows installation
Summary

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.

Authors

Table of Contents

Chapter 1: Introduction - Machine Learning and Statistical Science
Machine learning in the bigger picture
Tools of the trade–programming language and libraries
Basic mathematical concepts
Summary
Chapter 2: The Learning Process
Understanding the problem
Dataset definition and retrieval
Feature engineering
Dataset preprocessing
Model definition
Loss function definition
Model fitting and evaluation
Model implementation and results interpretation
Summary
References
Chapter 3: Clustering
Grouping as a human activity
Automating the clustering process
Finding a common center - K-means
Nearest neighbors
K-NN sample implementation
Summary
References
Chapter 4: Linear and Logistic Regression
Regression analysis
Linear regression
Data exploration and linear regression in practice
Logistic regression
Summary
References
Chapter 5: Neural Networks
History of neural models
Implementing a simple function with a single-layer perceptron
Summary
References
Chapter 6: Convolutional Neural Networks
Origin of convolutional neural networks
Deep neural networks
Deploying a deep neural network with Keras
Exploring a convolutional model with Quiver
References
Summary
Chapter 7: Recurrent Neural Networks
Solving problems with order — RNNs
LSTM
Univariate time series prediction with energy consumption data
Summary
References
Chapter 8: Recent Models and Developments
GANs
Reinforcement learning
Basic RL techniques: Q-learning
References
Summary
Chapter 9: Software Installation and Configuration
Linux installation
macOS X environment installation
Windows installation
Summary

Book Details

ISBN 139781786469878
Paperback270 pages
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