Introducing Machine Learning, 1st edition

Published by Microsoft Press (January 31, 2020) © 2020

  • Dino Esposito
  • Francesco Esposito
  • Available for purchase from all major ebook resellers, including InformIT.com
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  • Available for purchase from all major ebook resellers, including InformIT.com

Title overview

Today, machine learning offers software professionals unparalleled opportunity for career growth. In Introducing Machine Learning, best-selling software development author, trainer, and consultant Dino Esposito offers a complete introduction to the field for students, programmers, architects, and lead developers alike.

Esposito begins by illuminating what’s known about how humans and machines learn, introducing the most important classes of machine learning algorithms, and explaining what each of them can do. Esposito demystifies key concepts ranging from neural networks to supervised and unsupervised learning. Next, he explains each step needed to build a successful machine learning solution, from collecting and fine-tuning source data to building and testing your solution.

Then, building on these essentials, he guides you through constructing two complete solutions with ML.NET, Microsoft’s powerful open source and cross-platform machine learning framework. Step by step, you’ll create systems for performing sentiment analysis on social feeds, and analyzing traffic to predict accidents. By the time you’re finished, you’ll be ready to participate in data science projects and build working solutions of your own.

Table of contents

  • Chapter 1 How Humans Learn
  • Chapter 2 Intelligent Software
  • Chapter 3 Mapping Problems and Algorithms
  • Chapter 4 General Steps for a Machine Learning Solution
  • Chapter 5 The Data Factor
  • Chapter 6 The .NET Way
  • Chapter 7 Implementing the ML.NET Pipeline
  • Chapter 8 ML.NET Tasks and Algorithms
  • Chapter 9 Math Foundations of Machine Learning
  • Chapter 10 Metrics of Machine Learning
  • Chapter 11 How to Make Simple Predictions: Linear Regression
  • Chapter 12 How to Make Complex Predictions and Decisions: Trees
  • Chapter 13 How to Make Better Decisions: Ensemble Methods
  • Chapter 14 Probabilistic Methods: Naïve Bayes
  • Chapter 15 How to Group Data: Classification and Clustering
  • Chapter 16 Feed-Forward Neural Networks
  • Chapter 17 Design of a Neural Network
  • Chapter 18 Other Types of Neural Networks
  • Chapter 19 Sentiment Analysis: An End-to-End Solution
  • Chapter 20 AI Cloud Services for the Real World
  • Chapter 21 The Business Perception of AI

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