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Introducing Machine Learning

by Dino Esposito

A practical introduction to building machine learning applications with ML.NET

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Paperback New English

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AlbakiReads Editorial

A practical introduction to building machine learning applications with ML.NET

Introducing Machine Learning maps the path from core AI and machine learning concepts to application development in Microsoft’s ecosystem. It outlines the stages of a machine learning project, including data processing, model training, evaluation, and the delivery of a model, while using ML.NET coding scenarios to connect those stages to implementation. Coverage extends across algorithm families, prediction and decision-making tasks, classification, clustering, probabilistic methods, and introductory neural-network design. The book also addresses AutoML, mathematical foundations, Azure runtime services, and the business considerations surrounding machine learning, framing technical work within real-world solution building.

Good fit for readers who enjoy

  • Developers building machine learning applications with ML.NET
  • Data scientists seeking programming-oriented machine learning examples
  • Professionals interested in Azure-based machine learning services

Themes

Machine learning workflowsML.NET developmentData processing and model evaluationClassification, clustering, and predictionDeep learning and cloud AI services

Based on publisher information and book metadata.

Book overview

Master machine learning concepts and develop real-world solutions

Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft's powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning.

- 14-time Microsoft MVP Dino Esposito and Francesco Esposito help you

- Explore what's known about how humans learn and how intelligent software is built

- Discover which problems machine learning can address

- Understand the machine learning pipeline: the steps leading to a deliverable model

- Use AutoML to automatically select the best pipeline for any problem and dataset

- Master ML.NET, implement its pipeline, and apply its tasks and algorithms

- Explore the mathematical foundations of machine learning

- Make predictions, improve decision-making, and apply probabilistic methods

- Group data via classification and clustering

- Learn the fundamentals of deep learning, including neural network design

- Leverage AI cloud services to build better real-world solutions faster

About This Book

- For professionals who want to build machine learning applications: both developers who need data science skills and data scientists who need relevant programming skills

- Includes examples of machine learning coding scenarios built using the ML.NET library

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