Introducing Machine Learning (Developer Reference)

Introducing Machine Learning (Developer Reference)

Book Description
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


Part I: Laying the Groundwork of Machine Learning
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

Part II: Machine Learning in .NET
Chapter 6. The .NET Way
Chapter 7. Implementing the ML.NET Pipeline
Chapter 8. ML.NET Tasks and Algorithms

Part III: Fundamentals of Shallow Learning
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 dаta: Classification and Clustering

Part IV: Fundamentals of Deep Learning
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

Part V: Final Thoughts
Chapter 20. AI Cloud Services for the Real World
Chapter 21. The Business Perception of AI

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