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About this Book

Real-World Machine Learning is a book for people who want to apply machine learning (ML) to their own real-world problems. It describes and explains the processes, algorithms, and tools that mainstream ML comprises. The focus is on the practical application of well-known algorithms, not building them from scratch. Each step in the process of building and using ML models is presented and illustrated through examples that range from simple to intermediate-level complexity.

Roadmap

Part 1, “The machine-learning workflow,” introduces each of the five steps of the basic machine-learning workflow with a chapter:

  • Chapter 1, “What is machine learning?” introduces the field of machine learning and what it’s useful for.
  • Chapter 2, “Real-world data,” dives into common data processing and preparation steps in the ML workflow.
  • Chapter 3, “Modeling and prediction,” introduces how to build simple ML models and make predictions with widely used algorithms and libraries.
  • Chapter 4, “Model evaluation and optimization,” dives deeper into your ML models to evaluate and optimize their performance.
  • Chapter 5, “Basic feature engineering,” introduces the most common ways to augment your raw data with your knowledge of the problem.

Part 2, “Practical application,” introduces techniques for scaling your models and extracting features from text, images, and time-series data to improve performance on many modern ML problems. This part also includes three full example chapters.

How to use this book

Intended audience

Code conventions, downloads, and software requirements

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