You're reading from Machine Learning Infrastructure and Best Practices for Software Engineers

Product type Book

Published in Jan 2024

Publisher Packt

ISBN-13 9781837634064

Pages 346 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Miroslaw Staron

Table of Contents (24) Chapters

Preface

1. Part 1:Machine Learning Landscape in Software Engineering

2. Machine Learning Compared to Traditional Software

3. Elements of a Machine Learning System

4. Data in Software Systems – Text, Images, Code, and Their Annotations

5. Data Acquisition, Data Quality, and Noise

6. Quantifying and Improving Data Properties

7. Part 2: Data Acquisition and Management

8. Processing Data in Machine Learning Systems

9. Feature Engineering for Numerical and Image Data

10. Feature Engineering for Natural Language Data

11. Part 3: Design and Development of ML Systems

12. Types of Machine Learning Systems – Feature-Based and Raw Data-Based (Deep Learning)

13. Training and Evaluating Classical Machine Learning Systems and Neural Networks

14. Training and Evaluation of Advanced ML Algorithms – GPT and Autoencoders

15. Designing Machine Learning Pipelines (MLOps) and Their Testing

16. Designing and Implementing Large-Scale, Robust ML Software

17. Part 4: Ethical Aspects of Data Management and ML System Development

18. Ethics in Data Acquisition and Management

19. Ethics in Machine Learning Systems

20. Integrating ML Systems in Ecosystems

21. Summary and Where to Go Next

22. Index

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Feature engineering – the basics

Feature engineering is the process of transforming raw data into vectors of numbers that can be used in machine learning algorithms. This process is structured and requires us to first select which feature extraction mechanism we need to use – which depends on the type of the task – and then configure the chosen feature extraction mechanism. When the chosen algorithm is configured, we can use it to transform the raw input data into a matrix of features – we call this process feature extraction. Sometimes, the data needs to be processed before (or after) the feature extraction, for example, by merging fields or removing noise. This process is called data wrangling.

The number of feature extraction mechanisms is large, and we cannot cover all of them. Not that we need to either. What we need to understand, however, is how the choice of feature extraction mechanism influences the properties of the data. We’ll dive...