Book Image

Python Machine Learning By Example - Fourth Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example - Fourth Edition

By: Yuxi (Hayden) Liu

Overview of this book

The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.
Table of Contents (18 chapters)
16
Other Books You May Enjoy
17
Index

Getting Started with Machine Learning and Python

The concept of artificial intelligence (AI) outpacing human knowledge is often referred to as the “technological singularity.” Some predictions in the AI research community and other fields suggest that the singularity could happen within the next 30 years. Regardless of its time horizon, one thing is clear: the rise of AI highlights the growing importance of analytical and machine learning skills. Mastering these disciplines equips us to not only understand and interact with increasingly complex AI systems but also actively participate in shaping their development and application, ensuring they benefit humanity.

In this chapter, we will kick off our machine learning journey with the basic, yet important, concepts of machine learning. We will start with what machine learning is all about, why we need it, and its evolution over a few decades. We will then discuss typical machine learning tasks and explore several essential techniques to work with data and models.

At the end of the chapter, we will set up the software for Python, the most popular language for machine learning and data science, and the libraries and tools that are required for this book.

We will go into detail on the following topics:

  • An introduction to machine learning
  • Knowing the prerequisites
  • Getting started with three types of machine learning
  • Digging into the core of machine learning
  • Data preprocessing and feature engineering
  • Combining models
  • Installing software and setting up