Book Image

Python Machine Learning By Example - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
15
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16
Index

Getting Started with Machine Learning and Python

It is believed that in the next 30 years, artificial intelligence (AI) will outpace human knowledge. Regardless of whether it will lead to job losses, analytical and machine learning skills are becoming increasingly important. In fact, this point has been emphasized by the most influential business leaders, including the Microsoft co-founder, Bill Gates, Tesla CEO, Elon Musk, and former Google executive chairman, Eric Schmidt.

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 of working with data and working with models.

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

We will go into detail on the following topics:

  • The importance of machine learning
  • The core of machine learning—generalizing with data
  • Overfitting and underfitting
  • The bias-variance trade-off
  • Techniques to avoid overfitting
  • Techniques for data preprocessing
  • Techniques for feature engineering
  • Techniques for model aggregation
  • Setting up a Python environment
  • Installing the main Python packages
  • Introducing TensorFlow 2