Book Image

Building Machine Learning Systems with Python - Third Edition

By : Luis Pedro Coelho, Willi Richert, Matthieu Brucher
Book Image

Building Machine Learning Systems with Python - Third Edition

By: Luis Pedro Coelho, Willi Richert, Matthieu Brucher

Overview of this book

Machine learning enables systems to make predictions based on historical data. Python is one of the most popular languages used to develop machine learning applications, thanks to its extensive library support. This updated third edition of Building Machine Learning Systems with Python helps you get up to speed with the latest trends in artificial intelligence (AI). With this guide’s hands-on approach, you’ll learn to build state-of-the-art machine learning models from scratch. Complete with ready-to-implement code and real-world examples, the book starts by introducing the Python ecosystem for machine learning. You’ll then learn best practices for preparing data for analysis and later gain insights into implementing supervised and unsupervised machine learning techniques such as classification, regression and clustering. As you progress, you’ll understand how to use Python’s scikit-learn and TensorFlow libraries to build production-ready and end-to-end machine learning system models, and then fine-tune them for high performance. By the end of this book, you’ll have the skills you need to confidently train and deploy enterprise-grade machine learning models in Python.
Table of Contents (17 chapters)
Free Chapter
1
Getting Started with Python Machine Learning

A regression approach to recommendations

An alternative to neighborhoods is to formulate recommendations as a regression problem and apply the methods that we learned in the Chapter 6, Clustering - Finding Related Posts.

We first consider why this problem is not a good fit for a classification formulation. We could certainly attempt to learn a five-class model, using one class for each possible movie rating. However, there are two problems with this approach:

  • The different possible errors are not at all the same. For example, mistaking a 5-star movie for a 4-star one is not as serious a mistake as mistaking a 5-star movie for a 1-star one
  • Intermediate values make sense. Even if our inputs are only integer values, it is perfectly meaningful to say that the prediction is 4.3. We can see that this is a different prediction than 3.5, even if they both round to 4

These two factors...