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

Summary

Congratulations! You just learned two important things, of which the most important is that, as a typical machine learning operator, you will spend most of your time understanding and refining the data—exactly what we just did in our first, tiny machine learning example. We hope that this example helped you to start switching your mental focus from algorithms to data.

Then, you learned how important it is to have the correct experiment setup, and that it is vital to not mix up training and testing. Admittedly, the use of polynomial fitting is not the coolest thing in the machine learning world; we chose it so that you would not be distracted by the coolness of some shiny algorithm when we conveyed those two most important messages we mentioned earlier.

So, let's move on to Chapter 2, Classifying with Real-world Examples, we are on the topic of classification. Now, we will apply the concepts on a very specific, but very important, type of data, namely text.