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

Clustering – Finding Related Posts

Until now, we have always considered training as learning a function that maps some data to some labels. For the tasks in this chapter, we may not possess labels that we can use to learn the classification model. This could be, for example, because they were too expensive to collect. Just imagine the cost if the only way to obtain millions of labels was to ask humans to annotate those manually. What could we do in that case?

We find some pattern within the data itself. This is what we will do in this chapter, where we again consider the challenge of a question and answer website. When a user is browsing our site, perhaps because they are searching for specific information, the search engine will most likely point them to a specific answer. If the presented answers are not what they are looking for, the website should present (at least...