The topic of this chapter is classification. In this setting of machine learning, you provide the system with examples of different classes of objects that you are interested in and then ask it to generalize to new examples where the class is not known. This may seem abstract, but you have probably already used this form of machine learning as a consumer, even if you were not aware of it: your email system will likely have the ability to automatically detect spam. That is, the system will analyze all incoming emails and mark them as either spam or not spam. Often, you, the end user, will be able to manually tag emails as spam or not, in order to improve its spam detection ability. This is exactly what we mean by classification: you provide examples of spam and and non-spam emails and then use an automated system to classify incoming emails...
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Table Of Contents
Building Machine Learning Systems with Python - Third Edition
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Building Machine Learning Systems with Python
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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)
Preface
Free Chapter
Getting Started with Python Machine Learning
Classifying with Real-World Examples
Regression
Classification I – Detecting Poor Answers
Dimensionality Reduction
Clustering – Finding Related Posts
Recommendations
Artificial Neural Networks and Deep Learning
Classification II – Sentiment Analysis
Topic Modeling
Classification III – Music Genre Classification
Computer Vision
Reinforcement Learning
Bigger Data
Where to Learn More About Machine Learning
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