For companies, it is vital to closely monitor the public reception of key events, such as product launches or press releases. With real-time access and easy accessibility of user-generated content on Twitter, it is now possible to do sentiment classification of tweets. Sometimes also called opinion mining, it is an active field of research in which several companies are already selling such services. As this shows that there obviously exists a market, we are motivated to use our classification muscles built in the last chapter to build our own home-grown sentiment classifier.
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Book Overview & Buying
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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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