Machine learning allows models or systems to learn without being explicitly programmed. You will see how to use the best library support available, including, scikit-learn, TensorFlow, and many others, to build efficient, smart systems.
Preface
by Matthieu Brucher, Wilhelm Richert, Luis Pedro Coelho
Building Machine Learning Systems with Python - Third Edition
- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Preface
- Getting Started with Python Machine Learning
- Machine learning and Python – a dream team
- What the book will teach you – and what it will not
- How to best read this book
- What to do when you are stuck
- Getting started
- Fundamentals of machine learning
- Our first (tiny) application of machine learning
- Summary
- Machine learning and Python – a dream team
- Classifying with Real-World Examples
- Regression
- Classification I – Detecting Poor Answers
- Sketching our roadmap
- Learning to classify classy answers
- Fetching the data
- Creating our first classifier
- Deciding how to improve the performance
- Using logistic regression
- Looking behind accuracy – precision and recall
- Slimming the classifier
- Ship it!
- Classification using Tensorflow
- Summary
- 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
- Other Books You May Enjoy
Preface
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