In machine learning, what we are doing is asking a question and answering it. From the samples we have, we create a question that is the learning aspect of the model. Answering the question involves using the model for new samples.
Fundamentals of machine learning
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
Fundamentals of machine learning
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