更新时间：2021-08-13 16:36:01
封面
版权信息
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Chapter 1. Getting Started with Python Machine Learning
Machine learning and Python – the dream team
What the book will teach you (and what it will not)
What to do when you are stuck
Getting started
Our first (tiny) machine learning application
Summary
Chapter 2. Learning How to Classify with Real-world Examples
The Iris dataset
Building more complex classifiers
A more complex dataset and a more complex classifier
Binary and multiclass classification
Chapter 3. Clustering – Finding Related Posts
Measuring the relatedness of posts
Preprocessing – similarity measured as similar number of common words
Clustering
Solving our initial challenge
Tweaking the parameters
Chapter 4. Topic Modeling
Latent Dirichlet allocation (LDA)
Comparing similarity in topic space
Choosing the number of topics
Chapter 5. Classification – Detecting Poor Answers
Sketching our roadmap
Learning to classify classy answers
Fetching the data
Creating our first classifier
Deciding how to improve
Using logistic regression
Looking behind accuracy – precision and recall
Slimming the classifier
Ship it!
Chapter 6. Classification II – Sentiment Analysis
Fetching the Twitter data
Introducing the Naive Bayes classifier
Creating our first classifier and tuning it
Cleaning tweets
Taking the word types into account
Chapter 7. Regression – Recommendations
Predicting house prices with regression
Penalized regression
P greater than N scenarios
Chapter 8. Regression – Recommendations Improved
Improved recommendations
Basket analysis
Chapter 9. Classification III – Music Genre Classification
Fetching the music data
Looking at music
Using FFT to build our first classifier
Improving classification performance with Mel Frequency Cepstral Coefficients
Chapter 10. Computer Vision – Pattern Recognition
Introducing image processing
Loading and displaying images
Classifying a harder dataset
Local feature representations
Chapter 11. Dimensionality Reduction
Selecting features
Other feature selection methods
Feature extraction
Multidimensional scaling (MDS)
Chapter 12. Big(ger) Data
Learning about big data
Using jug to break up your pipeline into tasks
Using Amazon Web Services (AWS)
Appendix A. Where to Learn More about Machine Learning
Online courses
Books
What was left out
Index
