Luis Pedro Coelho, Willi Richert - Building Machine Learning Systems with Python - Second Edition [2015, ePub, ENG]

1: Getting Started with Python Machine Learning
Machine learning and Python – a 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) application of machine learning
Summary

2: Classifying with Real-world Examples
The Iris dataset
Building more complex classifiers
A more complex dataset and a more complex classifier
Classifying with scikit-learn
Binary and multiclass classification
Summary

3: Clustering – Finding Related Posts
Measuring the relatedness of posts
Preprocessing – similarity measured as a similar number of common words
Clustering
Solving our initial challenge
Tweaking the parameters
Summary

4: Topic Modeling
Latent Dirichlet allocation
Comparing documents by topics
Choosing the number of topics
Summary

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!
Summary

6: Classification II – Sentiment Analysis
Sketching our roadmap
Fetching the Twitter data
Introducing the Naïve Bayes classifier
Creating our first classifier and tuning it
Cleaning tweets
Taking the word types into account
Summary

7: Regression
Predicting house prices with regression
Penalized or regularized regression
Summary

8: Recommendations
Rating predictions and recommendations
Basket analysis
Summary

9: Classification – Music Genre Classification
Sketching our roadmap
Fetching the music data
Looking at music
Using FFT to build our first classifier
Improving classification performance with Mel Frequency Cepstral Coefficients
Summary

10: Computer Vision
Introducing image processing
Local feature representations
Summary

11: Dimensionality Reduction
Sketching our roadmap
Selecting features
Feature extraction
Multidimensional scaling
Summary

12: Bigger Data
Learning about big data
Using Amazon Web Services
Summary