Building Machine Learning Systems with Python [2nd ed] 9781336281325, 1336281324, 9781784392772, 1784392774, 9781784392888, 178439288X

Table of contents :
Content: Cover
Copyright
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Table of Contents
Preface
Chapter 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
Introduction to NumPy, SciPy, and matplotlib
Installing Python
Chewing data efficiently with NumPy and intelligently with SciPy
Learning NumPy
Indexing
Handling nonexisting values
Comparing the runtime
Learning SciPy
Our first (tiny) application of machine learning. Reading in the dataPreprocessing and cleaning the data
Choosing the right model and learning algorithm
Before building our first model ...
Starting with a simple straight line
Towards some advanced stuff
Stepping back to go forward --
another look at our data
Training and testing
Answering our initial question
Summary
Chapter 2: Classifying with Real-world Examples
The Iris dataset
Visualization is a good first step
Building our first classification model
Evaluation --
holding out data and cross-validation
Building more complex classifiers. A more complex dataset and a more complex classifierLearning about the Seeds dataset
Features and feature engineering
Nearest neighbor classification
Classifying with scikit-learn
Looking at the decision boundaries
Binary and multiclass classification
Summary
Chapter 3: Clustering --
Finding Related Posts
Measuring the relatedness of posts
How not to do it
How to do it
Preprocessing --
similarity measured as a similar number of common words
Converting raw text into a bag of words
Counting words
Normalizing word count vectors
Removing less important words
Stemming. Stop words on steroidsOur achievements and goals
Clustering
K-means
Getting test data to evaluate our ideas on
Clustering posts
Solving our initial challenge
Another look at noise
Tweaking the parameters
Summary
Chapter 4: Topic Modeling
Latent Dirichlet allocation
Building a topic model
Comparing documents by topics
Modeling the whole of Wikipedia
Choosing the number of topics
Summary
Chapter 5: Classification --
Detecting Poor Answers
Sketching our roadmap
Learning to classify classy answers
Tuning the instance
Tuning the classifier
Fetching the data. Slimming the data down to chewable chunksPreselection and processing of attributes
Defining what is a good answer
Creating our first classifier
Starting with kNN
Engineering the features
Training the classifier
Measuring the classifier's performance
Designing more features
Deciding how to improve
Bias-variance and their tradeoff
Fixing high bias
Fixing high variance
High bias or low bias
Using logistic regression
A bit of math with a small example
Applying logistic regression to our post classification problem
Looking behind accuracy --
precision and recall.

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