Building Machine Learning Systems with Python

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.