Cover
Copyright
Credits
About the Author
About the Reviewer
www.PacktPub.com
Table of Contents
Preface
Chapter 1: Thinking in Machine Learning
The human interface
Design principles
Types of questions
Are you asking the right question?
Tasks
Classification
Regression
Clustering
Dimensionality reduction
Errors
Optimization
Linear programming
Models
Features
Unified modeling language
Class diagrams
Object diagrams
Activity diagrams
State diagrams
Summary
Chapter 2: Tools and Techniques
Python for machine learning
IPython console
Installing the SciPy stack
NumPY
Constructing and transforming arrays
Mathematical operations
Matplotlib
Pandas
SciPy
Scikit-learn
Summary
Chapter 3: Turning Data into Information
What is data?
Big data
Challenges of big data
Data volume
Data velocity
Data variety
Data models
Data distributions
Data from databases
Data from the Web
Data from natural language
Data from images
Data from application programming interfaces
Signals
Data from sound
Cleaning data
Visualizing data
Summary
Chapter 4: Models โ Learning from Information
Logical models
Generality ordering
Version space
Coverage space
PAC learning and computational complexity
Tree models
Purity
Rule models
The ordered list approach
Set-based rule models
Summary
Chapter 5: Linear Models
Introducing least squares
Gradient descent
The normal equation
Logistic regression
The Cost function for logistic regression
Multiclass classification
Regularization
Summary
Chapter 6: Neural Networks
Getting started with neural networks
Logistic units
Cost function
Minimizing the cost function
Implementing a neural network
Gradient checking
Other neural net architectures
Summary
Chapter 7: Features โ How Algorithms See the World
Feature types
Quantitative features
Ordinal features
Categorical features
Operations and statistics
Structured features
Transforming features
Discretization
Normalization
Calibration
Principle component analysis
Summary
Chapter 8: Learning with Ensembles
Ensemble types
Bagging
Random forests
Extra trees
Boosting
Adaboost
Gradient boosting
Ensemble strategies
Other methods
Summary
Chapter 9: Design Strategies and
Case Studies
Evaluating model performance
Model selection
Gridsearch
Learning curves
Real-world case studies
Building a recommender system
Content-based filtering
Collaborative filtering
Reviewing the case study
Insect detection in greenhouses
Reviewing the case study
Machine learning at a glance
Summary
Index