Designing Machine Learning Systems with Python

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 – 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 – 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