Python machine learning : unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics

Cover
Copyright
Credits
Foreword
About the Author
About the Reviewers
www.PacktPub.com
Table of Contents
Preface
Chapter 1: Giving Computers the Ability to Learn from Data
	Building intelligent machines to transform data into knowledge
	The three different types of machine learning
		Making predictions about the future with supervised learning
			Classification for predicting class labels
			Regression for predicting continuous outcomes
		Solving interactive problems with reinforcement learning
		Discovering hidden structures with unsupervised learning
			Finding subgroups with clustering
			Dimensionality reduction for data compression
	An introduction to the basic terminology and notations
	A roadmap for building machine learning systems
		Preprocessing – getting data into shape
		Training and selecting a predictive model
		Evaluating models and predicting unseen data instances
	Using Python for machine learning
		Installing Python packages
	Summary
Chapter 2: Training Machine Learning Algorithms for Classification
	Artificial neurons – a brief glimpse into the early history of machine learning
	Implementing a perceptron learning algorithm in Python
		Training a perceptron model on the Iris dataset
	Adaptive linear neurons and the convergence of learning
		Minimizing cost functions with gradient descent
		Implementing an Adaptive Linear Neuron in Python
		Large scale machine learning and stochastic gradient descent
	Summary
Chapter 3: A Tour of Machine Learning Classifiers Using Scikit-learn
	Choosing a classification algorithm
	First steps with scikit-learn
		Training a perceptron via scikit-learn
	Modeling class probabilities via logistic regression
		Logistic regression intuition and conditional probabilities
		Learning the weights of the logistic cost function
		Training a logistic regression model with scikit-learn
		Tackling overfitting via regularization
	Maximum margin classification with support vector machines
		Maximum margin intuition
		Dealing with the nonlinearly separable case using slack variables
		Alternative implementations in scikit-learn
	Solving nonlinear problems using a kernel SVM
		Using the kernel trick to find separating hyperplanes in higher dimensional space
	Decision tree learning
		Maximizing information gain – getting the most bang for the buck
		Building a decision tree
		Combining weak to strong learners via random forests
	K-nearest neighbors – a lazy learning algorithm
	Summary
Chapter 4: Building Good Training 
Sets – Data Preprocessing
	Dealing with missing data
		Eliminating samples or features with missing values
		Imputing missing values
		Understanding the scikit-learn estimator API
	Handling categorical data
		Mapping ordinal features
		Encoding class labels
		Performing one-hot encoding on nominal features
	Partitioning a dataset in training and test sets
	Bringing features onto the same scale
	Selecting meaningful features
		Sparse solutions with L1 regularization
		Sequential feature selection algorithms
	Assessing feature importance with random forests
	Summary
Chapter 5: Compressing Data via Dimensionality Reduction
	Unsupervised dimensionality reduction via principal component analysis
		Total and explained variance
		Feature transformation
		Principal component analysis in scikit-learn
	Supervised data compression via linear discriminant analysis
		Computing the scatter matrices
		Selecting linear discriminants for the new feature subspace
		Projecting samples onto the new feature space
		LDA via scikit-learn
	Using kernel principal component analysis for nonlinear mappings
		Kernel functions and the kernel trick
		Implementing a kernel principal component analysis in Python
			Example 1 – separating half-moon shapes
			Example 2 – separating concentric circles
		Projecting new data points
		Kernel principal component analysis in scikit-learn
	Summary
Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning
	Streamlining workflows with pipelines
		Loading the Breast Cancer Wisconsin dataset
		Combining transformers and estimators in a pipeline
	Using k-fold cross-validation to assess model performance
		The holdout method
		K-fold cross-validation
	Debugging algorithms with learning and validation curves
		Diagnosing bias and variance problems with learning curves
		Addressing overfitting and underfitting with validation curves
	Fine-tuning machine learning models via grid search
		Tuning hyperparameters via grid search
		Algorithm selection with nested cross-validation
	Looking at different performance evaluation metrics
		Reading a confusion matrix
		Optimizing the precision and recall of a classification model
		Plotting a receiver operating characteristic
		The scoring metrics for multiclass classification
	Summary
Chapter 7: Combining Different Models for Ensemble Learning
	Learning with ensembles
	Implementing a simple majority vote classifier
		Combining different algorithms for classification with majority vote
	Evaluating and tuning the ensemble classifier
	Bagging – building an ensemble of classifiers from bootstrap samples
	Leveraging weak learners via adaptive boosting
	Summary
Chapter 8: Applying Machine Learning to Sentiment Analysis
	Obtaining the IMDb movie review dataset
	Introducing the bag-of-words model
		Transforming words into feature vectors
		Assessing word relevancy via term frequency-inverse document frequency
		Cleaning text data
		Processing documents into tokens
	Training a logistic regression model for document classification
	Working with bigger data – online algorithms and out-of-core learning
	Summary
Chapter 9: Embedding a Machine Learning Model into 
a Web Application
	Serializing fitted scikit-learn estimators
	Setting up a SQLite database for data storage
	Developing a web application with Flask
		Our first Flask web application
		Form validation and rendering
	Turning the movie classifier into a web application
	Deploying the web application to a public server
		Updating the movie review classifier
	Summary
Chapter 10: Predicting Continuous 
Target Variables with Regression Analysis
	Introducing a simple linear regression model
	Exploring the Housing Dataset
		Visualizing the important characteristics of a dataset
	Implementing an ordinary least squares linear regression model
		Solving regression for regression parameters with gradient descent
		Estimating the coefficient of a regression model via scikit-learn
	Fitting a robust regression model using RANSAC
	Evaluating the performance of linear regression models
	Using regularized methods for regression
	Turning a linear regression model into a curve – polynomial regression
		Modeling nonlinear relationships in the Housing Dataset
		Dealing with nonlinear relationships using random forests
			Decision tree regression
			Random forest regression
	Summary
Chapter 11
: Working with Unlabeled 
Data – Clustering Analysis
	Grouping objects by similarity using k-means
		K-means++
		Hard versus soft clustering
		Using the elbow method to find the optimal number of clusters
		Quantifying the quality of clustering via silhouette plots
	Organizing clusters as a hierarchical tree
		Performing hierarchical clustering on a distance matrix
		Attaching dendrograms to a heat map
		Applying agglomerative clustering via scikit-learn
	Locating regions of high density via DBSCAN
	Summary
Chapter 12: Training Artificial Neural Networks for Image Recognition
	Modeling complex functions with artificial neural networks
		Single-layer neural network recap
		Introducing the multi-layer neural network architecture
		Activating a neural network via forward propagation
	Classifying handwritten digits
		Obtaining the MNIST dataset
		Implementing a multi-layer perceptron
	Training an artificial neural network
		Computing the logistic cost function
		Training neural networks via backpropagation
	Developing your intuition for backpropagation
	Debugging neural networks with gradient checking
	Convergence in neural networks
	Other neural network architectures
		Convolutional Neural Networks
		Recurrent Neural Networks
	A few last words about neural network implementation
	Summary
Chapter 13: Parallelizing Neural Network Training with Theano
	Building, compiling, and running expressions with Theano
		What is Theano?
		First steps with Theano
		Configuring Theano
		Working with array structures
		Wrapping things up – a linear regression example
	Choosing activation functions for feedforward neural networks
		Logistic function recap
		Estimating probabilities in multi-class classification via the softmax function
		Broadening the output spectrum by using a hyperbolic tangent
	Training neural networks efficiently using Keras
	Summary
Index