Machine Learning Algorithms

Cover
Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Table of Contents
Preface
Chapter 1: A Gentle Introduction to Machine Learning
	Introduction – classic and adaptive machines
		Descriptive analysis
		Predictive analysis
	Only learning matters
		Supervised learning
		Unsupervised learning
		Semi-supervised learning
		Reinforcement learning
		Computational neuroscience
	Beyond machine learning – deep learning and bio-inspired adaptive systems
	Machine learning and big data
	Summary
Chapter 2: Important Elements in Machine Learning
	Data formats
		Multiclass strategies
			One-vs-all
			One-vs-one
	Learnability
		Underfitting and overfitting
		Error measures and cost functions
		PAC learning
	Introduction to statistical learning concepts
		MAP learning
		Maximum likelihood learning
	Class balancing
		Resampling with replacement
		SMOTE resampling
	Elements of information theory
		Entropy
		Cross-entropy and mutual information 
		Divergence measures between two probability distributions
	Summary
Chapter 3: Feature Selection and Feature Engineering
	scikit-learn toy datasets
	Creating training and test sets
	Managing categorical data
	Managing missing features
	Data scaling and normalization
		Whitening
	Feature selection and filtering
	Principal Component Analysis
		Non-Negative Matrix Factorization
		Sparse PCA
		Kernel PCA
	Independent Component Analysis
	Atom extraction and dictionary learning
	Visualizing high-dimensional datasets using t-SNE
	Summary
Chapter 4: Regression Algorithms
	Linear models for regression
	A bidimensional example
	Linear regression with scikit-learn and higher dimensionality
		R2 score
		Explained variance
		Regressor analytic expression
	Ridge, Lasso, and ElasticNet
		Ridge
		Lasso
		ElasticNet
	Robust regression
		RANSAC
		Huber regression
	Bayesian regression
	Polynomial regression
	Isotonic regression
	Summary
Chapter 5: Linear Classification Algorithms
	Linear classification
	Logistic regression
	Implementation and optimizations
	Stochastic gradient descent algorithms
	Passive-aggressive algorithms
		Passive-aggressive regression
	Finding the optimal hyperparameters through a grid search
	Classification metrics
		Confusion matrix
		Precision
		Recall
		F-Beta
		Cohen's Kappa
		Global classification report
		Learning curve
	ROC curve
	Summary
Chapter 6: Naive Bayes and Discriminant Analysis
	Bayes' theorem
	Naive Bayes classifiers
	Naive Bayes in scikit-learn
		Bernoulli Naive Bayes
		Multinomial Naive Bayes
			An example of Multinomial Naive Bayes for text classification
		Gaussian Naive Bayes
	Discriminant analysis
	Summary
Chapter 7: Support Vector Machines
	Linear SVM
	SVMs with scikit-learn
		Linear classification
	Kernel-based classification
		Radial Basis Function
		Polynomial kernel
		Sigmoid kernel
		Custom kernels
		Non-linear examples
	ν-Support Vector Machines
	Support Vector Regression
		An example of SVR with the Airfoil Self-Noise dataset
	Introducing semi-supervised Support Vector Machines (S3VM)
	Summary
Chapter 8: Decision Trees and Ensemble Learning
	Binary Decision Trees
		Binary decisions
		Impurity measures
			Gini impurity index
			Cross-entropy impurity index
			Misclassification impurity index
		Feature importance
	Decision Tree classification with scikit-learn
	Decision Tree regression
		Example of Decision Tree regression with the Concrete Compressive Strength dataset
	Introduction to Ensemble Learning
		Random Forests
			Feature importance in Random Forests
		AdaBoost
		Gradient Tree Boosting
		Voting classifier
	Summary
Chapter 9: Clustering Fundamentals
	Clustering basics
	k-NN
	Gaussian mixture
		Finding the optimal number of components
	K-means
		Finding the optimal number of clusters
			Optimizing the inertia
			Silhouette score
			Calinski-Harabasz index
			Cluster instability
	Evaluation methods based on the ground truth
		Homogeneity 
		Completeness
		Adjusted Rand Index
	Summary
Chapter 10: Advanced Clustering
	DBSCAN
	Spectral Clustering
	Online Clustering
		Mini-batch K-means
		BIRCH
	Biclustering
	Summary
Chapter 11: Hierarchical Clustering
	Hierarchical strategies
	Agglomerative Clustering
		Dendrograms
		Agglomerative Clustering in scikit-learn
		Connectivity constraints
	Summary
Chapter 12: Introducing Recommendation Systems
	Naive user-based systems
		Implementing a user-based system with scikit-learn
	Content-based systems
	Model-free (or memory-based) collaborative filtering
	Model-based collaborative filtering
		Singular value decomposition strategy
		Alternating least squares strategy
		ALS with Apache Spark MLlib
	Summary 
Chapter 13: Introducing Natural Language Processing
	NLTK and built-in corpora
		Corpora examples
	The Bag-of-Words strategy
		Tokenizing
			Sentence tokenizing
			Word tokenizing
		Stopword removal
			Language detection
		Stemming
		Vectorizing
			Count vectorizing
				N-grams
			TF-IDF vectorizing
	Part-of-Speech
		Named Entity Recognition
	A sample text classifier based on the Reuters corpus
	Summary
Chapter 14: Topic Modeling and Sentiment Analysis in NLP
	Topic modeling
		Latent Semantic Analysis
		Probabilistic Latent Semantic Analysis
		Latent Dirichlet Allocation
	Introducing Word2vec with Gensim
	Sentiment analysis
		VADER sentiment analysis with NLTK
	Summary
Chapter 15: Introducing Neural Networks
	Deep learning at a glance
		Artificial neural networks
	MLPs with Keras
		Interfacing Keras to scikit-learn
	Summary
Chapter 16: Advanced Deep Learning Models
	Deep model layers
		Fully connected layers
			Convolutional layers
			Dropout layers
			Batch normalization layers
			Recurrent Neural Networks
	An example of a deep convolutional network with Keras
	An example of an LSTM network with Keras
	A brief introduction to TensorFlow
		Computing gradients
		Logistic regression
		Classification with a multilayer perceptron
		Image convolution
	Summary
Chapter 17: Creating a Machine Learning Architecture
	Machine learning architectures
		Data collection
		Normalization and regularization
		Dimensionality reduction
		Data augmentation
		 Data conversion
		Modeling/grid search/cross-validation
		Visualization
		GPU support
		A brief introduction to distributed architectures
	Scikit-learn tools for machine learning architectures
		Pipelines
		Feature unions
	Summary
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