Name: Machine Learning Algorithms
Author: Giuseppe Bonaccorso
ISBN: 9781785884511

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Machine Learning Algorithms

Giuseppe Bonaccorso

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Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
A Gentle Introduction to Machine Learning
Introduction - classic and adaptive machines
Only learning matters
Supervised learning
Unsupervised learning
Reinforcement learning
Beyond machine learning - deep learning and bio-inspired adaptive systems
Machine learning and big data
Further reading
Summary
Important Elements in Machine Learning
Data formats
Multiclass strategies
One-vs-all
One-vs-one
Learnability
Underfitting and overfitting
Error measures
PAC learning
Statistical learning approaches
MAP learning
Maximum-likelihood learning
Elements of information theory
References
Summary
Feature Selection and Feature Engineering
scikit-learn toy datasets
Creating training and test sets
Managing categorical data
Managing missing features
Data scaling and normalization
Feature selection and filtering
Principal component analysis
Non-negative matrix factorization
Sparse PCA
Kernel PCA
Atom extraction and dictionary learning
References
Summary
Linear Regression
Linear models
A bidimensional example
Linear regression with scikit-learn and higher dimensionality
Regressor analytic expression
Ridge, Lasso, and ElasticNet
Robust regression with random sample consensus
Polynomial regression
Isotonic regression
References
Summary
Logistic Regression
Linear classification
Logistic regression
Implementation and optimizations
Stochastic gradient descent algorithms
Finding the optimal hyperparameters through grid search
Classification metrics
ROC curve
Summary
Naive Bayes
Bayes theorem
Naive Bayes classifiers
Naive Bayes in scikit-learn
Bernoulli naive Bayes
Multinomial naive Bayes
Gaussian naive Bayes
References
Summary
Support Vector Machines
Linear support vector machines
scikit-learn implementation
Linear classification
Kernel-based classification
Radial Basis Function
Polynomial kernel
Sigmoid kernel
Custom kernels
Non-linear examples
Controlled support vector machines
Support vector regression
References
Summary
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
Ensemble learning
Random forests
Feature importance in random forests
AdaBoost
Gradient tree boosting
Voting classifier
References
Summary
Clustering Fundamentals
Clustering basics
K-means
Finding the optimal number of clusters
Optimizing the inertia
Silhouette score
Calinski-Harabasz index
Cluster instability
DBSCAN
Spectral clustering
Evaluation methods based on the ground truth
Homogeneity
Completeness
Adjusted rand index
References
Summary
Hierarchical Clustering
Hierarchical strategies
Agglomerative clustering
Dendrograms
Agglomerative clustering in scikit-learn
Connectivity constraints
References
Summary
Introduction to Recommendation Systems
Naive user-based systems
User-based system implementation 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
Alternating least squares with Apache Spark MLlib
References
Summary
Introduction to 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
A sample text classifier based on the Reuters corpus
References
Summary
Topic Modeling and Sentiment Analysis in NLP
Topic modeling
Latent semantic analysis
Probabilistic latent semantic analysis
Latent Dirichlet Allocation
Sentiment analysis
VADER sentiment analysis with NLTK
References
Summary
A Brief Introduction to Deep Learning and TensorFlow
Deep learning at a glance
Artificial neural networks
Deep architectures
Fully connected layers
Convolutional layers
Dropout layers
Recurrent neural networks
A brief introduction to TensorFlow
Computing gradients
Logistic regression
Classification with a multi-layer perceptron
Image convolution
A quick glimpse inside Keras
References
Summary
Creating a Machine Learning Architecture
Machine learning architectures
Data collection
Normalization
Dimensionality reduction
Data augmentation
Data conversion
Modeling/Grid search/Cross-validation
Visualization
scikit-learn tools for machine learning architectures
Pipelines
Feature unions
References
Summary

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Machine Learning Algorithms