Machine learning algorithms : popular algorithms for data science and machine learning

Machine learning algorithms popular algorithms for data science and machine learning

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and...

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Detalles Bibliográficos
Otros Autores: Bonaccorso, Giuseppe, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham ; Mumbai : Packt Publishing 2018.
Edición:Second edition
Materias:
Machine learning.
Artificial intelligence.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630731406719
Tabla de Contenidos:
  • 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
  • Other Books You May Enjoy
  • Index.

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