Machine Learning Algorithms: Popular Algorithms for Data Science and Machine Learning, 2nd Edition.

Bonaccorso  Giuseppe.

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dc.contributor.author	Bonaccorso Giuseppe.
dc.date.accessioned	2024-01-26T21:37:44Z
dc.date.available	2024-01-26T21:37:44Z
dc.date.issued	2018
dc.identifier.citation	Bonaccorso. Machine Learning Algorithms: Popular Algorithms for Data Science and Machine Learning, 2nd Edition.: 2nd ed. - Birmingham: Packt Publishing Ltd, 2018 - 1 online resource (514 pages) - URL: https://libweb.kpfu.ru/ebsco/pdf/1881497.pdf
dc.identifier.isbn	9781789345483
dc.identifier.isbn	1789345480
dc.identifier.uri	https://dspace.kpfu.ru/xmlui/handle/net/178497
dc.description	Introducing semi-supervised Support Vector Machines (S3VM).
dc.description.abstract	Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. This book will act as an entry point for anyone who wants to make a career in Machine Learning. It covers algorithms like Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, and Feature engineering.
dc.description.tableofcontents	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.
dc.description.tableofcontents	One-vs-allOne-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.
dc.description.tableofcontents	Data scaling and normalizationWhitening; 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.
dc.description.tableofcontents	Huber regressionBayesian 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.
dc.language	English
dc.language.iso	en
dc.publisher	Birmingham Packt Publishing Ltd
dc.subject.other	Computers -- Intelligence (AI) & Semantics.
dc.subject.other	Computers -- Data Modeling & Design.
dc.subject.other	Database design & theory.
dc.subject.other	Artificial intelligence.
dc.subject.other	Machine learning.
dc.subject.other	Information architecture.
dc.subject.other	Computers -- Machine Theory.
dc.subject.other	Mathematical theory of computation.
dc.subject.other	Machine learning.
dc.subject.other	Computer algorithms.
dc.subject.other	Computer algorithms.
dc.subject.other	Machine learning.
dc.subject.other	Electronic books.
dc.title	Machine Learning Algorithms: Popular Algorithms for Data Science and Machine Learning, 2nd Edition.
dc.type	Book
dc.description.pages	1 online resource (514 pages)
dc.collection	Электронно-библиотечные системы
dc.source.id	EN05CEBSCO05C1259