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

Author: Giuseppe Bonaccorso
Publisher: Birmingham : Packt Publishing Ltd, 2018.
Edition/Format:   eBook : Document : English : 2nd edView all editions and formats
Summary:
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.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Bonaccorso, Giuseppe.
Machine Learning Algorithms : Popular Algorithms for Data Science and Machine Learning, 2nd Edition.
Birmingham : Packt Publishing Ltd, ©2018
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Giuseppe Bonaccorso
ISBN: 9781789345483 1789345480
OCLC Number: 1051137944
Notes: Introducing semi-supervised Support Vector Machines (S3VM).
Description: 1 online resource (514 pages)
Contents: 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-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. 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. 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.

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  Read more...

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