Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
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Sanger's rule 416


activation function 500

activation function, Multilayer Perceptron (MLP)

about 509

hyperbolic tangent 509, 510

rectifier activation function 510, 511, 512

sigmoid 509, 510

softmax 512


about 456, 457, 458, 459, 460

example, with scikit-learn 468, 469, 470, 471, 473

AdaBoost.M1 456

AdaBoost.R2 465, 466, 467, 468

AdaBoost.SAMME 460, 461

AdaBoost.SAMME.R 462, 463, 464


about 539, 540

using, with TensorFlow/Keras 540


using, with TensorFlow/Keras 538

Adaptive Moment Estimation (Adam)

about 536

in TensorFlow/Keras 537

adjacency matrix 134

adjusted Rand index

about 197, 198

adversarial training

about 635, 636, 637, 638, 639

affinity matrix 134


used, for determining optimal number of components 366, 367

anti-Hebbian 423

approaches, ensemble learning

bagging (bootstrap aggregating) 441