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Mastering Machine Learning Algorithms Expert Techniques For Implementing Popular Machine Learning Algorithms Finetuning Your Models And Understanding How They Work 2nd Edition Giuseppe Bonaccorso

  • SKU: BELL-11734604
Mastering Machine Learning Algorithms Expert Techniques For Implementing Popular Machine Learning Algorithms Finetuning Your Models And Understanding How They Work 2nd Edition Giuseppe Bonaccorso
$ 31.00 $ 45.00 (-31%)

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Mastering Machine Learning Algorithms Expert Techniques For Implementing Popular Machine Learning Algorithms Finetuning Your Models And Understanding How They Work 2nd Edition Giuseppe Bonaccorso instant download after payment.

Publisher: Packt
File Extension: PDF
File size: 34.97 MB
Pages: 798
Author: Giuseppe Bonaccorso
ISBN: 9781838820299, 9781838821913, 1838820299, 1838821910
Language: English
Year: 2020
Edition: 2nd edition

Product desciption

Mastering Machine Learning Algorithms Expert Techniques For Implementing Popular Machine Learning Algorithms Finetuning Your Models And Understanding How They Work 2nd Edition Giuseppe Bonaccorso by Giuseppe Bonaccorso 9781838820299, 9781838821913, 1838820299, 1838821910 instant download after payment.

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.

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