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Mastering Machine Learning Algorithms
Mastering Machine Learning Algorithms

Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models

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Mastering Machine Learning Algorithms

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Key benefits

  • Discover high-performing machine learning algorithms and understand how they work in depth
  • One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation
  • Master concepts related to algorithm tuning, parameter optimization, and more

Description

Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn v0.19.1. You will also learn how to use Keras and TensorFlow 1.x to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need.

Who is this book for?

This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.

What you will learn

  • Explore how a ML model can be trained, optimized, and evaluated
  • Understand how to create and learn static and dynamic probabilistic models
  • Successfully cluster high-dimensional data and evaluate model accuracy
  • Discover how artificial neural networks work and how to train, optimize, and validate them
  • Work with Autoencoders and Generative Adversarial Networks
  • Apply label spreading and propagation to large datasets
  • Explore the most important Reinforcement Learning techniques

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 25, 2018
Length: 576 pages
Edition : 1st
Language : English
ISBN-13 : 9781788625906
Category :

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Product Details

Publication date : May 25, 2018
Length: 576 pages
Edition : 1st
Language : English
ISBN-13 : 9781788625906
Category :

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Frequently bought together


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Deep Reinforcement Learning Hands-On
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Machine Learning Algorithms
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Mastering Machine Learning Algorithms
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Total 7,808.97 Stars icon

Table of Contents

16 Chapters
Machine Learning Model Fundamentals Chevron down icon Chevron up icon
Introduction to Semi-Supervised Learning Chevron down icon Chevron up icon
Graph-Based Semi-Supervised Learning Chevron down icon Chevron up icon
Bayesian Networks and Hidden Markov Models Chevron down icon Chevron up icon
EM Algorithm and Applications Chevron down icon Chevron up icon
Hebbian Learning and Self-Organizing Maps Chevron down icon Chevron up icon
Clustering Algorithms Chevron down icon Chevron up icon
Ensemble Learning Chevron down icon Chevron up icon
Neural Networks for Machine Learning Chevron down icon Chevron up icon
Advanced Neural Models Chevron down icon Chevron up icon
Autoencoders Chevron down icon Chevron up icon
Generative Adversarial Networks Chevron down icon Chevron up icon
Deep Belief Networks Chevron down icon Chevron up icon
Introduction to Reinforcement Learning Chevron down icon Chevron up icon
Advanced Policy Estimation Algorithms Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.4
(5 Ratings)
5 star 40%
4 star 20%
3 star 0%
2 star 20%
1 star 20%
Colbert Philippe Jul 16, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent academic book for the Machine Learning practitioner! It's exactly what I was looking for. It focuses of reiterative algorithms for learning
Amazon Verified review Amazon
Gasher Jul 02, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book to learn concepts around Machine Learning and application. Highly suggest for anyone interested in using ML in their work.
Amazon Verified review Amazon
Stefan Hildebrandt Jul 16, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
When ordering the book I wasn't sure what to expect, as multiple colleagues here in Germany told me that the topic is a bit hard to understand, but I have to say it was the direct opposite, moreover this book definitely brought my fundamental understandings on a new level. I like that the author is going into details and is moving more towards math-problems rather than existing algorithms. After reading the book I felt more secure about any tweaks I had to do and generally more thoughtful about the deeper level.Still, only 4 stars as the writing style is a bit to harsh. I think more explanation and some steps would be helpful.But, definitely a recommendation from my side!
Amazon Verified review Amazon
Johannes Feb 13, 2019
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Unfortunately, the printing quality is rather bad. Formulas are blurry and hard to read; it seems something went wrong with certain environments (maybe a problem with LaTex typesetting, who knows). It makes the whole experience of reading the book quite dreadful.
Amazon Verified review Amazon
Kindle user Sep 10, 2018
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
I was hoping to be able to use this as a textbook, but the explanations are incomplete and there are no citations.
Amazon Verified review Amazon
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