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Introduction to Semi-Supervised Learning

Semi-supervised learning is a machine learning branch that tries to solve problems with both labeled and unlabeled data with an approach that employs concepts belonging to clustering and classification methods. The high availability of unlabeled samples, in contrast with the difficulty of labeling huge datasets correctly, drove many researchers to investigate the best approaches that allow extending the knowledge provided by the labeled samples to a larger unlabeled population without loss of accuracy. In this chapter, we're going to introduce this branch and, in particular, we will discuss:

The semi-supervised scenario
The assumptions needed to efficiently operate in such a scenario
The different approaches to semi-supervised learning
Generative Gaussian mixtures algorithm
Contrastive pessimistic likelihood estimation approach
Semi...

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.

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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Colbert Philippe Jul 16, 2019

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

Gasher Jul 02, 2018

Great book to learn concepts around Machine Learning and application. Highly suggest for anyone interested in using ML in their work.

Stefan Hildebrandt Jul 16, 2018

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!

Johannes Feb 13, 2019

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.

Kindle user Sep 10, 2018

I was hoping to be able to use this as a textbook, but the explanations are incomplete and there are no citations.

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

What do you get with Print?

Mastering Machine Learning Algorithms

Introduction to Semi-Supervised Learning

Semi-supervised scenario

Generative Gaussian mixtures

Contrastive pessimistic likelihood estimation

Semi-supervised Support Vector Machines (S3VM)

Transductive Support Vector Machines (TSVM)

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

What do you get with Print?

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Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

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