Machine Learning

Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.

All categories

Books under this sub-category (39 books)

An Introduction to Statistical Learning with Applications in R

Post date: 07 Apr 2016
An introduction to statistical learning methods, this book contains a number of R labs with detailed explanations on how to implement the various methods in real life settings.
Publisher: Springer-Verlag GmbH
Publication date: 24 Jun 2014
 
 An Introduction to Statistical Learning with Applications in R

An Introduction to Statistical Learning with Applications in R

Post date: 07 Apr 2016
An introduction to statistical learning methods, this book contains a number of R labs with detailed explanations on how to implement the various methods in real life settings.
Publisher: Springer-Verlag GmbH
Publication date: 24 Jun 2014


[Early Access Version] Model-Based Machine Learning

Post date: 15 Dec 2016
This book looks at machine learning from a perspective called model-based machine learning. This viewpoint will guide you towards building successful machine learning solutions without requiring that you master the huge literature on machine learning.
Publication date: 01 Jan 2016
Document Type: Book
 
[Early Access Version] Model-Based Machine Learning

[Early Access Version] Model-Based Machine Learning

Post date: 15 Dec 2016
This book looks at machine learning from a perspective called model-based machine learning. This viewpoint will guide you towards building successful machine learning solutions without requiring that you master the huge literature on machine learning.
Publication date: 01 Jan 2016
Document Type: Book


[No longer publicly accessible] Learning Deep Architectures for AI

Post date: 30 Nov 2016
This monograph discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models.
Publication date: 31 Dec 2009
Document Type: Paper
 
[No longer publicly accessible] Learning Deep Architectures for AI

[No longer publicly accessible] Learning Deep Architectures for AI

Post date: 30 Nov 2016
This monograph discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models.
Publication date: 31 Dec 2009
Document Type: Paper


[Sign-up required] Building Machine Learning Systems with Python

Post date: 06 Mar 2017
This book provides you with an accessible route into Python machine learning, featuring a wealth of real-world examples.
Publisher: Packt Publishing
Publication date: 26 Jul 2013
Document Type: Book
 
[Sign-up required] Building Machine Learning Systems with Python

[Sign-up required] Building Machine Learning Systems with Python

Post date: 06 Mar 2017
This book provides you with an accessible route into Python machine learning, featuring a wealth of real-world examples.
Publisher: Packt Publishing
Publication date: 26 Jul 2013
Document Type: Book


[Sign-up required] Practical Data Analysis - Transform, model, and visualize your data through hands-on projects, developed in open source tools

Post date: 03 May 2017
A hands-on guide to understanding the nature of your data and turn it into insight. Presents a detailed exploration of the current work in data analysis through self-contained projects.
Publisher: Packt Publishing
Publication date: 22 Oct 2013
Document Type: Book
 
[Sign-up required] Practical Data Analysis - Transform, model, and visualize your data through hands-on projects, developed in open source tools

[Sign-up required] Practical Data Analysis - Transform, model, and visualize your data through hands-on projects, developed in open source tools

Post date: 03 May 2017
A hands-on guide to understanding the nature of your data and turn it into insight. Presents a detailed exploration of the current work in data analysis through self-contained projects.
Publisher: Packt Publishing
Publication date: 22 Oct 2013
Document Type: Book


A Brief Introduction to Machine Learning for Engineers

Post date: 29 Nov 2020
This monograph aims at providing an introduction to key concepts, algorithms, and theoretical results in machine learning. The treatment concentrates on probabilistic models for supervised and unsupervised learning problems.
Publisher: Now Publishers
Publication date: 17 May 2018
Document Type: Book
 
A Brief Introduction to Machine Learning for Engineers

A Brief Introduction to Machine Learning for Engineers

Post date: 29 Nov 2020
This monograph aims at providing an introduction to key concepts, algorithms, and theoretical results in machine learning. The treatment concentrates on probabilistic models for supervised and unsupervised learning problems.
Publisher: Now Publishers
Publication date: 17 May 2018
Document Type: Book


A Course in Machine Learning

Post date: 02 Apr 2016
A set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).
Publication date: 01 Sep 2015
 
A Course in Machine Learning

A Course in Machine Learning

Post date: 02 Apr 2016
A set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).
Publication date: 01 Sep 2015


A First Encounter with Machine Learning

Post date: 14 Apr 2016
A simple, intuitive introduction into the concepts of machine learning.
Publication date: 21 Apr 2010
Document Type: Book
 
A First Encounter with Machine Learning

A First Encounter with Machine Learning

Post date: 14 Apr 2016
A simple, intuitive introduction into the concepts of machine learning.
Publication date: 21 Apr 2010
Document Type: Book


Algorithmic Aspects of Machine Learning

Post date: 24 Feb 2021
This is a monograph based on the class "Algorithmic Aspects of Machine Learning" taught at MIT in Fall 2013, Spring 2015 and Fall 2017.
Publication date: 01 Jan 2018
Document Type: Lecture Notes
 
Algorithmic Aspects of Machine Learning

Algorithmic Aspects of Machine Learning

Post date: 24 Feb 2021
This is a monograph based on the class "Algorithmic Aspects of Machine Learning" taught at MIT in Fall 2013, Spring 2015 and Fall 2017.
Publication date: 01 Jan 2018
Document Type: Lecture Notes


Applied Data Science

Post date: 29 Jun 2016
Lecture notes for Applied Data Science course at Columbia University. It focuses more on the statistics edge, while also teaching readers some basic programming skill.
Publication date: 31 Dec 2012
 
Applied Data Science

Applied Data Science

Post date: 29 Jun 2016
Lecture notes for Applied Data Science course at Columbia University. It focuses more on the statistics edge, while also teaching readers some basic programming skill.
Publication date: 31 Dec 2012


Azure Machine Learning

Post date: 04 Oct 2016
Introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then deploy those models for consumption as cloud web services.
Publisher: Pearson Education
Publication date: 25 Apr 2015
Document Type: Book
 
Azure Machine Learning

Azure Machine Learning

Post date: 04 Oct 2016
Introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then deploy those models for consumption as cloud web services.
Publisher: Pearson Education
Publication date: 25 Apr 2015
Document Type: Book


Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

Post date: 08 Apr 2016
An introduction to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view, using Python.
Publisher: Addison-Wesley Professional
Publication date: 12 Oct 2015
 
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

Post date: 08 Apr 2016
An introduction to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view, using Python.
Publisher: Addison-Wesley Professional
Publication date: 12 Oct 2015


Bayesian Reasoning and Machine Learning

Post date: 14 Apr 2016
This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided.
Publisher: Cambridge University Press
Publication date: 18 Nov 2015
 
Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning

Post date: 14 Apr 2016
This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided.
Publisher: Cambridge University Press
Publication date: 18 Nov 2015


Computer Science & Information Technology

Post date: 17 Apr 2017
Proceeding from the Fourth International Conference on Computer Science and Information Technology (CoSIT 2017) Geneva, Switzerland, March 25-26, 2017.
Publisher: AIRCC Publishing Corporation
Publication date: 01 Apr 2017
License: Creative Commons Attribution 4.0 International
Document Type: Proceeding
 
Computer Science & Information Technology

Computer Science & Information Technology

Post date: 17 Apr 2017
Proceeding from the Fourth International Conference on Computer Science and Information Technology (CoSIT 2017) Geneva, Switzerland, March 25-26, 2017.
Publisher: AIRCC Publishing Corporation
Publication date: 01 Apr 2017
License: Creative Commons Attribution 4.0 International Document Type: Proceeding


Data-Intensive Text Processing with MapReduce

Post date: 13 Apr 2016
This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.
Publisher: Morgan & Claypool Publishers
Publication date: 30 Apr 2010
 
Data-Intensive Text Processing with MapReduce

Data-Intensive Text Processing with MapReduce

Post date: 13 Apr 2016
This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.
Publisher: Morgan & Claypool Publishers
Publication date: 30 Apr 2010


Book Categories
Sponsors