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 categoriesBooks under this sub-category (35 books)
An Introduction to Statistical Learning with Applications in R
Post date: 08 Apr 2016An 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.
An Introduction to Statistical Learning with Applications in R
Post date: 08 Apr 2016An 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.
![[Early Access Version] Model-Based Machine Learning](http://mail.freetechbooks.com/uploads/1481788280-mblm-crrs.jpg)
[Early Access Version] Model-Based Machine Learning
Post date: 15 Dec 2016This 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
Document Type: Book
[Early Access Version] Model-Based Machine Learning
Post date: 15 Dec 2016This 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
Document Type: Book
![[No longer publicly accessible] Learning Deep Architectures for AI](http://mail.freetechbooks.com/uploads/1480471974-ftml_book_rs.jpg)
[No longer publicly accessible] Learning Deep Architectures for AI
Post date: 30 Nov 2016This 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
Document Type: Paper
[No longer publicly accessible] Learning Deep Architectures for AI
Post date: 30 Nov 2016This 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
Document Type: Paper
![[Sign-up required] Building Machine Learning Systems with Python](http://mail.freetechbooks.com/uploads/1488793189-9781782161400-rs.jpg)
[Sign-up required] Building Machine Learning Systems with Python
Post date: 06 Mar 2017This book provides you with an accessible route into Python machine learning, featuring a wealth of real-world examples.
[Sign-up required] Building Machine Learning Systems with Python
Post date: 06 Mar 2017This book provides you with an accessible route into Python machine learning, featuring a wealth of real-world examples.
![[Sign-up required] Practical Data Analysis - Transform, model, and visualize your data through hands-on projects, developed in open source tools](http://mail.freetechbooks.com/uploads/1493802708-9781783280995-PRACTICAL_DATA_ANALYSIS_rs.jpg)
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.
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.
Post date: 03 May 2017A 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.
A Brief Introduction to Machine Learning for Engineers
Post date: 29 Nov 2020This 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.
A Brief Introduction to Machine Learning for Engineers
Post date: 29 Nov 2020This 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.
Post date: 03 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.).
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
Post date: 03 Apr 2016A 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 2016A simple, intuitive introduction into the concepts of machine learning.
Publication date: 21 Apr 2010
Document Type: Book
Document Type: Book
A First Encounter with Machine Learning
Post date: 14 Apr 2016A simple, intuitive introduction into the concepts of machine learning.
Publication date: 21 Apr 2010
Document Type: Book
Document Type: Book
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.
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
Post date: 29 Jun 2016Lecture 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
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.
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.
Post date: 04 Oct 2016Introduces 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.
Bayesian Reasoning and Machine Learning
Post date: 14 Apr 2016This 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.
Bayesian Reasoning and Machine Learning
Post date: 14 Apr 2016This 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.
Computer Science & Information Technology
Post date: 17 Apr 2017Proceeding 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
Publication date: 01 Apr 2017
License: Creative Commons Attribution 4.0 International
Document Type: Proceeding
Computer Science & Information Technology
Post date: 17 Apr 2017Proceeding 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
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 2016This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.
Data-Intensive Text Processing with MapReduce
Post date: 13 Apr 2016This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.
Post date: 30 Mar 2016
The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano.
The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano.
Publication date: 29 Mar 2016
Post date: 30 Mar 2016The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano.
Publication date: 29 Mar 2016
Post date: 29 Sep 2016
Provides the background needed for a modern theoretical course in computer science.
Provides the background needed for a modern theoretical course in computer science.
Publication date: 11 Apr 2014
Document Type: Textbook
Document Type: Textbook
Post date: 29 Sep 2016Provides the background needed for a modern theoretical course in computer science.
Publication date: 11 Apr 2014
Document Type: Textbook
Document Type: Textbook
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