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Machine Learning with Scikit-Learn - The Cancer Dataset - 4 - Process Outline
Machine Learning with Scikit-Learn - The Cancer Dataset - 4 - Process Outline
Published: 2017/03/14
Channel: Cristi Vlad
Practical Machine Learning in Ruby
Practical Machine Learning in Ruby
Published: 2016/12/14
Channel: edutechional
1. Hubness-aware machine learning: outline, nearest neighbor classification
1. Hubness-aware machine learning: outline, nearest neighbor classification
Published: 2015/07/17
Channel: Krisztian Buza
Classifying Handwritten Digits with TF.Learn - Machine Learning Recipes #7
Classifying Handwritten Digits with TF.Learn - Machine Learning Recipes #7
Published: 2016/08/16
Channel: Google Developers
What is Machine Learning?
What is Machine Learning?
Published: 2017/08/24
Channel: Google Cloud
Machine Learning course- Shai Ben-David: Lecture 1
Machine Learning course- Shai Ben-David: Lecture 1
Published: 2015/01/20
Channel: Understanding Machine Learning - Shai Ben-David
ISIT 2015 Tutorial: Information Theory Meets Machine Learning (1/3)
ISIT 2015 Tutorial: Information Theory Meets Machine Learning (1/3)
Published: 2017/05/08
Channel: IEEE Information Theory Society
Shawn Scully: Production and Beyond: Deploying and Managing Machine Learning Models
Shawn Scully: Production and Beyond: Deploying and Managing Machine Learning Models
Published: 2015/12/04
Channel: PyData
Tutorial: Climate Change: Challenges for Machine Learning
Tutorial: Climate Change: Challenges for Machine Learning
Published: 2016/08/18
Channel: Microsoft Research
Ian Goodfellow- Machine Learning Privacy and Security AIWTB 2017
Ian Goodfellow- Machine Learning Privacy and Security AIWTB 2017
Published: 2017/05/17
Channel: With The Best
How Machine Learning Can Cut Insurance Premiums
How Machine Learning Can Cut Insurance Premiums
Published: 2016/09/07
Channel: Raj Ramesh
Machine Learning Course - Lecture 9
Machine Learning Course - Lecture 9
Published: 2016/08/12
Channel: Microsoft Research
Machine Learning for Robotics
Machine Learning for Robotics
Published: 2012/11/02
Channel: VideoLecturesChannel
Frameworks for Distributed Machine Learning
Frameworks for Distributed Machine Learning
Published: 2016/07/08
Channel: Microsoft Research
Using Machine Learning Classifiers to Define Personas
Using Machine Learning Classifiers to Define Personas
Published: 2016/09/12
Channel: Team Coria
Embracing a Taxonomy of Types to Simplify Machine Learning - Leah McGuire
Embracing a Taxonomy of Types to Simplify Machine Learning - Leah McGuire
Published: 2017/06/14
Channel: Databricks
Sparse Methods for Machine Learning: Theory and Algorithms
Sparse Methods for Machine Learning: Theory and Algorithms
Published: 2012/12/11
Channel: VideoLecturesChannel
Lecture 10 - Neural Networks
Lecture 10 - Neural Networks
Published: 2012/05/06
Channel: caltech
Samy Bengio: A note of caution about training sequence prediction/generation models
Samy Bengio: A note of caution about training sequence prediction/generation models
Published: 2016/06/29
Channel: artwithMI
Machine Learning and Machine Translation
Machine Learning and Machine Translation
Published: 2008/10/24
Channel: GoogleTechTalks
AWS re:Invent 2016: Fraud Detection with Amazon Machine Learning on AWS (FIN301)
AWS re:Invent 2016: Fraud Detection with Amazon Machine Learning on AWS (FIN301)
Published: 2016/11/30
Channel: Amazon Web Services
Deep Learning - Jürgen Schmidhuber - #1
Deep Learning - Jürgen Schmidhuber - #1
Published: 2014/04/14
Channel: Zürich Machine Learning and Data Science Meetup
Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50
Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50
Published: 2017/04/20
Channel: BharatiDWConsultancy
[PURDUE MLSS] Machine Learning for Discovery in Legal Cases by David D. Lewis
[PURDUE MLSS] Machine Learning for Discovery in Legal Cases by David D. Lewis
Published: 2011/11/30
Channel: Purdue University
Machine Learning for  Computer Graphics An brief introduction
Machine Learning for Computer Graphics An brief introduction
Published: 2016/05/31
Channel: castors2007
Lecture 02 - Is Learning Feasible?
Lecture 02 - Is Learning Feasible?
Published: 2012/04/09
Channel: caltech
Let’s Write a Decision Tree Classifier from Scratch: Machine Learning Recipes #8
Let’s Write a Decision Tree Classifier from Scratch: Machine Learning Recipes #8
Published: 2017/09/13
Channel: Google Developers
Andreas Mueller: Machine Learning with scikit learn
Andreas Mueller: Machine Learning with scikit learn
Published: 2015/12/04
Channel: PyData
IETF 93 - Thursday Lunch Presentation - Recent Advances in Machine Learning
IETF 93 - Thursday Lunch Presentation - Recent Advances in Machine Learning
Published: 2015/07/23
Channel: IETF - Internet Engineering Task Force
Writing Our First Classifier - Machine Learning Recipes #5
Writing Our First Classifier - Machine Learning Recipes #5
Published: 2016/06/08
Channel: Google Developers
Diagnosis of Brain Tumor using ML Algorithm
Diagnosis of Brain Tumor using ML Algorithm
Published: 2017/06/15
Channel: Zarrar Shaikh
Keynote Applying Recent Advances in Machine Learning to Networking- Dave Meyer
Keynote Applying Recent Advances in Machine Learning to Networking- Dave Meyer
Published: 2016/10/20
Channel: OpenDaylight Project
Human-centric machine learning.
Human-centric machine learning.
Published: 2016/06/22
Channel: Microsoft Research
DSN 2014 Keynote: "Sibyl: A System for Large Scale Machine Learning at Google"
DSN 2014 Keynote: "Sibyl: A System for Large Scale Machine Learning at Google"
Published: 2014/06/27
Channel: Tech Talk
(Info 1.2) Entropy - Definition (continued)
(Info 1.2) Entropy - Definition (continued)
Published: 2011/03/25
Channel: mathematicalmonk
Continuous Optimization of MIcroservices using Machine Learning by Ramki Ramakrishna
Continuous Optimization of MIcroservices using Machine Learning by Ramki Ramakrishna
Published: 2017/04/19
Channel: Devoxx
Lecture 01 - The Learning Problem
Lecture 01 - The Learning Problem
Published: 2012/08/28
Channel: caltech
Machine Learning with Scikit-Learn - The Cancer Dataset - 16 - Random Forests 1
Machine Learning with Scikit-Learn - The Cancer Dataset - 16 - Random Forests 1
Published: 2017/04/28
Channel: Cristi Vlad
Processing moCap data for Machine Learning with Fabric Engine (part 1)
Processing moCap data for Machine Learning with Fabric Engine (part 1)
Published: 2016/11/09
Channel: Gustavo Eggert Boehs
Genetic Algorithms 3/30: Outline of the Basic Genetic Algorithm
Genetic Algorithms 3/30: Outline of the Basic Genetic Algorithm
Published: 2015/03/08
Channel: Noureddin Sadawi
#بتاع_كله سيطرة الحوسبة - Machine Learning - كورسات ببلاش
#بتاع_كله سيطرة الحوسبة - Machine Learning - كورسات ببلاش
Published: 2016/07/22
Channel: Mostafa Saady مصطفى سعدي
Scale R to Big Data Using Hadoop and Spark
Scale R to Big Data Using Hadoop and Spark
Published: 2016/06/17
Channel: Data Science Dojo
Processing moCap data for Machine Learning with Fabric Engine (part 2)
Processing moCap data for Machine Learning with Fabric Engine (part 2)
Published: 2016/11/09
Channel: Gustavo Eggert Boehs
Machine Learning Classification kNN in R (Breast Cancer)
Machine Learning Classification kNN in R (Breast Cancer)
Published: 2017/04/26
Channel: Data Scientist PakinJa
Reactions from the 1st eBay Machine Learning and Data Science Conference
Reactions from the 1st eBay Machine Learning and Data Science Conference
Published: 2016/03/14
Channel: eBay
Mutual Information - Georgia Tech - Machine Learning
Mutual Information - Georgia Tech - Machine Learning
Published: 2015/02/23
Channel: Udacity
Introduction - Georgia Tech - Machine Learning
Introduction - Georgia Tech - Machine Learning
Published: 2015/02/23
Channel: Udacity
Lecture 14 - Support Vector Machines
Lecture 14 - Support Vector Machines
Published: 2012/05/18
Channel: caltech
Maximal Variance and Information Loss - Intro to Machine Learning
Maximal Variance and Information Loss - Intro to Machine Learning
Published: 2015/02/23
Channel: Udacity
Introduction to Machine Learning for Quantitative Finance - Webinar Teaser (15 June 2017)
Introduction to Machine Learning for Quantitative Finance - Webinar Teaser (15 June 2017)
Published: 2017/06/08
Channel: QuantInsti Quantitative Learning
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WIKIPEDIA ARTICLE

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The following outline is provided as an overview of and topical guide to machine learning:

Machine learning – subfield of computer science[1] (more particularly soft computing) that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed".[2] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.[3] Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.

What type of thing is machine learning?[edit]

Branches of machine learning[edit]

Subfields[edit]

Cross-disciplinary fields[edit]

Machine learning hardware[edit]

Machine learning tools[edit]

Proprietary frameworks[edit]

Open source frameworks[edit]

Machine learning libraries[edit]

Machine learning methods[edit]

Supervised learning[edit]

Artificial neural network[edit]

Bayesian[edit]

Decision tree[edit]

Linear classifier[edit]

Unsupervised learning[edit]

Artificial neural network[edit]

Association rule learning[edit]

Hierarchical clustering[edit]

Cluster analysis[edit]

Anomaly detection[edit]

Semi-supervised learning[edit]

Reinforcement learning[edit]

Deep learning[edit]

Others[edit]

Applications of machine learning[edit]

Machine learning problems and tasks[edit]

Machine learning research[edit]

History of machine learning[edit]

Machine learning projects[edit]

Machine learning organizations[edit]

Machine learning venues[edit]

Machine learning conferences and workshops[edit]

Machine learning journals[edit]

Persons influential in machine learning[edit]

See also[edit]

Further reading[edit]

References[edit]

  1. ^ a b http://www.britannica.com/EBchecked/topic/1116194/machine-learning  This tertiary source reuses information from other sources but does not name them.
  2. ^ Phil Simon (March 18, 2013). Too Big to Ignore: The Business Case for Big Data. Wiley. p. 89. ISBN 978-1-118-63817-0. 
  3. ^ Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning. 30: 271–274. 
  4. ^ http://www.learningtheory.org/

External links[edit]

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