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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
Class room Machine Learning course
Class room Machine Learning course
Published: 2017/05/08
Channel: Gate Lectures by Ravindrababu Ravula
Practical Machine Learning in Ruby
Practical Machine Learning in Ruby
Published: 2016/12/14
Channel: edutechional
Tutorial: Climate Change: Challenges for Machine Learning
Tutorial: Climate Change: Challenges for Machine Learning
Published: 2016/08/18
Channel: Microsoft Research
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
Writing Our First Classifier - Machine Learning Recipes #5
Writing Our First Classifier - Machine Learning Recipes #5
Published: 2016/06/08
Channel: Google Developers
Curse of Dimensionality - Georgia Tech - Machine Learning
Curse of Dimensionality - Georgia Tech - Machine Learning
Published: 2015/02/23
Channel: Udacity
Machine Learning with TensorFlow for Business Intelligence : TensorFlow outline
Machine Learning with TensorFlow for Business Intelligence : TensorFlow outline
Published: 2017/12/29
Channel: Smart Media
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
Machine Learning and AI for the Sciences - Towards Understanding
Machine Learning and AI for the Sciences - Towards Understanding
Published: 2017/11/30
Channel: Center for Brains, Minds and Machines (CBMM)
What is Machine Learning?
What is Machine Learning?
Published: 2017/08/24
Channel: Google Cloud
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
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
Data Science: Supervised Machine Learning in Python : Introduction and Outline
Data Science: Supervised Machine Learning in Python : Introduction and Outline
Published: 2017/12/26
Channel: Blue Gator
Cancer Test - Intro to Machine Learning
Cancer Test - Intro to Machine Learning
Published: 2015/02/23
Channel: Udacity
Ensemble Machine Learning in Python: Random Forest, AdaBoost : Outline and Motivation
Ensemble Machine Learning in Python: Random Forest, AdaBoost : Outline and Motivation
Published: 2018/01/02
Channel: Videos 2018
Machine Learning and Machine Translation
Machine Learning and Machine Translation
Published: 2008/10/24
Channel: GoogleTechTalks
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
How Machine Learning Can Cut Insurance Premiums
How Machine Learning Can Cut Insurance Premiums
Published: 2016/09/07
Channel: Raj Ramesh
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
Frameworks for Distributed Machine Learning
Frameworks for Distributed Machine Learning
Published: 2016/07/08
Channel: Microsoft Research
Machine Learning with Scikit-Learn - The Cancer Dataset - 2
Machine Learning with Scikit-Learn - The Cancer Dataset - 2
Published: 2017/03/10
Channel: Cristi Vlad
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
Machine Learning for Robotics
Machine Learning for Robotics
Published: 2012/11/02
Channel: VideoLecturesChannel
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
Machine Learning with Scikit-Learn - The Cancer Dataset - 33 - Predict Proba
Machine Learning with Scikit-Learn - The Cancer Dataset - 33 - Predict Proba
Published: 2017/07/20
Channel: Cristi Vlad
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
Andreas Mueller: Machine Learning with scikit learn
Andreas Mueller: Machine Learning with scikit learn
Published: 2015/12/04
Channel: PyData
Representation - Georgia Tech - Machine Learning
Representation - Georgia Tech - Machine Learning
Published: 2015/02/23
Channel: Udacity
Using Convolutional Neural Networks to Automatically Analyze Aerial and Satellite Imagery
Using Convolutional Neural Networks to Automatically Analyze Aerial and Satellite Imagery
Published: 2017/09/06
Channel: azavea
Sparse Methods for Machine Learning: Theory and Algorithms
Sparse Methods for Machine Learning: Theory and Algorithms
Published: 2012/12/11
Channel: VideoLecturesChannel
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
Machine Learning with Scikit-Learn - The Cancer Dataset - 25 - SVMs 1
Machine Learning with Scikit-Learn - The Cancer Dataset - 25 - SVMs 1
Published: 2017/06/04
Channel: Cristi Vlad
Machine Learning with Scikit-Learn - The Cancer Dataset - 6 - KNN 2
Machine Learning with Scikit-Learn - The Cancer Dataset - 6 - KNN 2
Published: 2017/03/20
Channel: Cristi Vlad
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
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
Machine Learning Course - Lecture 9
Machine Learning Course - Lecture 9
Published: 2016/08/12
Channel: Microsoft Research
Using Machine Learning Classifiers to Define Personas
Using Machine Learning Classifiers to Define Personas
Published: 2016/09/12
Channel: Team Coria
Machine Learning with Scikit-Learn - The Cancer Dataset - 3
Machine Learning with Scikit-Learn - The Cancer Dataset - 3
Published: 2017/03/12
Channel: Cristi Vlad
Machine learning - Motivational video
Machine learning - Motivational video
Published: 2018/01/05
Channel: AK49
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
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
Lecture 02 - Is Learning Feasible?
Lecture 02 - Is Learning Feasible?
Published: 2012/04/09
Channel: caltech
Rebecca Bilbro - Yellowbrick: Steering Machine Learning with Visual Transformers
Rebecca Bilbro - Yellowbrick: Steering Machine Learning with Visual Transformers
Published: 2017/05/07
Channel: PyData
Detecting Order Fraud For 500K Merchants: Machine Learning At Scale - Nevena Francetic
Detecting Order Fraud For 500K Merchants: Machine Learning At Scale - Nevena Francetic
Published: 2017/11/22
Channel: Hyperight AB
Tom Bocklisch - Conversational AI: Building clever chatbots
Tom Bocklisch - Conversational AI: Building clever chatbots
Published: 2017/07/26
Channel: PyData
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
Machine Learning with Scikit-Learn - The Cancer Dataset - 1
Machine Learning with Scikit-Learn - The Cancer Dataset - 1
Published: 2017/03/07
Channel: Cristi Vlad
Scikit Learn Linear SVC Example Machine Learning Tutorial with Python p. 11
Scikit Learn Linear SVC Example Machine Learning Tutorial with Python p. 11
Published: 2015/01/02
Channel: sentdex
Webinar: Self Service Machine Learning
Webinar: Self Service Machine Learning
Published: 2017/10/10
Channel: Sisense
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WIKIPEDIA ARTICLE

From Wikipedia, the free encyclopedia
  (Redirected from List of machine learning concepts)
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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 of machine learning[edit]

Subfields of machine learning

Cross-disciplinary fields involving machine learning[edit]

Cross-disciplinary fields involving machine learning

Applications of machine learning[edit]

Applications of machine learning

Machine learning hardware[edit]

Machine learning hardware

Machine learning tools[edit]

Machine learning tools   (list)

Machine learning frameworks[edit]

Machine learning framework

Proprietary machine learning frameworks[edit]

Proprietary machine learning frameworks

Open source machine learning frameworks[edit]

Open source machine learning frameworks

Machine learning libraries[edit]

Machine learning library   (list)

Machine learning algorithms[edit]

Machine learning algorithm

Types of machine learning algorithms[edit]

Machine learning methods[edit]

Machine learning method   (list)

Dimensionality reduction[edit]

Dimensionality reduction

Ensemble learning[edit]

Ensemble learning

Meta learning[edit]

Meta learning

Reinforcement learning[edit]

Reinforcement learning

Supervised learning[edit]

Supervised learning

Bayesian[edit]

Bayesian statistics

Decision tree algorithms[edit]

Decision tree algorithm

Linear classifier[edit]

Linear classifier

Unsupervised learning[edit]

Unsupervised learning

Artificial neural networks[edit]

Artificial neural network

Association rule learning[edit]

Association rule learning

Hierarchical clustering[edit]

Hierarchical clustering

Cluster analysis[edit]

Cluster analysis

Anomaly detection[edit]

Anomaly detection

Semi-supervised learning[edit]

Semi-supervised learning

Deep learning[edit]

Deep learning

Other machine learning methods and problems[edit]

Machine learning research[edit]

Machine learning research

History of machine learning[edit]

History of machine learning

Machine learning projects[edit]

Machine learning projects

Machine learning organizations[edit]

Machine learning organizations

Machine learning conferences and workshops[edit]

Machine learning publications[edit]

Books on machine learning[edit]

Books about machine learning

Machine learning journals[edit]

Persons influential in machine learning[edit]

See also[edit]

Other[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/
  5. ^ Settles, Burr (2010), "Active Learning Literature Survey" (PDF), Computer Sciences Technical Report 1648. University of Wisconsin–Madison, retrieved 2014-11-18 
  6. ^ Rubens, Neil; Elahi, Mehdi; Sugiyama, Masashi; Kaplan, Dain (2016). "Active Learning in Recommender Systems". In Ricci, Francesco; Rokach, Lior; Shapira, Bracha. Recommender Systems Handbook (2 ed.). Springer US. doi:10.1007/978-1-4899-7637-6. ISBN 978-1-4899-7637-6. 

External links[edit]

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