Downloaded 33 times
![Where
to
use
it?
• Automa)cally
categoriza)on
• Preparing
recommenda)on
• Analyzing
sen)ment
and
behaviors
• Recognizing
pa]erns
• Grouping
unrecognized
pa]erns
• OCR,
Voice
recogni)on,
Image
recogni)on
• Discovering
likelihood
and
many
more.](https://image.slidesharecdn.com/introductiontomachinelearningv1-140607104000-phpapp02/85/Introduction-to-Machine-Learning-8-320.jpg)
![Few
pointers
• h]p://guidetodatamining.com/
– Very
easy
learning
and
programmer
focused
• Introduc)on
to
Machine
Learning
–
Ethem
Alpaydin
(The
MIT
Press)
• Mahout
in
Ac)on
• Mlbase
documenta)on](https://image.slidesharecdn.com/introductiontomachinelearningv1-140607104000-phpapp02/85/Introduction-to-Machine-Learning-19-320.jpg)
![Learn
by
prac)cing
• Apache
Mahout
-‐
h]ps://mahout.apache.org/
• MLbase
-‐
h]p://www.mlbase.org/
• Easyrec
–
h]p://www.easyrec.org
• Weka
-‐
h]p://www.cs.waikato.ac.nz/ml/weka/](https://image.slidesharecdn.com/introductiontomachinelearningv1-140607104000-phpapp02/85/Introduction-to-Machine-Learning-20-320.jpg)
![You
can
use
in
produc)on
(without
coding)
• h]p://predic)on.io/
-‐
For
Collabora)ve
filtering
based
recommenda)on
engine.
• Google
Predic)on
API
-‐
h]ps://developers.google.com/predic)on/
• Algorithm.io
-‐
h]p://www.algorithms.io/
(Not
sure
about
it)](https://image.slidesharecdn.com/introductiontomachinelearningv1-140607104000-phpapp02/85/Introduction-to-Machine-Learning-21-320.jpg)
Uploaded bynhm taveer hossain khan
Introduction to Machine Learning
This document introduces machine learning. It defines machine learning as giving computers the ability to learn without being explicitly programmed. It discusses supervised and unsupervised learning algorithms like classification, regression, clustering, and recommendation systems. Popular algorithms discussed include naive bayes, decision trees, k-means, and support vector machines. The document encourages learning machine learning through online courses and libraries like Mahout, MLbase and Weka. Commercial machine learning platforms are also mentioned.
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Similar to Introduction to Machine Learning
Introduction to Machine Learning
- 1. Introduc)on to Machine Learning NHM Tanveer Hossain Khan (Hasan)
- 2. About Me • I “work for fun” and mostly work with Ruby. • Love programming and learning. • Skilled on Ruby, Java, PHP, Nodejs and Go. • Love to take challenge • I am working with Tweek.tv (one of the Berlin startups)
- 3. What’s in ? • What is Machine learning ? • GeQng rid of fear • Where to use it ? • Who is using ? • Discussion on few Machine learning algorithms. • Few books and references. • Q/A
- 4. What is Machine Learning ?
- 5. Defini)on ? “Field of study that gives computers the ability to learn without being explicitly programmed” By Arthur Samuel (Collected from wiki)
- 6. What is Machine Learning? 1. Train machine with examples 2. Algorithm stores the trained data into a internal mathema)cal model. 3. Predict new data based on the trained model.
- 7. GeQng rid of fear
- 8. Where to use it? • Automa)cally categoriza)on • Preparing recommenda)on • Analyzing sen)ment and behaviors • Recognizing pa]erns • Grouping unrecognized pa]erns • OCR, Voice recogni)on, Image recogni)on • Discovering likelihood and many more.
- 9. Who is using ? • Facebook (Image tagging, Newsfeed) • Gmail (Spam detec)on, Important email detec)on) • YouTube (Video recommenda)on, What to watch) • Google search (Preparing search result) • Amazon (Sugges)ng similar product) • Many more…
- 10. Let’s introduce ML algorithms
- 11. ML in Ac)on • Supervised learning – Classifica)on – Regression • Unsupervised learning – Clustering • Recommenda)on – Content based – Collabora)ve filtering
- 12. Supervised Learning • Machine doesn’t own any cogni)ve system like human does hence they need human intervened feature extrac)on! • Classifica)on & Regression – Naïve Bayes – Decision Tree • ID3 Algorithm – k-‐NN (k nearest neighbors) – SVM (Support Vector Machine) – Many more…
- 13. Naïve Bayes • Mul) class classifica)on • Base on bayes theorem • Text categoriza)on • Works with small training data
- 14. Support Vector Machine (SVM) • Binary classifica)on • None probabilis)c binary linear classifica)on • Represents examples as points in space • Linear classifier • Text categoriza)on • Uses loss func)on
- 15. ID3 • Decision tree • Predic)ve model • Itera)ve • Uses in Informa)on Retrieval (IR) technologies
- 16. Unsupervised Learning • Clustering – k-‐means – Many more…
- 17. k-‐means • Signal processing • Data mining • Itera)ve • Feature learning • Cluster analysis • Color quan)za)on (Reduce number of dis)nct colors from an image)
- 18. Recommenda)ons • Content based – Natural language processing – Named En)ty Recogni)on – Disambigua)on (VW Golf or Sports Golf) • Collabora)ve Filtering – Using SVM, Naïve bayes – Implicit or explicit feedback – Distance calcula)on & k-‐nn based filtering – User or item based
- 19. Few pointers • h]p://guidetodatamining.com/ – Very easy learning and programmer focused • Introduc)on to Machine Learning – Ethem Alpaydin (The MIT Press) • Mahout in Ac)on • Mlbase documenta)on
- 20. Learn by prac)cing • Apache Mahout -‐ h]ps://mahout.apache.org/ • MLbase -‐ h]p://www.mlbase.org/ • Easyrec – h]p://www.easyrec.org • Weka -‐ h]p://www.cs.waikato.ac.nz/ml/weka/
- 21. You can use in produc)on (without coding) • h]p://predic)on.io/ -‐ For Collabora)ve filtering based recommenda)on engine. • Google Predic)on API -‐ h]ps://developers.google.com/predic)on/ • Algorithm.io -‐ h]p://www.algorithms.io/ (Not sure about it)
- 22. That’s it, Thanks all J Q/A