Download - Machine Learning - What, Where and How
Machine LearningWhat, Where and How
Narinder Kumar ([email protected])
Mercris Technologies (www.mercris.com)
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Agenda
Definition
Types of Machine Learning
Under-the Hood
Languages & Libraries
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What is Machine Learning ?
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Definition Field of Study that gives Computers the ability
to learn without being explicitly programmed --Arthur Samuel
A more Mathematical one
A Computer program is said to learn from Experience E with respect to some Task T and Performance measure P, if it's Performance at Task in T, as measured by P, improves with Experience E –Tom M. Mitchell
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Related Disciplines
Sub-Field of Artificial Intelligence
Deals with Design and Development of Algorithms
Closely related to Data Mining
Uses techniques from Statistics, Probability Theory
and Pattern Recognition
Not new but growing fast because of Big Data
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Types of Machine Learning Supervised Machine Learning
Provide right set of answers for different set of questions
Underlying algorithm learns/infers over a period of time
Tries to return correct answers for similar questions
Unsupervised Machine Learning
Provide data & Let underlying algorithm find some structure
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Popular Use Cases
Recommendation Systems
Amazon, Netflix, iTunes Genius, IMDb...
Up-Selling & Churn Analysis
Customer Sentiment Analysis
Market Segmentation
...
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Understanding Regression
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Problem Contest
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Typical Machine Learning Algorithm
Training Set
Learning Algorithm
HypothesisInput FeaturesInput
FeaturesExpectedOutput
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Let's Simplify a bit
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House Sizes vs Prices
House Sizes (Sq Yards)
Pric
es (
1000
US
D)
➢ Goal is to draw a Straight line which covers our Data-Set reasonably
➢ Our Hypothesis can be
➢ Such that hΘ x=Θ0+Θ1 xhθ( x)=θ0+θ1 x
hθ( x)≃ y
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In Mathematical Terms➢ Hypothesis
➢ Parameters
➢ Cost Function
➢ We would like to minimize
hθ( x)=θ0+θ1 x
θ0 ,θ1
J (θ0 ,θ1)
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Solution : Gradient Descent➢ Start with an initial
values of
➢ Keep Changing until we end up at minimum
θ0 ,θ1
θ0 ,θ1
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Mathematically
Repeat Until Convergence
For Our Scenario
Generic Formula
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Let's see all this in Action
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Extending Regression➢ Quadratic Model
➢ Cubic Model
➢ Square Root Model
➢ We can create multiple new Features like
X 2=X2 X 3=X
3 X 4=√ X
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Additional Pointers
➢ Mean Normalization
➢ Feature Scaling
➢ Learning Rate
➢ Gradient Descent vs Others
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HOW-TOLanguages & Libraries
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Languages
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Libraries, Tools and Products
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A Short Introduction
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What is WEKA ? Developed by Machine Learning Group,
University of Waikato, New Zealand Collection of Machine Learning Algorithms Contains tools for
Data Pre-Processing Classification & Regression Clustering Visualization
Can be embedded inside your application Implemented in Java
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Main Components
Explorer
Experimenter
Knowledge Flow
CLI
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Terminology Training DataSet == Instances Each Row in DataSet == Instance Instance is Collection of Attributes (Features) Types of Attributes
Nominal (True, False, Malignant, Benign, Cloudy...)
Real values (6, 2.34, 0...) String (“Interesting”, “Really like it”, “Hate
It” ...) ...
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Sample DataSets@RELATION house
@ATTRIBUTE houseSize real@ATTRIBUTE lotSize real@ATTRIBUTE bedrooms real@ATTRIBUTE granite real@ATTRIBUTE bathroom real@ATTRIBUTE sellingPrice real
@DATA3529,9191,6,0,0,205000 3247,10061,5,1,1,224900 4032,10150,5,0,1,197900 2397,14156,4,1,0,189900 2200,9600,4,0,1,195000 3536,19994,6,1,1,325000 2983,9365,5,0,1,230000
@RELATION CPU
@attribute outlook {sunny, overcast, rainy}
@attribute temperature real@attribute humidity real@attribute windy {TRUE, FALSE}@attribute play {yes, no}
@datasunny,85,85,FALSE,nosunny,80,90,TRUE,noovercast,83,86,FALSE,yesrainy,70,96,FALSE,yesrainy,68,80,FALSE,yesrainy,65,70,TRUE,noovercast,64,65,TRUE,yes
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WEKA Demo
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Apache Mahout➢ Collection of Machine Learning Algorithms➢ Map-Reduce Enabled (most cases)➢ DataSources
➢ Database➢ File-System➢ Lucene Integration
➢ Very Active Community➢ Apache License
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WEKA vs Apache Mahout
WEKA➢ Lot of Algorithms➢ Tools for
➢ Modeling➢ Comparison➢ Data-Flow
➢ May need work for running on large data-sets
➢ License Issues
Apache-Mahout➢ Lesser number of
Algorithms but growing
➢ Lack of tools for Modeling
➢ Ready by Design for Large Scale
➢ Vibrant Community➢ Apache License
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&
An Overview
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Google Prediction API 101
➢ Cloud Based Web Service for Machine Learning
➢ Exposed as REST API
➢ Does not require any Machine Learning
knowledge
➢ Capabilities
➢ Categorical &
➢ Regression
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Working with Google Prediction API
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Let's see in Action
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Analysis
Very Promising Concept
Can be powerful tool for SME's
Not configurable
Data Security
Not Yet Production Ready (IMHO)
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Recap
➢ Very vast
➢ Huge demand
➢ Has an Initial Steep Learning Curve
➢ Several libraries available
➢ Lot of Innovative work going on currently
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Resources➢ Online Machine Learning Course - Prof. Andrew
Ng, Stanford University ➢ WEKA Wiki and API docs➢ Apache Mahout Wiki➢ IBM Developer Works Articles➢ Google Prediction API Web Site➢ Data Mining : Practical Machine Learning Tools &
Techniques – Ian H. Witten, Eibe Frank, Mark Hall➢ Machine Learning Forums
