1. Machine Learning
What, Where and How
Narinder Kumar
(nkumar@mercris.com)
Mercris Technologies
(www.mercris.com)

2. Agenda

Definition

Types of Machine Learning

Under-the Hood

Languages & Libraries
2

3. What is Machine Learning ?
3

4. 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 4

5. 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
5

6. 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 6

7. Popular Use Cases

Recommendation Systems

Amazon, Netflix, iTunes Genius, IMDb...

Up-Selling & Churn Analysis

Customer Sentiment Analysis

Market Segmentation

...
7

8. Understanding Regression
8

9. Problem Contest
9

10. Typical Machine Learning Algorithm
Training Set
Learning
Algorithm
Input Expected
Hypothesis Output
Features
10

11. Let's Simplify a bit
➢
Goal is to draw a
4000
House Sizes vs Prices Straight line which
3500
covers our Data-Set
3000 reasonably
2500 ➢
Our Hypothesis can be
Prices (1000 USD)
2000
1500
hθ ( x)=θ0+θ1 x
hΘthat 0+Θ1(xx)≃ y
x=Θ h
1000
Such
500
➢
0
θ
50 100 150 200 250 300 350 400
House Sizes (Sq Yards)
11

12. In Mathematical Terms
➢
Hypothesis hθ ( x)=θ0+θ1 x
➢
Parameters θ0 ,θ1
➢
Cost Function
➢
We would like to minimize J (θ0 ,θ1 )
12

13. Solution : Gradient Descent
➢
Start with an initial
values of θ0 , θ1
➢
Keep Changing θ0 , θ1
until we end up at
minimum
13

14. Mathematically
Repeat Until Convergence
For Our Scenario
Generic Formula
14

15. Let's see all this in Action
15

16. Extending Regression
➢
Quadratic Model
➢
Cubic Model
➢
Square Root Model
➢
We can create multiple new Features like
X 2=X 2
X 3=X 3
X 4= √ X
16

17. Additional Pointers
➢
Mean Normalization
➢
Feature Scaling
➢
Learning Rate
➢
Gradient Descent vs Others
17

18. HOW-TO
Languages & Libraries
18

19. Languages
19

20. Libraries, Tools and Products
20

21. A Short Introduction
21

22. 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
22

23. Main Components

Explorer

Experimenter

Knowledge Flow

CLI
23

24. 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” ...)

...
24

25. Sample DataSets
@RELATION house @RELATION CPU
@ATTRIBUTE houseSize real @attribute outlook {sunny, overcast,
@ATTRIBUTE lotSize real rainy}
@ATTRIBUTE bedrooms real @attribute temperature real
@ATTRIBUTE granite real @attribute humidity real
@ATTRIBUTE bathroom real @attribute windy {TRUE, FALSE}
@ATTRIBUTE sellingPrice real @attribute play {yes, no}
@DATA @data
3529,9191,6,0,0,205000 sunny,85,85,FALSE,no
3247,10061,5,1,1,224900 sunny,80,90,TRUE,no
4032,10150,5,0,1,197900 overcast,83,86,FALSE,yes
2397,14156,4,1,0,189900 rainy,70,96,FALSE,yes
2200,9600,4,0,1,195000 rainy,68,80,FALSE,yes
3536,19994,6,1,1,325000 rainy,65,70,TRUE,no
2983,9365,5,0,1,230000 overcast,64,65,TRUE,yes
25

26. WEKA Demo
26

27. 27

28. Apache Mahout
➢
Collection of Machine Learning Algorithms
➢
Map-Reduce Enabled (most cases)
➢
DataSources
➢
Database
➢
File-System
➢
Lucene Integration
➢
Very Active Community
➢
Apache License
28

29. WEKA vs Apache Mahout
WEKA Apache-Mahout
➢
Lot of Algorithms ➢
Lesser number of
➢
Tools for Algorithms but
➢
Modeling growing
➢
Comparison ➢
Lack of tools for
➢
Data-Flow Modeling
➢
May need work for ➢
Ready by Design for
running on large data- Large Scale
sets ➢
Vibrant Community
➢
License Issues ➢
Apache License
29

30. &
An Overview
30

31. 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
31

32. Working with Google Prediction API
32

33. Let's see in Action
33

34. Analysis
Very Promising Concept
Can be powerful tool for SME's
Not configurable
Data Security
Not Yet Production Ready (IMHO)
34

35. Recap
➢
Very vast
➢
Huge demand
➢
Has an Initial Steep Learning Curve
➢
Several libraries available
➢
Lot of Innovative work going on currently
35

36. nkumar@mercris.com
@kumar_narinder
www.mercris.com
http://mercris.wordpress.com
36

37. 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
37