2. Contents
2
What is Machine Learning?
Machine Learning Applications
Pre-requisites for ML
Learning ML
ML Algorithm Categorization
Supervised, Un-supervised & Semi-supervised Algorithms
Parametric & Non-Parametric Algorithms
The ML Framework
Machine Learning Tools
R Vs Python Vs SAS
Reference & Resources
8. “Machine learning is the next Internet”
(Tony Tether, Director, DARPA)
“Machine learning is the hot new thing”
(John Hennessy, President, Stanford)
“Web rankings today are mostly a matter of machine
learning” (Prabhakar Raghavan, Dir. Research, Yahoo)
“Machine learning is today’s discontinuity”
(Jerry Yang, CEO, Yahoo)
Quotes
10. 10
Machine Learning is the
Study of algorithms that
Improve their performance
At some task
With experience
Machine learning is the science of getting computers to act
without being explicitly programmed – Andrew Ng
Machine Learning is the training of a model from data that
generalizes a decision against a performance measure – Jason in
his blog
What is Machine Learning?
11. 11
The field of machine learning is concerned with the question of
how to construct computer programs that automatically improve
with experience - Tom Mitchell in his book Machine Learning
Vast amounts of data are being generated in many fields, and the
statisticians’s job is to make sense of it all: to extract important
patterns and trends, and to understand “what the data says”. We
call this learning from data - Hastie, Tibshirani & Friedman
Other Definitions
14. 14
Machine Learning Applications
Human Resource management
Internal mobility (Matching of employees and job descriptions)
External recruitment (Browsing candidates through social platforms
like linkedIn)
Skills management: mapping all company skills (including their
relationships e.g. one programming language being close to another
one)
15. 15
Machine Learning Applications
Automating employee access control by Amazon
To develop a computer algorithm that will predict which employees
should be granted access to what resources.
To identify whales in the ocean based on audio recordings by Cornell
University
so that ships can avoid hitting them
To determine which bird species is/are on a given audio recording
collected in field conditions by Oregon State University
Identifying heart failure by IBM researchers
A way to extract heart failure diagnosis criteria from free-text
physician notes.
16. 16
Machine Learning Applications
To predict whether someone is a psychopath based on his twitter usage
To predict in advance whether a product launch will be successful or not.
Stop malware
Self driving cars
Automatic speech recognition, automatic voice/face/finger print
recognition, automatic medical diagnostics and many more
17. Growth of Machine Learning
The rise and shine of machine learning is due to
Improved data capture, networking, faster computers
New sensors / Input Output devices
Demand for self-customization to user, environment
Improved machine learning algorithms
21. 21
Do you really need to be an expert in math?
The real prerequisite for machine learning isn’t math, it’s data
analysis
https://www.r-bloggers.com/the-real-prerequisite-for-machine-
learning-isnt-math-its-data-analysis/
http://courses.washington.edu/css490/2012.Winter/lecture_slide
s/02_math_essentials.pdf
22. 22
Do you really need to be an expert in programming?
You do not have to be an excellent programmer to start your
career in machine learning
http://machinelearningmastery.com/what-if-im-not-a-good-
programmer/
23. 23
Adapt, improvise and conquer
Finish what you start no matter what
Start even when you are not ready
Passion for machine learning
Prerequisites
https://www.quora.com/What-are-prerequisites-to-start-learning-Machine-Learning
33. 33
ML Algorithm Categorization
On the basis of similarity in form/function
Regression
Classification
Clustering
Anomaly
Detection
Recommender
System
Regularization
Tree Based
Algo
ANN
Deep Learning
34. 34
ML Algorithm Categorization
On the basis of similarity in form/function, there are many other
algorithms such as ensemble methods, reinforcement learning,
computer vision, natural language processing (NLP),
dimensionality reduction algorithms, Baysian algorithms, &
Instance based algorithms etc.
Read more: http://www.cs.uvm.edu/~icdm/algorithms/index.shtml
35. 35
ML Algorithm Categorization
On the basis of target function
Parametric
Algorithms
• Algorithms that simplify the
function to a known form
• E.g., Linear Reg, Logistic Reg.
Non-Parametric
Algorithms
• Algorithms that don’t make strong
assumptions about the form of
the function
• E.g., SVM, ANN, Decision Trees
36. 36
Parametric Vs Non-Parametric Algorithms
Parametric
Algorithms
Simple
Speed
Less data
Non-Parametric
Algorithms
Difficult to
interpret
Slower
More data
37. 37
Parametric Vs Non-Parametric Algorithms
Parametric
Algorithms
Constrained
Limited Complexity
Poor fit
Under-fitting
Non-Parametric
Algorithms
Power
Flexible
Performance
Over-fitting
Trade-off between prediction
accuracy & model interpretability
38. 38
Data is like people –
Interrogate it hard enough and it
will tell you whatever you want
to hear
Just like that
40. The machine learning framework
y = f (x)
Training
Given a training set of labeled examples {(x1,y1), …, (xN, yN)},
estimate the prediction function f by minimizing the prediction error on the
training set
Testing
Apply f to a never before seen test example x and output the
predicted value y = f(x)
output
prediction
function
Input
42. The ML framework - Testing
Prediction-
Apple
Image
Features
Test Image
Learned
model
Prediction -
Orange
Image
Features
Test Image
Learned
model
Prediction-
Cherry
Image
Features
Test Image
Learned
model
43. How machine learning algorithms works?
Model Representation
Model Evaluation
Model Improvement
45. ML in Practice
Understanding domain, prior knowledge, and goals
Data integration, selection, cleaning, pre-processing, etc.
Learning models
Interpreting results
Consolidating and deploying discovered knowledge
Loop
46. References
46
Elements of Statistical Learning by Hastie, Tibshirani,
Friedman
Foundations of Machine Learning by Mehryar
Mohri, Afshin Rostamizadeh and Ameet Talwalkar
Pattern Recognition and Machine Learning by
Bishop
Machine Learning with R by Brett Lantz's
Machine Learning for Hackers by Conway & White