Machine learning systems have been around since the 1950s. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed.

1. An Introduction
Machine Learning
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2. Agenda
• Why Machine Learning?
• What is Machine Learning?
• Supervised Learning
• Unsupervised Learning
• Interesting Developments
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3. Why Machine Learning?
1. Machine learning systems have been around since the 1950s, so why are we
suddenly seeing breakthroughs in so many diverse areas?
a. Main Factors
i. Enormously increased data
ii. Improved algorithms
iii. Powerful computer hardware
b. Consider some of the instances where machine learning is applied:
i. The self-driving Google car
ii. Online recommendation engines like friend suggestions on
Facebook, product suggestion in Amazon, movie suggestion in
Netflix
2. All these examples echo the vital role machine learning in our day-to-day life.
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4. What is Machine Learning?
Machine learning is a core sub-area of artificial intelligence; it enables computers to
get into a mode of self-learning without being explicitly programmed.
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Predictions
Input Data

6. Two kinds of Machine Learning
• Supervised Learning
• Unsupervised Learning
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7. Supervised Learning
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Supervised algorithms use labeled data in which both the input and target outcome, or label, are provided to the
algorithm. Supervised learning is also called predictive modeling or predictive analytics because you build a model
that is capable of making predictions
Bedrooms
(X1)
Input
Sq. feet
(X2)
Input
Sale price
(Y)
Output
3 2000 $250,000
2 800 $150,000
Housing Price (Labeled Data)

8. Supervised Learning - Example
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Let’s say you are a real estate agent. You have house selling data for 3 months.
Click Here

9. Other Examples
• Given tumor historical data and identify tumor is malignant or not (Classification)
– Feature of tumor : Age of patient,Size of tumor,Status (Malignant or Benign)
– Prediction : Malignant or Benign
• Given historical car insurance fraudulent claims (Logistic Regression)
– Features of the claims: Age of the claimant, Claimed amount, Severity of the
accident
– Prediction : Fraud Probability
• Given historical real estate sales prices (Linear Regression)
– Features of houses: Square feet, Number of bedrooms, Location, Price
– Prediction : House Price
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10. Unsupervised Learning
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Unsupervised learning is where you only have input data (X) and no corresponding output variables.

11. Unsupervised Learning - Example
Customer Pattern
• Neighborhood near the local college really like small houses with lots of bedrooms.
• Home buyers in the suburbs prefer 3-bedroom houses with lots of square footage.
Knowing about different kinds of customers could help direct your marketing efforts.
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The real world is marketing data provider Acxiom’s life stage clustering system,
Personicx. This service segments U.S. households into 70 distinct clusters within 21
life stage groups that are used by advertisers when targeting Facebook ads, display
ads, direct mail campaigns, etc. Click Here
Other Example

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14. Interesting Developments
● In 2015, Google trained a conversational agent (AI) for their tech support
helpdesk
● DeepMind’s AlphaGo defeated one of the best human players at Go
● Point your camera at the menu written in non-english language and the
restaurant’s selections will magically appear in English via the Google Translate
app.
● IBM Watson for Oncology helps physicians quickly identify key information in a
patient’s medical record, surface relevant articles and explore treatment options.
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15. Reference Links
1. Simple Machine Learning Model in Python in 5 lines of code Click Here
2. Machine Learning Resources Click Here
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16. Questions?
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17. Thank You
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