Machine Learning 101 Aarthi Srinivasan & Evren Korpeoglu

1. MACHINE LEARNING
Evren Korpeoglu, Data Science
Aarthi Srinivasan, Product Management
/Productschool @ProdSchool /ProductmanagementSV

2. What to Expect?
2
• What is machine learning ?
• Why is it important ?
• How do we use it ?
• Technical Concepts
• Examples

3. What is Machine Learning?
3
1. Science of getting computers to learn or recognize something without being explicitly
programmed – Andrew Ng
• Branch of Artificial Intelligence which is a branch of Computer Science
• Give lots of data to the computer so that it can figure it out
• One of the first examples is the computer checkers program by Arthur Samuel
* - ref: Andew Ng Courses, Big data: A revolution
2. Distinguish big data & machine learning: Big data is the data seed for creating
machine learning forests
• Big data collects information based on our digital exhaust (crumbs we leave in
the digital world) , demographics, preferences, health etc.
• Machine learning will mine this data and model behaviors with interactive
responses based on this data

4. Why do we need this?
4
1. Tons of applications impacting human health, utility and future simplification
Health & Wellness Utilitarian Future
• DNA sampling &
diagnosis
• Health reminders
& prevention
through AI tools
• Correlation studies
• Customizable
tablets
• Real time optimized
path maps
• Search Ranking
• Spam filter on email
• News aggregators
• Shopping
Recommendation
• Facebook face
recognition
• Age recognition
• Voice recognition –
Siri, Alexa
• Driverless cars
• Home decoration

5. Key Terms
5
• A set of data used to predict relationships.
• E.g. A diamond’s size, cut and clarity helps predicts the price. Data and
answers for each sample.
Training Set
• Uses training set to make a prediction.
• E.g. Model predicts diamond prices based on past prices.
Supervised Learning
• Provide data without suggesting anything so computer can identify patterns
or groupings.
• E.g. Customer segmentation, DNA groupings.
Unsupervised Learning
• Each distinct measurable data value you select in the training data set.
• E.g. A diamonds’ size is one of the feature’s for predicting price.
Features/ Variables /
Attributes
• Using the features provided in the training set make a prediction. Fit a curve
using the data provided.
• E.g. Price of diamond = X*Cut + Y*Clarity + Z*Size + features…
Supervised: Regression
• A defined set of categories for placing new data (observations)
• E.g. Presence of absence of cancer; Types of diabetes
Supervised: Classification
• Process of assigning observations into subsets
• E.g. Customer segment creations
Unsupervised: Clustering

6. Learning Steps
6
Collect /
Update User
Data
1
Create /
Update
Training Set
data
2
Create /
Update
algorithm for
training data
Update
Algorithm
Validate
Algorithm
3
Create
predictive
model
4
New real-time
observations
A/B Test &
Launch on
production
5

7. Data Wrangling and Feature Extraction
7
Spam Email
Detection
Title
Sender
Domain
# of
Recipients
Email
content
Country of
Origin
Non-
dictionary
Words
Hyperlinks
Address
Book
Length of
email
• Structured Data (Best)
– RDBMS, columnar data
– Strict Schema
– SQL
• Semi-Structured Data (Better)
– JSON, XML
– Enforce minimum schema
– JSON, XML Parser
• Unstructured Data
– Text, Image, Raw email
– No Schema
– Batch processing
– Regular expressions
– Map Reduce
GARBAGE IN GARBAGE OUT

8. Model Training
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Feature
Extraction
(Feature
vector)
New
Text documents
User Activity
Images
Transaction history
Feature
Extraction
(Feature
vector)
Labels
Machine
Learning
Algorithm
Training / Testing
Text documents
User Activity
Images
Transaction history
Predictive
Model
Expected
Label
Model
Evaluation

9. Supervised learning techniques
9
• Linear classifier (numerical functions)
• Parametric (Probabilistic functions)
– Naïve Bayes, Hidden Markov models (HMM), Probabilistic
graphical models
• Non-parametric (Instance-based functions)
– K-nearest neighbors
• Non-metric (Symbolic functions)
– Classification and regression tree (CART)
• Aggregation
– Bagging (bootstrap + aggregation), Adaboost, Random
forest, Ensemble models

10. Linear Classifiers
10
• Logistic regression
– )
– w with minimum loss
– Solve iteratively using gradient descent
• Support vector machine (SVM)
– Maximum margin classifier
• Artificial Neural Networks
– Inspired from how neurons work
– Activation function (Sigmoid, ReLU etc.)
– Deep Learning

11. KNN / CART
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• K-Nearest Neighbors
– Find K nearest training examples
– Majority vote
– Easy to implement
– Not scalable for real time predictions
• Classification and Regression Trees
– Easy to interpret for small trees
• Random Forests
– Ensemble of decision trees
– Usually performs very good

12. Unsupervised Learning
12
• Clustering
– K-means clustering
– Spectral clustering
• Dimensionality reduction
– Principal component analysis (PCA)
– Factor analysis
• Product Recommendations
– Collaborative Filtering
• Association Rules
– Market Basket Analysis

13. Model Evaluation
13
• Measure model performance
• Optimize model to improve prediction
quality
– Feature selection
– Hyperparameter tuning
• A/B Testing
• Explore/Exploit
• http://en.wikipedia.org/wiki/Precision_and_recall

14. Sample Architecture
14
-HADOOP
- SPARK
PREDICTION ENGINE
REAL TIME
DATA
SQL / NO SQL
Data Base
CLIENT MACHINE LEARNING
SYSTEM

15. Health & Wellness Sen.se Mother (iOT)
15

16. Amazon Echo & Personalization
16

17. Houzz Visual Match Deep Learning
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