Product Management Event at #ProductCon Seattle on AI and ML for Product Management by Nitin Bhat, Senior Director of Product Management at Smartsheet.

1. www.productschool.com
AI and ML for Product Management
by Smartsheet Sr Dir of PM

2. Join 35,000+Product
Managers on
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prdct.school/PSJobPortalprdct.school/events-slack

3. C O U R S E S
Product
Management
Learn the skills you need to land
a Product Manager job

4. C O U R S E S
Coding
for Managers
Build a website and gain the
technical knowledge to lead
software engineers

5. C O U R S E S
Data Analytics
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Learn the skills to understand web
analytics, SQL and machine learning
concepts

6. C O U R S E S
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Digital Marketing
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7. C O U R S E S
UX Design
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users and deliver an exceptional end-
to-end experience

8. C O U R S E S
For experienced Product Managers
looking to gain strategic skills needed
for top leadership roles
Product
Leadership

9. C O U R S E S
Corporate
Training
Level up your team’s Product
Management skills

10. Nitin Bhat
T O N I G H T ’ S S P E A K E R

11. Nitin T Bhat
Artificial Intelligence and Machine Learning for
Product Managers
nibhat
S o u r
c e

12. Why learn it?
Mark Cuban- Upfront Summit 2017

13. Everyday use cases
1. Your iPhone classifying people and pictures on your photos app. That is classification!
2. Listing of properties into neighborhoods on AirBnB for easier navigation. That is clustering!
3. Finding answers to your queries through your search engine. That is ranking!
4. Finding the next best movie to watch on your favorite streaming site. That is
recommendation!
5. Finding the price estimate for the house you are looking on Zillow. That is regression!
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14. WHAT IS AI?
Human intelligence exhibited by machines.

15. How is all this connected?
Source
AI- Human intelligence exhibited by machines
Examples- Robotics, NLP, neural networks
ML- Branch of AI
Looks at pattern recognition, algorithms learn from
data and environment

16. AI- Common Use cases
Computer vision-
Object recognition
Speech recognition
(hello Alexa, Siri,
Cortana!)
Sentiment Analysis
(NLP)
Language/Machine
translation
Image
transformation
Time series(stock
forecast)
Graph analysis
(Movie reco)
Image source
Soteria- YC Startup School
2018

17. Text classification-Powered by NLTK APIs(nltk.org)
1. First determine text
is neutral or not
using hierarchical
classification.
1. Classify the text
first into positive,
negative (sentiment
polarity).

18. Computer vision
Detect image content (Age, index, gender, ethnicity, smile)

19. What is
Machine
Learning?
• Machine learning (ML) is a type of
artificial intelligence (AI)
• Learn (from data, patterns, etc.) without
specifically programming it so
• Generic algorithm, but specific learnings
(based on the data/problem)

20. What does
machine
learning
do?
• Then uses those patterns to
predict the future
Finds patterns in data
• Detecting credit card fraud
• Determining whether a customer
is likely to switch to a competitor
Examples:

21. When do
you use
machine
learning?
• Looking at hundreds
and millions of items
to predict something.
Email is spam or not.
When you
cannot
scale
• Detecting credit card
fraud- multiple inputs
When you
cannot
quantify all
the rules in
code

22. What can it help you solve?
A or B
Classification Algorithm
• Is this email spam or not?
• Will customer buy this
product or not?
A or B or C
Multiclass classification
• Is this product a book, movie,
or clothing?

23. What can it help you
solve?
• How much – or – How many?
• Regression classification algorithm
• Makes numerical prediction
• What is the temperature next Tuesday?
• What price will this house sell for?
Image courtesy- Microsoft/towardsdatascience

24. What can it help you
solve?
• Is it different/weird?
• Anomaly detection- Flags
unexpected or unusual events
or behaviors.
• Credit fraud flagging
https://medium.com/datadriveninvestor/detecting-anomalies-using-machine-learning-e3495f79718

25. What can it help you
solve?
• How is this organized?
• Clustering algorithm
• How is this organized?
• Separates data into separate clumps
• Example
• Which viewers like the same types of
movies?
Image courtesy- Microsoft/towardsdatascience

26. What can it help you
solve?
• What should I do next?
• Reinforcement learning
• These algorithms learn from outcomes,
and decide on the next action.
mage courtesy- Microsoft
• If I'm a self-driving car: At a yellow light, brake or
accelerate?
• For a robot vacuum: Keep vacuuming, or go back to
the charging station?

27. EXAMPLES

28. Name
$2,600.45
$2,294.58
$1,003.30
$33.32
Amount Fraudulent
Jordan
Harry
Potter
Ramya
No
Yes
Yes
What’s the pattern
for fraudulent
transactions?
Fraud Detection
Yes

29. Demo

30. NLP capabilities vs use cases
• Sentiment analysis
• Text classification
• Speech generation
• Entity
recognition/extraction
• Text classification
• Chatbots
• Reviewing resumes
• Classifying support issues
• Classifying sale
potential/sale lead
• Building abuse or fraud
detection

31. What is
Data
Science?
Extraction of information
Uses numbers and names (also known as
categories or labels) to predict answers to
questions.
Combination of math, computer science and
information tech
Overlaps with statistics, analysis

33. Source

34. www.productschool.com
Part-time Product Management, Coding, Data Analytics, Digital
Marketing, UX Design, Product Leadership courses and
Corporate Training