SlideShare a Scribd company logo

AI and ML for Product Management by Smartsheet Sr Dir of PM

•Download as PPTX, PDF•
•
Product School
Product School

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

1 of 34
www.productschool.com AI and ML for Product Management by Smartsheet Sr Dir of PM
Join 35,000+Product Managers on Free Resources Discover great job opportunities Job Portal prdct.school/PSJobPortalprdct.school/events-slack
C O U R S E S Product Management Learn the skills you need to land a Product Manager job
C O U R S E S Coding for Managers Build a website and gain the technical knowledge to lead software engineers
C O U R S E S Data Analytics for Managers Learn the skills to understand web analytics, SQL and machine learning concepts
C O U R S E S Learn how to acquire more users and convert them into clients Digital Marketing for Managers
C O U R S E S UX Design for Managers Gain a deeper understanding of your users and deliver an exceptional end- to-end experience
C O U R S E S For experienced Product Managers looking to gain strategic skills needed for top leadership roles Product Leadership
C O U R S E S Corporate Training Level up your team’s Product Management skills
Nitin Bhat T O N I G H T ’ S S P E A K E R
Nitin T Bhat Artificial Intelligence and Machine Learning for Product Managers nibhat S o u r c e
Why learn it? Mark Cuban- Upfront Summit 2017
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! Brands and logos are owned by individual companies.
WHAT IS AI? Human intelligence exhibited by machines.
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
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
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).
Computer vision Detect image content (Age, index, gender, ethnicity, smile)
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)
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:
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
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?
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
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
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
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?
EXAMPLES
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
Demo
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
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
AI and ML for Product Management by Smartsheet Sr Dir of PM
Source
www.productschool.com Part-time Product Management, Coding, Data Analytics, Digital Marketing, UX Design, Product Leadership courses and Corporate Training

More Related Content

AI and ML for Product Management by Smartsheet Sr Dir of PM

  • 1. www.productschool.com AI and ML for Product Management by Smartsheet Sr Dir of PM
  • 2. Join 35,000+Product Managers on Free Resources Discover great job opportunities Job Portal 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 for Managers Learn the skills to understand web analytics, SQL and machine learning concepts
  • 6. C O U R S E S Learn how to acquire more users and convert them into clients Digital Marketing for Managers
  • 7. C O U R S E S UX Design for Managers Gain a deeper understanding of your 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! Brands and logos are owned by individual companies.
  • 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?
  • 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
  • 34. www.productschool.com Part-time Product Management, Coding, Data Analytics, Digital Marketing, UX Design, Product Leadership courses and Corporate Training