Slides from Chuck Chekuri's presentation at Orange County Advanced Analytics and Big Data Meetup on June 19, 2019 https://www.meetup.com/Orange-County-Advanced-Analytics-Meetup/

1. CHOOSING ML ALGORITHMS
Orange County Advance Analytics Meetup, June 2019
Chuck Chekuri, Director Data Science, SkygenUSA

2. CRITERIA FOR CHOOSING
• PROBLEM AT HAND
• MACHINE LEARNING TYPE
• ALGORITHM TYPE
• AVAILABLE DATA

3. TYPES OF ML PROBLEMS
• FORECASTING
• MEANINGFUL GROUPING
• INTERPRETATION
• AUTOMATION

4. PROBLEM AT HAND

5. MACHINE LEARNING TYPE

6. TYPES OF ALGORITHMS

7. TYPES
 TEXT
 NUMERIC
 CATEGORICAL
 DESCRETE
 CONTINUOUS
 IMAGE
 SOUND
 NUMERIC
 TIME SERIES
CHARECTERISTICS
 LOCATION ( CLOUD/DB/FILE)
 STATIC VS STREAMING
 HISTORICAL VS REAL TIME
 FREQUENCY OF CHANGE
 MACHINE GENERATED
 TRANSACTIONAL
 DEIDENTIFIED
 SUMMARIZED
QUALITY
 ACCURACY
 MISSING
 COMPLETENESS
 SYSTEMIC ERRORS
 SAMPLE SIZE
 CONFIDENCE LEVEL
 BOOSTABLITY
 CONVERTIBLITY
AVAILABLE DATA

8. NEED FOR AN ALGORITHM

9. REGRESSION CLUSTERING
IDEA OF A DISTANCE

10. CHOOSING AN ML TYPE

11. RECOMMENDER SYSTEMS
VIEWERS & SHOWS
Straight forward relationship between
viewers and shows with number of
episodes watched.
USERS
Count the number of common episodes
between viewers.
More the number, greater the similarity
between users.
SHOWS
Count the number of common
viewership between shows.
More the number, greater the similarity
between shows.
The more similar two viewers are, the more likely they will watch similar shows.

12. CHEAT SHEETS
https://download.microsoft.com/download/A/6/1/A613E11E-8F9C-424A-B99D-65344785C288/microsoft-machine-learning-algorithm-cheat-sheet-v7.pdf

13. SCIKIT-LEARN CHEAT SHEET
https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html