Slides to my on product meetup talk.

1. How To Get Started With
Machine Learning
11th October 2017
Alexander Schaefer, Project A

2. What is ML
Computation
Data
Program
Results
Traditional programming

3. What is ML
Computation
Data
Program
Results
Traditional programming

4. What is ML
Computation
Data
Program
{Cat or Dog}
Traditional programming
Data
Program

5. What is ML
Computation
Data
Program
Traditional programming
{Cat or Dog}
Data
Program

6. What is ML
Computation
Dog
Program
Computation
Data
Program
Traditional programming
ML Approach
{Cat or Dog}
Data
Program
Data
Result

7. What is ML
Computation
Data
Cat
Program
Computation
Data
Program
Traditional programming
ML Approach
{Cat or Dog}
Data
Result
Data
Program

8. What is ML
▪ “Machine learning systems automatically learn
programs from data” Domingos, 2012
Computation
Data
Cat
Program
ML Approach
Data
Result

9. What is ML
▪ “Machine learning systems automatically learn
programs from data” Domingos, 2012

10. What is ML
▪ “Machine learning systems automatically learn
programs from data” Domingos, 2012
▪ Coursera

11. What is ML
▪ “Machine learning systems automatically learn
programs from data” Domingos, 2012
▪ Coursera
▪ Kaggle

12. Your Problem
▪ Some examples from Kaggle:
▪ Which driver will file insurance in next 12 months?
kaggle.com

13. Your Problem
▪ Some examples from Kaggle:
▪ Which driver will file insurance in next 12 months?
▪ Which content will be clicked by a user?
kaggle.com

14. Your Problem
▪ Some examples from Kaggle:
▪ Which driver will file insurance in next 12 months?
▪ Which content will be clicked by a user?
▪ Which article will be bought by a customer in the next 12 months?
kaggle.com

15. Data
▪ Data is key

16. Data
Computation
Dog
Program
ML Approach
Data
Result

17. ▪ Data is key
▪ More data is better
Data

18. ▪ Data is key
▪ More data is better
Data
Banko & Brill, 2001
Halevy et al., 2009

19. ▪ Data is key
▪ More data is better
Data
deeplearning.ai

20. Data
▪ Data is key
▪ More data is better
▪ Better data is better

21. Data
▪ Data is key
▪ More data is better
▪ Better data is better
2009 2012

22. Data
▪ Data is key
▪ More data is better
▪ Better data is better
2005

23. Models and Evaluation
Computation
Dog
Program
ML Approach
Data
Result

24. Models and Evaluation
1. Come up with a baseline
▪ Naive approach
▪ Something you can do without ML
(mean, median)

25. Models and Evaluation
1. Come up with a baseline
▪ Naive approach
▪ Something you can do without ML
(mean, median)
2. Start with a simple model
▪ “Draw the line, yes this counts as
machine learning”
▪ Try to be better than the naive
approach

26. Models and Evaluation
1. Come up with a baseline
▪ Naive approach
▪ Something you can do without ML
(mean, median)
2. Start with a simple model
▪ “Draw the line, yes this counts as
machine learning”
▪ Try to be better than the naive
approach
3. Build a more complex model
▪ Beat the simple model
▪ Iterate

27. Models and Evaluation
1. Come up with a baseline
▪ Naive approach
▪ Something you can do without ML
(mean, median)
2. Start with a simple model
▪ “Draw the line, yes this counts as
machine learning”
▪ Try to be better than the naive
approach
3. Build a more complex model
▪ Beat the simple model
▪ Iterate
spotify.com

28. Models and Evaluation
1. Come up with a baseline
▪ Naive approach
▪ Something you can do without ML
(mean, median)
2. Start with a simple model
▪ “Draw the line, yes this counts as
machine learning”
▪ Try to be better than the naive
approach
3. Build a more complex model
▪ Beat the simple model
▪ Iterate
▪ Ideally stop when effort > gain
spotify.com

29. Models and Evaluation
Computation
Data
Dog
Program
ML Approach
Data
Result

30. Models and Evaluation
Computation
Data
Dog
Program
Computation
ML Approach
unseen
Data
Results
on unseen data
Data
Result

31. Models and Evaluation
Program
Computation
Results
on unseen data
▪ Generalization

32. QA and Validation

33. QA and Validation

34. QA and Validation
Program
Computation
Results
on unseen data

35. QA and Validation
Program
Computation
Results
on unseen data

36. Summary - How to get started with ML

37. Summary - How to get started with ML
▪ Get the data
▪ Simple model
▪ Evaluate

38. Thank you!

39. Contact
https://alexandschaefer.github.io/
https://twitter.com/alexandschaefer

40. Appendix