With all the hype around ML/AI everyone is looking at it. There is a widespread perception that you need to know Maths before you can do Machine learning. In this session we share why that is not true.

1. Why you don't need Maths
to get benefits of ML
- Aseem Bansal

2. Speaker
● Over 4 experience web development JVM
● Dabbling in ML for over 4 years
● Working with ML in production for over 2 years
https://medium.com/@asmbansal2
https://www.linkedin.com/in/bansalaseem
https://twitter.com/AseemBansal2

3. Current Perspective
● Only for people who have done a degree in Data science/Machine learning
● Only for people who know Maths

4. Historical reasons for the perspective
● When companies like Google, Baidu wanted to do ML they did not have the
required talent

5. Historical reasons for the perspective
● When companies like Google, Baidu wanted to do ML they did not have the
required talent
● They turned to universities which had the talent

6. Historical reasons for the perspective
● When companies like Google, Baidu wanted to do ML they did not have the
required talent
● They turned to universities which had the talent
● From universities they got professors who had the required skills

7. Historical reasons for the perspective
● When companies like Google, Baidu wanted to do ML they did not have the
required talent
● They turned to universities which had the talent
● From universities they got professors who had the required skills
● Academia is Theory and Maths centric

8. Historical reasons for the perspective
● When companies like Google, Baidu wanted to do ML they did not have the
required talent
● They turned to universities which had the talent
● From universities they got professors who had the required skills
● Academia is Theory and Maths centric
● That theory and Maths centric approach spread from there to everywhere

9. What are the problems with the perspective
● Academia has always been theory and Maths centric for everything
● Industry has always been about implementations

10. What are the problems with the perspective
● Web developers don’t write Tomcat or Nginx from scratch to understand the
basics of how to make a web application
● Cricketers don’t try to derive the equation of Parabola before they go for a
match

11. What are the problems with the perspective
● Doing Practical stuff does not always require Maths
● Doing Practical stuff does not always require theory
● To do practical stuff you understand things till the depth which is required and
just do it
● Don't have to be expert of underlying. just know how to use it
● There are people who have done the lower level stuff already

12. 3 levels of Machine learning as I see it
● Composing Big Black Boxes to
build Solutions
● Cloud providers have
production ready APIs available
● No ML/Maths expertise required

13. 3 levels of Machine learning as I see it
● Using a wider variety of small
boxes to build solutions
● There are production-ready
open source libraries available
which abstract away the Maths
● At this point you would need
understanding of ML concepts
and ML libraries

14. 3 levels of Machine learning as I see it
● Build custom boxes to build
your solutions
● In a lot of cases you won’t need
to go to completely custom
solutions
● When you do need to build
custom solutions then you
would need understanding of
Maths

15. Workflow of machine learning

16. Workflow of machine learning

17. Workflow of machine learning

18. Workflow of machine learning

19. Workflow of machine learning

20. What is a model

21. Using Pre-built Solutions
If you are doing any of the following you should try out the cloud providers before
you try to roll out your own
● Natural Language Processing
● Text to Text Translation
● Speech to Text
● Image Analysis
● Video Analysis

22. Using Pre-built Solutions
● Maths may not be required but real data is required

23. Using Pre-built Solutions
● It is Garbage-in Garbage-
out problem. Feed it bad
data you will get bad
Machine learning models
which will be useless

24. Using Pre-built Solutions
● If dealing with unstructured data
like Images, videos etc. GPUs
(aka Graphics Card) are required
which are not cheap.

25. Demo: Use libraries to build your own solutions
Going to use
● Python - programming language
● Jupyter - Browser based code editing and execution environment
● Sklearn - Machine learning library
● Matplotlib - charting library

26. Workflow of machine learning

27. Workflow of machine learning

28. Workflow of machine learning

29. Workflow of machine learning

30. Workflow of machine learning

31. Demo: Use libraries to build your own solutions
Going to use
● Python - programming language
● Jupyter - Browser based code editing and execution environment
● Sklearn - Machine learning library

32. What we did not answer
● Did not show you how to choose a algorithm
● Did not teach you the libraries
● Installation of Python/jupyter/libraries
● ML concepts
But a simple google search can you with those
● Search “Anaconda 3 download” and you will find a good page
● Search “sklearn tutorial” and go to the first tutorial that comes up
● Check out fast ai’s Machine learning course in their forums - quite practical

33. Don’t have to be a Maths Phd
to get benefits of Machine
learning