This document provides an overview of machine learning including definitions of supervised learning, unsupervised learning, and reinforcement learning. It discusses applications of machine learning such as fraud detection, self-driving cars, and personalized recommendations. The document then provides recommendations for getting started in machine learning as both a perfectionist and a pragmatist, including course recommendations. It also briefly discusses career paths in machine learning and options for higher education in machine learning in India and abroad.

1. Machine Learning
What is it , why is it important and what you need to do?

2. Agenda
● Introduction
● Supervised Learning
● Unsupervised Learning
● Reinforcement Learning
● Applications
● Demo*
● How to get started on your own? - The Perfectionist
● How to get started on your own? - The Pragmatist
● Career in ML?
● Higher Education in ML - India
● Higher Education in ML - Abroad

3. Introduction
*Image Courtesy - https://www.newtechdojo.com/list-machine-learning-algorithms/

4. Supervised Learning
*Image Courtesy - https://www.boozallen.com/content/dam/boozallen_site/sig/pdf/publications/machine-intelligence-quick-guide-to-how-machines-learn.pdf

5. Supervised Learning - “Concretely”
*Image Courtesy - http://www.holehouse.org/mlclass/01_02_Introduction_regression_analysis_and_gr.html

6. Linear Regression
*Image Courtesy - http://www.holehouse.org/mlclass/01_02_Introduction_regression_analysis_and_gr.html
Objective Error

7. Logistic Regression (Classification)
*Image Courtesy - http://www.holehouse.org/mlclass/01_02_Introduction_regression_analysis_and_gr.html
Objective
Error

8. Unsupervised Learning
*Image Courtesy - https://www.boozallen.com/content/dam/boozallen_site/sig/pdf/publications/machine-intelligence-quick-guide-to-how-machines-learn.pdf

9. Reinforcement Learning
*Image Courtesy - https://medium.freecodecamp.org/an-introduction-to-reinforcement-learning-4339519de419

10. Applications
● Real Time Fraud detection in Credit cards
● Self driving cars
● Practical Speech recognition
● Health Care
● Ads targeting
● HFT Algorithmic trading
● Uber, netflix
● Text recognition
● Space Debris
● Myntra - Pricing, Personalization, Recommendation, Fashion Design generation (GAN),
Forecasting, Search, Ads ranking, Sizing recommendation, User affinities, Personalized
Notification

11. ***Disclaimer - The thing you are about to witness is never attempted in a real life
situations. If things go horribly wrong please cut me some slack and bare with me.
As Murphy’s Law says- “Anything that can go wrong will go wrong”
DEMO
URL - https://goo.gl/forms/IJ6SM4qcRuKNvNGl2

12. How to get Started? - The Perfectionist
● Programming - Coursera Algorithms by Stanford Tim Roughgarden (Intermediate), NPTEL data structures by Dr
Naveen Garg (Beginner), Geeksforgeeks, Leetcode and Firecode.io(my favorite)
● Probability - Khan Academy(Beginner), MIT 6.041 Introduction to probability lectures by Prof. John Tsitsiklis
(Beginner/ Intermediate), Harvard Statistics 110 by Prof. Blitzstein (Advanced)
● Linear Algebra - Essence of Linear Algebra videos on Youtube by 3blue1brown channel (Beginner), Gilbert Strang
Lectures at MIT (Advanced), Mathematics for Machine Learning: Linear Algebra in Coursera by Imperial College
London (Intermediate)
● Calculus - Essence of Calculus videos on Youtube by 3blue1brown channel (Beginner), Multivariate Calculus on
Khan Academy(Intermediate)
● Machine Learning - Coursera Stanford Machine Learning by Andrew Ng*(Beginner), CS229 Machine Learning
Lectures by Andrew Ng(Intermediate), Nando De Freitas UG and PG Machine Learning Course at UCB in Youtube
(Intermediate), Abu Mostafa Machine Learning course at Caltech (Intermediate)
● Deep Learning - CS231n Lectures by Andrej Karpathy at Stanford(Beginner), Fast.ai course taught by Jeremy
Howard(Easy Beginner - No Prerequisite), Coursera Deep Learning by Andrew Ng(Beginner)
● Reinforcement Learning - David Silver Lectures in Youtube, Sutton Book (one of the easiest books to read), udacity
Reinforcement Learning, AI lectures at MIT, Deep Reinforcement Learning Bootcamp by Berkeley
● Udacity Nanodegrees - Foundation to Machine Learning, Deep Learning, Artificial Intelligence
● Books - Programming the Collective Intelligence (Easy Beginner), Pattern Recognition and Machine Learning by
Bishop, Kevin Murphy Machine Learning, Algorithm Design Manual (Intermediate)

13. Are You serious Dude?

14. How to get Started? - The Perfectionist
● Programming - Coursera Algorithms by Stanford Tim Roughgarden (Intermediate), NPTEL data structures by Dr
Naveen Garg (Beginner), Geeksforgeeks, Leetcode and Firecode.io(my favorite)
● Probability - Khan Academy(Beginner), MIT 6.041 Introduction to probability lectures by Prof. John Tsitsiklis
(Beginner/ Intermediate), Harvard Statistics 110 by Prof. Blitzstein (Advanced)
● Linear Algebra - Essence of Linear Algebra videos on Youtube by 3blue1brown channel (Beginner), Gilbert Strang
Lectures at MIT (Advanced), Mathematics for Machine Learning: Linear Algebra in Coursera by Imperial College
London (Intermediate)
● Calculus - Essence of Calculus videos on Youtube by 3blue1brown channel (Beginner), Multivariate Calculus on
Khan Academy(Intermediate)
● Machine Learning - Coursera Stanford Machine Learning by Andrew Ng*(Beginner), CS229 Machine Learning
Lectures by Andrew Ng(Intermediate), Nando De Freitas UG and PG Machine Learning Course at UCB in Youtube
(Intermediate), Abu Mostafa Machine Learning course at Caltech (Intermediate)
● Deep Learning - CS231n Lectures by Andrej Karpathy at Stanford(Beginner), Fast.ai course taught by Jeremy
Howard(Easy Beginner - No Prerequisite), Coursera Deep Learning by Andrew Ng(Beginner)
● Reinforcement Learning - David Silver Lectures in Youtube, Sutton Book (one of the easiest books to read), udacity
Reinforcement Learning, AI lectures at MIT, Deep Reinforcement Learning Bootcamp by Berkeley
● Udacity Nanodegrees - Foundation to Machine Learning, Deep Learning, Artificial Intelligence
● Books - Programming the Collective Intelligence (Easy Beginner), Pattern Recognition and Machine Learning by
Bishop, Kevin Murphy Machine Learning, Algorithm Design Manual (Intermediate)

15. How to get Started? - The Pragmatist
● Coursera Stanford Machine Learning by Andrew Ng*(Beginner)
● Kaggle Competitions for Machine Learning
● Fast.ai for Deep Learning

16. Career in ML?
Data Engineer
● Works on Big Data
● Knows Programming
● Provides Infrastructure
● Solid Software Engineer
● Every Big company has
requirement
Data Scientist / Analyst
● Works for Business teams
● Day to Day improvements
like revenue, marketing
● Mu Sigma, Sigtuple,
Fractal analytics
Data Scientist/ Applied
Scientist/ Computer
Scientist
● Works on Big Data
● Knows Programming,
Machine learning
● Big Companies - Amazon,
Flipkart, Google,
Microsoft, Swiggy
*My Career - Did all the things in perfectionist slide ;) , break problem

17. Higher Education in ML - India
● Udacity Nanodegree
● PGDBA - (IIM Kolkata, ISI Kolkata, IIT Kharagpur, 1 internship) - Best Course in the country right now - takes
60 students
● IISc Data Science Mtech - only 15 students are admitted - very good
● IIT Mtech in Computer Science or Machine Learning , IIT Bombay is the best
● IIIT Bangalore, IIIT Hyderabad MS in Data Science
● ISB Hyderabad 1 year course similar to PGDBA
● IIM Bangalore 6 months Data Analytics Diploma - best for working professional - in bangalore on weekends

18. Higher Education in ML - Abroad
● For job after MS United States is the place to go
● Only want job in US go to university with rank at least 80 according to US News & World Report
● Want to really learn something - go to top 30 colleges
● For Research Canada - University of British Columbia, University of Montreal, University of Toronto, UK -
Oxford, Imperial College London, University College London, Switzerland - EPFL and ETH Zurich
● Georgia Tech Online Masters
Personal Note : Doesn’t matter what career you choose remember Education is lifelong learning, challenge yourself.

19. THANK YOU