This document provides an overview of machine learning, deep learning, and data science. It discusses the key components of machine learning including supervised and unsupervised learning, as well as reinforcement learning. Deep learning is also introduced. Applications of machine learning discussed include classification algorithms for numbers, generating art using neural style transfer, natural language processing using recurrent neural networks, and detecting objects in images for driverless cars. Generative adversarial networks are also introduced. The document provides resources for learning more about these topics through courses, practice on platforms like Kaggle, and following data scientists on Twitter and blogs.

1. Data Science : Applying Machine Learning & Deep
Learning Algorithms

2. Agenda
Overview
Behind the scenes
Technology & Mathematics
Applications
Numbers - Classification Algorithms
Images - Generating Art
Sequential Data - Natural Language Processing
Videos - Driverless Cars
Generative Adversarial networks (GANs)
Resources

3. Overview of Machine Learning, Deep learning & AI
A Data Scientist is a person who knows more
mathematics than a programmer, and more
programming than a mathematician.
Data Science: Mathematics + Computer
Science + Business Knowledge

4. What is Machine Learning
A computer program is said to learn from experience E with respect to some class of
tasks T and performance measure P, if its performance at tasks in T, as measured by
P, improves with experience E.
Types of Machine Learning
● Supervised, Un-supervised & semi-supervised learning
● Reinforcement Learning
Key Components of learning:
● The loss function / cost functions
● Optimization Algorithms (Gradient Descent, RMSprop, Adam)
● Training, Validation and Test sets

5. What is Deep Learning

6. Agenda
Overview
Behind the scenes
Technology & Mathematics
Applications
Numbers - Classification Algorithms
Images - Generating Art
Sequential Data - Natural Language Processing
Videos - Driverless Cars
Generative Adversarial networks (GANs)
Resources

7. Technology & Mathematics behind Data Science
Technology:
Languages: Python, R, SAS, SparkML, PySpark
Hardware: GPUs to run complex mathematical equations
Notebooks: Jupyter notebook (most popular), Google Colab (free with GPU), AWS
Deep Learning Frameworks: Tensorflow, Keras, Theano, Torch, Caffe
Mathematics:
Linear Algebra
Calculus
Probability Theory
Statistics

8. Cost Function

9. Training, Validation and Test Set

10. Some Interesting Applications
- Product/Movie Recommendation Engines
- Fraud Detection in Loans & Credit Cards
- NLP - Siri/Alexa/OkGoogle
- NLP - Chatbots, Q&A, Machine Translation
- Art and Music Generation (Deepart.io, DeepJazz.io)
- Facial Recognition
- DeepFake
- Driverless Cars
- DeepMind AlphaGo

11. Agenda
Overview
Behind the scenes
Technology & Mathematics
Applications
Numbers - Classification Algorithms
Images - Generating Art
Sequential Data - Natural Language Processing
Videos - Driverless Cars
Generative Adversarial networks (GANs)
Resources

12. Classification Algorithm : Problem Statement
Generic Problem Statement: Classify the data into two or more sets based on
certain features
Loan Singh: Given the demographic and bank statement data of a customer, can
you classify a customer into good or bad (creditworthiness) and predict a probability
of him being a good customer
Airtel Device Finance: Given the telecom data for a customer, can you classify a
customer into good or bad (creditworthiness) and predict a probability of him being a
good customer

13. Linear Regression : Predict actual value

14. Logistic Regression: Predict 0 or 1 (binary
classification)

15. How we built the model with Airtel data to predict customer
default probability
Telecom Data - Multiple
Sources Single view -
User Profile
Targer : CIBIL Data of a
sample 1Million customers
Test Data
Train Data
Scoring
Validation : Actual
vs Predicted
New Customer Prediction
Model

16. Evaluating the performance of a model
Other Model Evaluation Metrics:
● Receiver Operating Characteristic (ROC)
● Gini Index
● KS and Decile Life
● Concordant-Discordant
● Entropy & Information Gain

17. Other Classification Algorithms: Decision Trees &
Random Forest
Gini index : if we select two items from a population at random then they must be of same class and probability
for this is 1 if population is pure (p2 + q2)

18. Random Forest : Ensemble Models

19. Algorithms run for Loan Singh prediction

20. Agenda
Overview
Behind the scenes
Technology & Mathematics
Applications
Numbers - Classification Algorithms
Images - Generating Art
Sequential Data - Natural Language Processing
Videos - Driverless Cars
Generative Adversarial networks (GANs)
Resources

21. Neural Style Transfer
Problem Statement : Generate Artwork from photographs based on the painting
type of famous painters
● Convolutional Neural Networks (CNN)
Vincent van Gogh
Picasso Monet

22. How does a computer understand images?

23. Layers in a CNN : Pooling, Convolution, Dense

24. Inception Network Architecture

25. How does CNN understand an Image : Layer 1

26. How does CNN understand an Image : Layer 2

27. How does CNN understand an Image : Layer 3

28. How does CNN understand an Image : Layer 4

29. How does CNN understand an Image : Layer 5

30. Neural Style Transfer

31. Example

32. Agenda
Overview
Behind the scenes
Technology & Mathematics
Applications
Numbers - Classification Algorithms
Images - Generating Art
Sequential Data - Natural Language Processing
Videos - Driverless Cars
Generative Adversarial networks (GANs)
Resources

33. Sequence Data : Recurrent Neural Networks

34. How does an algorithm understand language?
A language model estimates the probability of a sentence (sequence or words) occuring together, and provides a
comparison between different possible combination or variants of similar sentence.

35. Recurrent Neural Network
Input Sentence: Cats average 15 hours of sleep everyday.

36. Long - Short Term Memory Network
Input Sentences:
(1) Dog was playing
(2) Dogs were playing
Input Sentences:
(1) Dog, who is a beagle and name is Hazel, was playing
(2) Dogs, who are golden retrievers and 15 in number, were playing

37. Word Embeddings
Man:Woman = King:?

38. Agenda
Overview
Behind the scenes
Technology & Mathematics
Applications
Numbers - Classification Algorithms
Images - Generating Art
Sequential Data - Natural Language Processing
Videos - Driverless Cars
Generative Adversarial networks (GANs)
Resources

39. Driverless Cars - YOLO
Problem Statement : Given an image (a frame of a video) - Identify the boundary
box of cars and other objects
Algorithm Used: YOLO (You Look Only Once)

40. How does YOLO work?

41. Detecting multiple objects in an image

42. Agenda
Overview
Behind the scenes
Technology & Mathematics
Applications
Numbers - Classification Algorithms
Images - Generating Art
Sequential Data - Natural Language Processing
Videos - Driverless Cars
Generative Adversarial networks (GANs)
Resources

43. Generative Adversarial Networks (GANs)

44. Agenda
Overview
Behind the scenes
Technology & Mathematics
Applications
Numbers - Classification Algorithms
Images - Generating Art
Sequential Data - Natural Language Processing
Videos - Driverless Cars
Generative Adversarial networks (GANs)
Resources

45. Resources
● Learn
○ Coursera Machine Learning & Deep Learning Specializations
○ Fast.ai
○ Analytics Vidhya Blogs, KDNuggets Blog
○ Twitter feeds and Blogs of Data Scientists
● Practice
○ Kaggle Competitions (+ win money)
○ Google Colab Notebooks / AWS / Gaming PC with Nvidia GPU + CUDA

46. Thank You
Ashutosh Kumar
ashutosh@seynse.com
9741329821Blog on Machine learning:
http://www.ashukumar27.io/