This document provides an overview of machine learning concepts including supervised learning pipelines, different classifier types, and what makes a good feature for classification. It discusses machine learning algorithms learning from examples and experience, and highlights scikit-learn as an open source machine learning library. Examples are given around classifying dog breeds based on height, showing how features can capture different types of information and the importance of avoiding redundant or useless features.

1. Source code: https://github.com/dskskv/Think-ML/

2. Outline
● An Introduction to Machine Learning
● Hello World in Machine learning with 6 lines of
code
● Visualizing a Decision Tree
● Classifying Images
● Supervised learning : Pipeline
● Writing first Classifier

3. Early Days AI Programs : Deep Blue

4. Now, AI Programs
● Alpha go is best example, wrote for Playing Go
game, but it can play Atari games also.

5. Machine Learning
Machine Learning does this possible, it is study of
algorithms which learns from examples and
experience having set of rules and hardcoded
lines.
“Learns from Examples and Experience”

6. Let's have problem
Let's have problem: It seems easy but difficult to
solve without machine learning.

7. Open Source Libraries

12. Classifier

13. Scikit-learn

14. Test ! No error ! Yay !!

15. Supervised Learning
Collecting
Training
Data
Train
Classifier
Make
Predictions

16. Training Data
Weight Texture Label
150g Bumpy Orange
170g Bumpy Orange
140g Smooth Apple
130g Smooth Apple
Features
Examples

25. Training Data

26. Important Concepts
● How does this work in Real world ?
● How much training data do you need ?
● How is the tree created ?
● What makes a good feature ?

27. Many Types of Classifier
● Artificial Neural Network (ANN)
● Support Vector Machine (SVM)
● Nearest Neighbour classifier (KNN)
● Random Forest (RF)
● Gradient Boosting Machine (GBM)
● Etc..
● Etc..

28. Demo

29. 2. Visualizing a Decision Tree

30. 3. What Makes a Good Feature?
Imagine we want to write classifier to classify two
types of dogs.

33. Variation in the world !

34. Hands - On Session
https://github.com/dskskv/Think-ML/

36. About 80% of dogs at this height are
labs

37. About 95% of dogs at this height are
greyhounds

38. lFeature captures different types of information

39. Thought Experiment

45. Avoid useless features

46. Independent features are best

47. Height in Inches
Height in centimeters

48. Avoid Redundant features
Feature should be easy to understand

49. Thank you
@khatri_chetan