The document outlines an introduction to machine learning using Scikit-learn in Python, presented by Chetan Khatri. It covers essential concepts such as supervised learning, classifiers, and features, while also demonstrating practical examples like decision tree visualization and classifier implementation. The author emphasizes the importance of training data and selecting informative features to enhance machine learning models.

1. Think Machine Learning with Scikit-
learn (Python)
By: Chetan Khatri
Principal Big Data Engineer, Nazara Technologies.
Data Science Lab, The Department of Computer Science, University of Kachchh.

2. About me
l- Principal Big Data Engineer, Nazara Technologies.
l- Technical Reviewer – Packt Publication.
l- Ex. Developer - Eccella Corporation.
lAlumni, The Department of Computer Science, KSKV Kachchh
University.

3. Outline
lAn Introduction to Machine Learning
lHello World in Machine learning with 6 lines of
code
lVisualizing a Decision Tree
lClassifying Images
lSupervised learning : Pipeline
lWriting first Classifier

4. Early Days AI Programs : Deep Blue

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

6. Machine Learning
lMachine Learning does this possible, it is study of
algorithms which learns from examples and
experience having set of rules and hard coded
lines.
l“Learns from Examples and Experience”

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

8. Open Source Libraries

13. Classifier

14. Scikit-learn

15. Test ! No error ! Yay !!

16. Supervised Learning
Collecting
Training
Data
Train
Classifier
Make
Predictions

17. Training Data
Weight Texture Label
150g Bumpy Orange
170g Bumpy Orange
140g Smooth Apple
130g Smooth Apple
Feature
s
Example
s

26. Training Data

27. Important Concepts
lHow does this work in Real world ?
lHow much training data do you need ?
lHow is the tree created ?
lWhat makes a good feature ?

28. Many Types of Classifier
lArtificial Neural Network (ANN)
lSupport Vector Machine (SVM)
lNearest Neighbour classifier (KNN)
lRandom Forest (RF)
lGradient Boosting Machine (GBM)
lEtc..
lEtc..

29. Demo

30. 2. Visualizing a Decision Tree

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

34. Variation in the world !

35. lHands - On

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

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

39. lFeature captures different types of information

40. Thought Experiment

46. Avoid useless features

47. Independent features are best

48. lHeight in Inches
lHeight in centimeters

49. lHeight in Inches
lHeight in centimeters

50. lAvoid Redundant features
lFeature should be easy to understand

54. lSimpler relationships are easier to learn.
lIdeal features are:
lInformative
lIndependent
lSimple

55. 4. Pipeline - Machine Learning

73. lhttp://playground.tensorflow.org/

74. 5. Writing our first classifier

81. lMeasure Distance

85. Demo
lImplement nearest neighbor Algorithm

86. Next Step

89. Thank you