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Source code: https://github.com/dskskv/Think-ML/
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
Early Days AI Programs : Deep Blue
Now, AI Programs
● Alpha go is best example, wrote for Playing Go
game, but it can play Atari games also.
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”
Let's have problem
Let's have problem: It seems easy but difficult to
solve without machine learning.
Open Source Libraries
Classifier
Scikit-learn
Test ! No error ! Yay !!
Supervised Learning
Collecting
Training
Data
Train
Classifier
Make
Predictions
Training Data
Weight Texture Label
150g Bumpy Orange
170g Bumpy Orange
140g Smooth Apple
130g Smooth Apple
Features
Examples
Training Data
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 ?
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..
Demo
2. Visualizing a Decision Tree
3. What Makes a Good Feature?
Imagine we want to write classifier to classify two
types of dogs.
Variation in the world !
Hands - On Session
https://github.com/dskskv/Think-ML/
About 80% of dogs at this height are
labs
About 95% of dogs at this height are
greyhounds
lFeature captures different types of information
Thought Experiment
Avoid useless features
Independent features are best
Height in Inches
Height in centimeters
Avoid Redundant features
Feature should be easy to understand
Thank you
@khatri_chetan

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