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Think machine-learning-with-scikit-learn-chetan

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Chetan Khatri
Chetan Khatri

A Handson session on Machine learning with Scikit-learn

Data & Analytics
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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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