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Code of Conduct
Setup
Episodes
Introduction
Data Cleaning, Imputation, Cross-Validation
Linear and Logistic Regression
Training, Validation, Test Data and Overfitting
Linear Classifiers
Lunch Break
Decision trees and random forests
Navie Bayes and Kernel Methods
Clustering: K-means and Hierarchical Clustering
Dimensionality reduction
Principal and independent component analysis
Neural Networks and Back Propagation
Lunch Break
Deep Learning and Convolutional Neural Networks
All in one page (Beta)
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Reference
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Introduction to Machine Learning with Python
: Instructor Notes
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