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Applied Machine Learning in Python

Video Introducing this tutorial

Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn
Introduction
Key Concepts in Machine Learning
Python Tools for Machine Learning
An Example Machine Learning Problem
Examining the Data
K-Nearest Neighbors Classification


Module 2: Supervised Machine Learning - Part 1
Introduction to Supervised Machine Learning
Overfitting and Underfitting
Supervised Learning: Datasets
K-Nearest Neighbors: Classification and Regression
Linear Regression: Least-Squares
Linear Regression: Ridge, Lasso, and Polynomial Regression
Logistic Regression
Linear Classifiers: Support Vector Machines
Multi-Class Classification
Kernelized Support Vector Machines
Cross-Validation
Decision Trees


Module 3: Evaluation
Model Evaluation & Selection
Confusion Matrices & Basic Evaluation Metrics
Classifier Decision Functions
Precision-recall and ROC curves
Multi-Class Evaluation
Regression Evaluation
Model Selection: Optimizing Classifiers for Different Evaluation Metrics


Module 4: Supervised Machine Learning - Part 2
Naive Bayes Classifiers
Random Forests
Gradient Boosted Decision Trees
Neural Networks
Deep Learning (Optional)
Data Leakage
Introduction
Dimensionality Reduction and Manifold Learning
Clustering
Conclusion

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