Machine Learning Topics

Supervised Learning

Python and Jupyter Basics
Model Representation
Cost Function
Gradient Descent
Numpy and Vectorization
Multiple Variable Regression
Feature Scaling and Learning Rate
Polynomial Regression and Feature Engineering
Scikit-Learn and Gradient Descent
Linear Regression Summary
Introduction to Classification
Sigmoid Function
Decision Boundary
Logistic Loss
Cost Function in Logistic Regression
Gradient Descent for Logistic Regression
Scikit-Learn for Logistic Regression
Overfitting
Regularization
Logistic Regression Summary

Advanced Learning Algorithms

Neural Networks Assignment
Neurons and Layers
Coffee Roasting (TensorFlow)
Coffee Roasting (Numpy)
Neural Network Training Assignment
Multiclass Classification (TensorFlow)
ReLU Function
Softmax Function
Advice for Applying ML
Decision Trees with Markdown

Unsupervised Learning

Anomaly Detection
K-Means Assignment
Collaborative Filtering
Neural Network Recommender Systems
Reinforcement Learning Assignment
State-Action Value Function Example