Machine Learning

Articles in this section

• Linear Algebra - Vectors & Matrices

• Linear Algebra - Special Matrices & Concepts

• Probability - Basic Concepts & Random Variables

• Probability - Distributions & Expectation

• Calculus - Derivatives & Gradients

• Calculus - Integrals & Applications

• Optimization - Gradient Descent

• Optimization - Advanced Techniques

• Model Evaluation - Regression Metrics

• Supervised Learning - Classification (Logistic Regression)

• Model Evaluation - Training, Validation, Test Splits

• Model Evaluation - Metrics for Classification

• Overfitting and Underfitting

• Regularization

• Other Supervised Learning Algorithms (Brief Overview)

• Unsupervised Learning - Clustering (K-Means)

• Unsupervised Learning - Dimensionality Reduction (PCA)

• ML Project Workflow & Scikit-learn Introduction

• Introduction to Neural Networks (NNs)

• Activation Functions

• Feedforward Neural Networks

• Loss Functions for Neural Networks

• Optimization - Gradient Descent for Neural Networks

• Backpropagation - Intuition

• Backpropagation - A Little Deeper

• Training a Neural Network - The Full Picture

• Introduction to a Deep Learning Framework

• Training a Simple Neural Network with a Framework

• Understanding Hyperparameters & Common Issues

• Review & Next Steps