What is machine learning?
How Machine Learning Works in Practice
The Three Main Types of Machine Learning
Supervised Learning Fundamentals
Linear Regression with Classic Programming
Linear Regression with Machine Learning
Full Batch, Mini Batch and Stochastic Gradient Descent
The Artificial Neuron and Neural Networks
Linear Regression with a Single Neuron
Linear Regression with the Algebraic Method (Part 1)
Linear Regression with the Algebraic Method (Part 2)
One Dimensional Linear Regression (Algebraic Method)
Linear Regression with Neural Networks
Building a Simple Neural Network for Regression
Linear Regression with Two Outputs (Part 1)
Linear Regression with Two Outputs (Part 2)
A Deep Neural Network for Digit Classification
Defining the Classification Network in Code
Training the Digit Recognition Model
Testing and Evaluating the Model
From Pixels to Predictions with Convolutional Neural Networks
K Means Clustering: Theory and Intuition
Implementing K Means Clustering in Python
DBSCAN Clustering: Theory and Intuition
Implementing DBSCAN Clustering in Python
Dimensionality Reduction: Key Concepts
Dimensionality Reduction in Python (PCA)
Introduction to Reinforcement Learning
Solving CartPole with PPO (Stable Baselines3)
Lunar Lander with PPO: From Setup to Landing
Building a Custom Reinforcement Learning Environment
Conclusion and Where to Go Next
