Machine Learning
This page collects all my posts on topics I find interesting about how to learn Machine Learning 😁💻.
Interactive Rubik's Cube Solver & ML Explainer 🧩
Learn how to solve a Rubik's Cube with interactive 3D visualizations, custom algorithms walkthrough, and Python Reinforcement Learning models.
Elastic Net Regression: Theory, Geometry, and Numerical Example
Understand the theory, constraint geometry, and step-by-step numerical calculation of Elastic Net regression.
Introduction to Reinforcement Learning (Part 1)
An intuitive introduction to Reinforcement Learning. Learn the core concepts, Markov Decision Processes, key training trade-offs, and algorithm taxonomies.
Flappy bird Game Part 2 🐦 with Artificial Neural Network (ANN)
In this part 2, I will explain a simple Aritifical Neural Network (ANN) model to make Flappy Bird A.I.
Flappy bird Game Part 1 🐦
In this post, I will guide you through creating a Flappy Bird game, originally developed by Vietnamese creator Dong Ha NGUYEN. In part 2, I will explain a simple Aritifical Neural Network (ANN) model to make Flappy Bird A.I.
Support Vector Machine - Methodology 🧭
This post explains how Support Vector Machines (SVM and SVR) work, from the hyperplane concept and the kernel trick to the epsilon-insensitive tube used in regression.
Random Forest - Methodology 🌲
This post explains how Random Forest works, step by step. It covers bootstrap sampling, random feature selection, decision tree growth, and aggregation.
KNN Regression Part 2 – Wind Power Prediction 🌬️⚡
This post demonstrates how to model wind turbine power using KNN regression with simulated wind-farm data.
K-Nearest Neighbours (KNN) Algorithm - Part 1
K‑Nearest Neighbors (KNN) is a simple, non‑parametric machine learning algorithm that makes predictions based on the labels of the closest data points in the training set. It is supervised (it needs labeled examples) and can be used for both classification and regression tasks."
Naive Bayes - Prediction probability Storm days 🌪️
This is my 1st post about Machine Learning (ML). This is a topic I learnt from the book "An Introduction to Statistical Learning".