AI Engineering Blog

Articles on machine learning fundamentals from AI Solutions Camp: regression, classification, clustering and reinforcement learning, explained step by step.

• From Zero to Hero Part 1: A Short History of Machine Learning (2024-11-18, 8 min read) – How machine learning got from early neural network models and checkers programs to today's deep learning systems.
• From Zero to Hero Part 2: What is Machine Learning? (2024-12-02, 7 min read) – How machine learning differs from traditional programming, and why that shift in thinking matters.
• From Zero to Hero Part 3: Understanding Regression vs Classification (2024-12-11, 9 min read) – Predicting numbers versus predicting categories: the two core problems behind most machine learning applications.
• From Zero to Hero Part 4: The Four Machine Learning Paradigms (2024-12-19, 8 min read) – Supervised, unsupervised, semi-supervised and reinforcement learning: the four basic approaches behind most machine learning systems.
• From Zero to Hero Part 5: Introduction to Clustering (2024-12-26, 7 min read) – How algorithms group similar data points without labels, and where that helps, from customer behavior to gene sequences.
• From Zero to Hero Part 6: Centroid-Based Clustering (2025-01-02, 8 min read) – How K-means and related algorithms find groups around central points, and when this approach works well.
• From Zero to Hero Part 7: Gaussian Mixture Models (2025-01-09, 9 min read) – How probabilistic clustering lets a data point belong to several groups at once, which handles overlapping and irregular clusters.
• From Zero to Hero Part 8: Hierarchical Clustering (2025-01-15, 8 min read) – Building trees of clusters within clusters to see how data is organized at several scales, from single points to broad categories.
• From Zero to Hero Part 9: Density-Based Clustering (2025-01-22, 9 min read) – How DBSCAN and related algorithms find clusters of any shape by looking for dense regions, and flag outliers along the way.
• From Zero to Hero Part 10: Introduction to Regression (2025-01-29, 8 min read) – An introduction to regression analysis, the basis of predictive modeling in everything from business forecasting to scientific research.
• From Zero to Hero Part 11: Linear and Nonlinear Regression (2025-02-05, 9 min read) – The difference between linear and nonlinear relationships, and when each approach gives better predictions.
• From Zero to Hero Part 12: Logistic Regression (2025-02-12, 8 min read) – How predicting probabilities connects regression and classification, and turns a regression technique into a classifier.
• From Zero to Hero Part 13: Quantile Regression (2025-02-19, 8 min read) – Predicting more than the average: modeling whole distributions to understand risk ranges and extreme outcomes.
• From Zero to Hero Part 14: Regularization in Regression Analysis (2025-02-26, 9 min read) – Ridge, Lasso and Elastic Net: how regularization keeps models from overfitting and helps them generalize.
• From Zero to Hero Part 15: Reinforcement Learning (2025-03-05, 10 min read) – The last part of the series: how algorithms learn behavior through trial and error, with examples from games, robotics and decision-making.