Learning path 02
Core Machine Learning
Build practical intuition for prediction, classification, clustering, dimensionality reduction, and ensemble learning.
Recommended progression
Begin with linear and logistic regression, compare local and probabilistic classifiers, then move into trees, ensembles, clustering, and PCA.
Prerequisites
Comfort with Python, functions, arrays, and introductory algebra is useful. Each tutorial introduces the mathematics it depends on.
Published tutorials
Random Forest: Many Trees, One Strong Learner
Mushroom Classification → Fraud Detection Why Random Forest? Imagine trying to decide whether a mushroom is poisonous or edible. If you ask just one…
PCA (Dimensionality Reduction): Making Sense of High-Dimensional Data
MNIST Compression → Data Visualization Why PCA? In the real world, data is often messy, massive, and high-dimensional. For example, every image in the…
K-Means Clustering: Finding Hidden Patterns in Data
Image Color Quantization → Customer Segmentation Why K-Means Clustering? Have you ever tried to simplify a photo by reducing its colors into just a…
Logistic Regression: Predicting Probabilities with Linear Boundaries
Titanic Survival Prediction → Customer Churn Prediction Why Logistic Regression? In real life, many decisions are yes/no: Logistic Regression is one of the simplest…
Linear Regression: Predicting Trends with Simple Math
House Price Prediction → Sales Forecasting Why Linear Regression? Imagine you’re a real estate agent trying to estimate house prices. You know that bigger…
Naïve Bayes: Fast, Interpretable Text Classification
SMS Spam Filter → Email Spam Detection What You’ll Build We’ll train a Naïve Bayes classifier that flags spam versus ham (legitimate) messages. You’ll…
k-Nearest Neighbors (k-NN): Learning from Neighbors
Iris Classification → Product Recommendation Why k-NN? Imagine moving into a new neighborhood. To understand what life is like, you look at your closest…
Decision Trees: Simple Logic, Powerful Predictions
Play Tennis → Loan Approval Prediction Why Decision Trees? Think about how you make decisions in everyday life. For instance, a bank loan officer…