Core Machine Learning Overview

Build solid ML skills step by step. Clear goals, small projects, and habits that make models reliable in the real world.

Introduction to Machine Learning

What ML is and when to use it

Understand the difference between ML, AI, and Deep Learning. Learn when ML makes sense and when traditional programming is better. Preview the full workflow before diving into algorithms.

You'll learn: ML definitions, real-world applications, when to use ML, prerequisites check

ML Lifecycle

From problem to production

See the complete journey: define the problem, collect and prepare data, train models, evaluate with proper metrics, deploy, and monitor. Learn to iterate—measure, improve, repeat.

You'll learn: Problem framing, data preparation, experiment tracking, model evaluation, deployment basics, monitoring

Regression

Predict continuous values

Master Linear Regression from the ground up. Understand cost functions, training processes, and evaluation metrics. Learn to spot and fix overfitting.

You'll learn: Linear regression, polynomial regression, cost functions (MSE), R², RMSE, MAE, bias-variance tradeoff

Classification

Predict categories

Build classifiers that generalize. From Logistic Regression to Decision Trees and Random Forests. Master evaluation metrics and confusion matrices.

You'll learn: Logistic regression, decision trees, random forests, accuracy, precision, recall, F1, ROC-AUC

Supervised Advanced

Expand your toolkit

Add KNN, Naive Bayes, and SVM to your arsenal. Compare algorithms and learn when to use each. Build intuition for model selection.

You'll learn: k-Nearest Neighbors, Naive Bayes, Support Vector Machines, algorithm comparison, model selection

Unsupervised Learning

Find patterns without labels

Discover hidden structure in data. Cluster similar items and compress features while keeping signal. Master evaluation without ground truth.

You'll learn: K-means clustering, hierarchical clustering, DBSCAN, PCA, dimensionality reduction, evaluation strategies

Reinforcement & Next Steps

Learning through rewards

Understand how agents learn from interaction. Explore Q-Learning basics and see where RL fits. Chart your path forward in ML and deep learning.

You'll learn: RL fundamentals, Q-Learning intro, key ML concepts summary, next learning steps, project ideas

Start simple, measure honestly, and improve in small steps. A clear baseline plus good metrics beats fancy algorithms every time.