This series walks you through the foundations, algorithms, and real-world applications of machine learning — from simple classifiers to deep neural networks and ranking systems used by search engines and social feeds.
Each lesson builds upon the previous one, helping you evolve from simple pattern recognition to advanced topics like Deep Learning, Recommender Systems, and Learning to Rank.
🧩 Lessons Overview
📘 Lessons
- Introduction — What is machine learning and why it matters in today’s data-driven world.
- Prerequisites — The essential math and logic foundations: statistics, probability, and linear algebra.
🎯 Supervised Learning
Supervised learning is where we teach a model using labeled data — examples where we already know the correct answer. The system learns to generalize patterns from this labeled information.
- Naive Bayes — Classify messages as spam or not spam using probabilities.
- Model Performance — Learn how to measure accuracy, precision, recall, and other metrics.
- Naive Bayes Optimizations — Extend the model to handle multiple classes and categories.
- K-Nearest Neighbors — Classify new points based on the majority vote of nearby examples.
- Decision Trees — Build decision-making trees that resemble human reasoning.
- Linear Regression — Predict continuous outcomes like electricity demand or prices.
- Logistic Regression — Move from prediction to classification with probability thresholds.
- Support Vector Machines — Separate data with the best possible margin between classes.
🧭 Unsupervised Learning
In unsupervised learning, the data doesn’t come with labels. The model must discover patterns and structure all by itself — grouping, compressing, or identifying hidden relationships.
- K-Means Clustering — Discover clusters and natural groupings in unlabeled data.
- Singular Value Decomposition (SVD) — Decompose data to reveal underlying patterns — key to dimensionality reduction and recommender systems.
🧠 Deep Learning
Deep learning is inspired by how the human brain processes information. It powers modern applications like image recognition, voice assistants, and text generation.
- Neural Networks — Understand how neurons and layers form the backbone of deep learning.
- Convolutional Neural Networks — Learn how computers “see” and classify images.
- Recurrent Neural Networks — Model sequences such as speech, text, and time-series data.
- Generative Adversarial Networks (GANs) — Discover how two competing neural networks can create realistic new data like images and videos.
💡 Recommender Systems
Recommender systems personalize what users see online — from Netflix and YouTube suggestions to product recommendations in e-commerce.
- Collaborative and Content-Based Filtering — Understand how user behavior and content similarity drive modern recommendation engines.
📊 Ranking Systems
After generating recommendations, the next challenge is ordering them for maximum engagement — deciding what should appear first.
- Learning To Rank — Learn how search engines, feeds, and e-commerce platforms rank content to optimize user experience.
🧠 Who This Series Is For
This series is perfect for:
- Developers and data scientists starting their ML journey
- Students preparing for interviews or academic projects
- Researchers revisiting the fundamentals
- Entrepreneurs exploring how AI powers modern products
🧰 What You’ll Learn
By following the series, you’ll gain a deep understanding of:
- The three main branches of ML: supervised, unsupervised, and deep learning
- How to train, evaluate, and improve models
- The intuition behind popular algorithms like SVMs, CNNs, RNNs, and GANs
- How machine learning is applied to real-world problems like recommendation, forecasting, and ranking
🔗 Start Learning
👉 Begin your journey with Lesson 1: Introduction or jump directly to any section that interests you.