AI & Machine Learning Fundamentals

Syllabus

1. Module 1

   ML Foundations

   Build intuition for core machine learning concepts and your first supervised learning algorithms.

   5 lessons

   1. What Is Machine Learning? →
   2. Linear Regression from Scratch →
   3. Logistic Regression Introduction →
   4. KNN Algorithm Explained →
   5. Train/Test Split and Metrics →

2. Module 2

   Models and Ensembles

   Level up with tree-based models, ensemble methods, and techniques to keep your models honest.

   4 lessons

   1. Decision Trees and Random Forests →
   2. Gradient Boosting Explained →
   3. Overfitting and Regularization →
   4. Cross-Validation Strategies →

3. Module 3

   ML Engineering

   Master the practical skills that separate notebook experiments from production-ready models.

   5 lessons

   1. Feature Engineering Techniques →
   2. Data Preprocessing Pipeline →
   3. Hyperparameter Tuning Guide →
   4. Model Evaluation Metrics →
   5. Dimensionality Reduction →

4. Module 4

   LLM Foundations

   Understand how large language models work under the hood, from tokens to transformer blocks.

   4 lessons

   1. What Is an LLM? →
   2. Tokenization Explained →
   3. Temperature and Top-p Explained →
   4. Transformers Architecture Explained →

5. Module 5

   Working with LLMs

   Put LLMs to work with function calling, structured output, streaming, and reliability techniques.

   5 lessons

   1. Function Calling →
   2. Structured Output →
   3. Streaming Responses →
   4. Embeddings Explained for Developers →
   5. Hallucination Detection and Mitigation →

6. Module 6

   AI Applications

   Build real-world AI systems with agents, tool use, vector search, and safety guardrails.

   5 lessons

   1. AI Agents Architecture Guide →
   2. AI Agents Tool Use Patterns →
   3. Vector Search with FAISS →
   4. Guardrails and Content Filtering →
   5. Model Deployment Patterns →