Machine Learning Tutorials
Master machine learning, data science, and analytics through interactive tutorials and hands-on projects
Natural Language Processing Fundamentals
Learn the fundamentals of Natural Language Processing with interactive examples and hands-on exercises.
Master NLP concepts through 9 interactive sections covering tokenization, sentiment analysis, and more.
Naive Bayes Classification Guide
Master Naive Bayes classification with interactive weather prediction demo and comprehensive mathematical explanations.
Learn Naive Bayes through interactive weather prediction examples, mathematical foundations, and real-world applications.
Machine Learning Fundamentals
Complete hands-on course with Python implementations and real-world examples covering introduction, regression, and classification.
Complete ML fundamentals course with three comprehensive chapters covering introduction, regression, and classification.
Understanding ML Model Relationships: From Basic Models to Ensemble Methods
Interactive guide to understanding how different ML techniques connect, from basic models through regularization to ensemble methods like Random Forest and XGBoost
Master the relationships between ML algorithms through 8 interactive chapters covering the journey from linear models to advanced ensemble methods.
Decision Trees Tutorial
Master decision tree algorithms from basics to advanced techniques. 5 comprehensive chapters covering introduction, mathematics, Python implementation, overfitting prevention, and ensemble methods.
Interactive decision tree tutorial with Python demos, mathematical foundations, and practical applications across 5 focused chapters.
Complete Exploratory Data Analysis: LeetCode Dataset
Master the complete EDA workflow from data loading to insights, using a real LeetCode dataset with 1825 coding problems.
Learn the complete EDA process: missing data strategies, outlier detection, visualization techniques, and statistical insights.
Comprehensive Clustering Analysis
Master clustering algorithms from mathematical foundations to advanced applications. 15 comprehensive chapters covering distance metrics, K-means, hierarchical clustering, DBSCAN, and evaluation techniques.
Clean, interactive clustering tutorial with mathematical foundations, algorithm implementations, and real-world applications across 15 detailed chapters.
Interactive Linear Algebra: Matrix-Vector Multiplication
Visualize 2D matrix-vector multiplication with interactive animations. Understand linear transformations through drag-and-drop vector manipulation and real-time matrix operations.
Interactive 2D visualization of matrix-vector multiplication with drag-and-drop vectors, real-time transformations, and 3Blue1Brown-inspired animations.
Coding Interview Algorithms: Complete Guide
Master all essential coding interview algorithms with detailed explanations, step-by-step implementations, and real-world examples. Learn when and where to use each algorithm.
Comprehensive guide to coding interview algorithms covering Arrays, Linked Lists, Trees, Graphs, Dynamic Programming, and more with step-by-step code explanations.
Neural Networks Fundamentals
Master the foundations of neural networks from mathematical principles to practical implementations. 8 comprehensive chapters covering feedforward networks, backpropagation, activation functions, CNNs, RNNs, and LSTMs.
Complete neural networks course with detailed explanations, formulas, and code examples covering feedforward networks, backpropagation, CNNs, RNNs, and LSTMs.
Transformer Architecture Deep Dive
Master the Transformer architecture that revolutionized NLP. 10 comprehensive chapters covering attention mechanisms, self-attention, multi-head attention, positional encoding, encoder-decoder architecture, and implementation details.
Deep dive into Transformer architecture with extensive formulas, code examples, and visual explanations covering attention, self-attention, multi-head attention, and complete implementation.
Large Language Models (LLMs)
Master Large Language Models from pre-training to fine-tuning. 8 comprehensive chapters covering BERT, GPT, transfer learning, fine-tuning strategies, prompt engineering, and practical applications.
Complete LLM course covering BERT, GPT, pre-training, fine-tuning, LoRA, prompt engineering, and practical applications with detailed explanations and code examples.
RAG & Retrieval Systems
Master Retrieval-Augmented Generation (RAG) systems from vector databases to production deployment. 7 comprehensive chapters covering embeddings, retrieval strategies, RAG architecture, and building production systems.
Complete RAG tutorial covering embeddings, vector databases, retrieval strategies, RAG architecture, and production deployment with detailed explanations and code examples.
Building Agentic AI Systems
Production Handbook: 22 chapters across 5 sections covering architectures, tool use, MCP, memory, multi-agent orchestration, LangGraph, safety, evaluation, fine-tuning, and frontier research. Prerequisite: Agentic AI Foundations.
Complete advanced course on building Agentic AI systems: agent architectures, MCP, memory systems, multi-agent orchestration, LangGraph, CrewAI, safety, evaluation, and fine-tuning for tool use.
Agentic AI Foundations
Core Concepts: 8 chapters covering the agent loop, ReAct framework, tool-using agents, multi-agent systems, orchestration, and building your first production agent. The recommended starting point before the advanced course.
Core Concepts: agent loop, ReAct, tool use, multi-agent systems, and orchestration across 8 focused chapters. Start here before the Production Handbook.
ML Software Engineering: Interview Concept Review
Structured recap for ML SWE loops: foundational stack, supervised and unsupervised learning, deep learning, retrieval and agents — with onsite deep-dives wherever the portfolio lacks a dedicated tutorial, and links wherever it does.
Interview-ready concept review spanning Python tooling, data work, classical ML, deep learning themes, retrieval, agents, and product ML — anchored to fuller tutorials across the portfolio.