Tutorials
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- PythonPython ABCs- The Complete Guide to Abstract Base ClassesExplore Python's abc module, learn how Abstract Base Classes enforce interfaces, create robust hierarchies, and improve your OOP design.
- PythonInheritance vs. Composition in Python- When to Use WhichMaster Python's inheritance and composition, how they work, when to use each, with clear examples. Elevate your OOP skills effectively.
- PythonHow Python Handles Multiple Inheritance- MRO ExplainedLearn to design robust Python class hierarchies by mastering multiple inheritance and Method Resolution Order (MRO) behavior.
- Generative AI and NLPRAPTOR RAG Explained: Building Hierarchical Retrieval for Smarter AI AnswersRAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) builds a tree of increasingly abstract summaries over your documents, so RAG can retrieve specific details and high-level insights alike. Learn how it works and build it in Python.
- Generative AI and NLPGraphRAG Explained: Your Complete Guide to Knowledge Graph-Powered RAGGraph RAG is an advanced RAG technique that connects text chunks using vector similari to build knowledge graphs, enabling more comprehensive and contextual answers than traditional RAG systems. Graph RAG understands connections between chunks and can traverse relationships to provide richer, more complete responses.
- Generative AI and NLPFusion RAG Explained: How to Combine Vector and Keyword Search for Better AI AnswersFusion RAG is a technique that combines vector and keyword search scores to find more relevant documents for your system. Instead of relying on just one search approach, it normalizes scores from different methods and creates an intelligent weighted average to give you better, more comprehensive answers.
- Generative AI and NLPFeedback Loop RAG: Improving Retrieval with User InteractionsFeedback Loop RAG is an advanced RAG technique that learns from user interactions to continuously improve retrieval quality over time. Unlike traditional RAG systems that remain static, this approach learns from each interaction to deliver more accurate and personalized responses.
- Generative AI and NLPAdaptive RAG: The Ultimate Guide to Dynamic Retrieval-Augmented GenerationAdaptive RAG chooses the retrieval strategy from the question's complexity: no retrieval for simple queries, single-step retrieval for factual ones and multi-step retrieval for complex questions. Learn how it works and build one in Python.
- Generative AI and NLPSelf RAG Explained: Teaching AI to Evaluate Its Own ResponsesSelf RAG teaches a language model to critique its own generation: decide when retrieval is needed, judge whether retrieved passages are relevant and check that the answer is supported by evidence. Learn how it works and build it in Python.
- Generative AI and NLPMulti-Modal RAG Explained: How AI Understands Text, Images, and MoreMulti-Modal RAG retrieves from text and visual content together, so an AI system can answer questions from documents, images, charts and diagrams. Learn how it works and build it in Python.
- Generative AI and NLPDocument Augmentation : A Guide to Optimizing RAG PerformanceDocument Augmentation RAG enriches documents with generated summaries, questions and metadata before indexing, so user queries match more entry points. Learn the technique and build it in Python.
- Generative AI and NLPCorrective RAG: Fixing LLM Errors for Accurate AI ResponseCorrective RAG (CRAG) is an advanced RAG technique that adds a quality check layer to your retrieval system. Instead of blindly trusting retrieved documents, CRAG evaluates their relevance and takes corrective action when the retrieved information is poor, irrelevant, or insufficient.
- Generative AI and NLPBuilding Reliable RAG Systems: Adding Validation Layers for Accurate AI AnswersReliable RAG adds validation layers to retrieval-augmented generation: it checks document relevance, detects hallucinations and highlights which sources support each answer. Learn how it works and build one in Python.
- Statistics and MathUnderstanding Confidence Intervals: A spelled out guide to clarify misconceptionsA clear guide to confidence intervals, what they really mean, common misconceptions, how to calculate them, and why they matter in statistical analysis.
- Generative AI and NLPHybrid Search: Vector + Keyword Techniques for better RAG retrievalHybrid Search RAG combines vector embeddings for semantic matches with keyword search for exact matches, then ranks and merges the results. Learn how it works and build it in Python.
- Generative AI and NLPExplainable Retrieval: How to Make RAG Systems Transparent & TrustworthyExplainable Retrieval is a technique that makes RAG (Retrieval-Augmented Generation) systems transparent by showing users which documents were retrieved, why they were chosen, and how confident the system is in its selections. This enables better trust, debugging, and bias detection in AI applications.
- Machine LearningBuild a Custom Scikit-Learn Regression Model: Step-by-Step GuideCreating custom regressors in scikit-learn means building your own machine learning models that follow scikit-learn's API conventions, allowing them to work seamlessly with pipelines, grid search, and all other scikit-learn tools
- Generative AI and NLPSemantic Chunking for RAG: Optimizing Retrieval-Augmented GenerationSemantic Chunking is a context-aware text splitting technique that groups sentences by meaning rather than splitting by fixed sizes. This preserves semantic relationships and dramatically improves retrieval accuracy in RAG systems by ensuring each chunk contains complete, related ideas.
- Generative AI and NLPHyPE-RAG: How Hypothetical Prompt Embeddings Solve Question Matching in Retrieval SystemsHyPE (Hypothetical Prompt Embeddings) is an advanced RAG enhancement technique that precomputes hypothetical questions for each document chunk during indexing rather than generating content at query time.
- Statistics and MathRidge Regression as MAP Estimation – Supporting notesExplore the elegant connection between Ridge Regression and MAP estimation. Step-by-step derivation showing why the L2 penalty equals a Gaussian prior on weights.
- Generative AI and NLPOptimizing RAG Chunk Size: Your Definitive Guide to Better Retrieval AccuracyThe optimal chunk size for RAG systems is usually 128 to 512 tokens: smaller chunks suit precise fact lookups, larger chunks keep more context for reasoning. Learn how to measure and pick the right size for your use case.
- Statistics and MathMaximum A Posteriori (MAP) Estimation – Clearly ExplainedMaximum A Posteriori (MAP) estimation is a Bayesian method for finding the most likely parameter values given observed data and prior knowledge. Unlike maximum likelihood estimation which only considers the data, MAP combines what we observe with what we already know (or believe) about the parameters.
- Generative AI and NLPRelevant Segment Extraction (RSE) – Building better Context by assembling contiguous chunks for better RAG PerformanceRelevant Segment Extraction (RSE) is a query-time post-processing technique that intelligently combines related text chunks into longer, coherent segments, providing LLMs with better context than individual chunks alone.
- Generative AI and NLPHypothetical Document Embedding (HyDE) – A Smarter RAG method to Search DocumentsLearn how HyDE (Hypothetical Document Embeddings) improves RAG systems by creating richer query embeddings for smarter, more accurate AI-driven retrievals.
- Generative AI and NLPBuild a Simple RAG System with CSV Files: Step-by-Step Guide for BeginnersLearn how to build a Simple RAG system using CSV files by converting structured data into embeddings for more accurate, AI-powered question answering.
- Generative AI and NLPSimple RAG Explained: A Beginner’s Guide to Retrieval-Augmented Generation (RAG)Discover how Simple RAG (Retrieval-Augmented Generation) works. This beginner’s guide breaks down how RAG works step by step with Python code implementation.
- Generative AI and NLPOllama Tutorial: Your Guide to running LLMs LocallyA complete guide to Ollama, run LLMs like Llama 3, Mistral, and Gemma locally. Covers installation, model management, prompting, API usage, and customization.
- Machine LearningCUDA Programming: Do Large scale parallel computing in GPU from ScratchLearn CUDA Programming to implement large scale parallel computing in GPU from Scratch. Understand concepts and underlying principles.
- Practice exercisesPython Exercises – Level 1Python exercises to practice as a beginner. These are devised as mini tasks that you might need to apply when programming with Python.
- Machine LearningComplete Data Science and AI Roadmap by ML+A structured roadmap to learn Data Science, AI, and ML from scratch. Covers foundations, Python, statistics, machine learning, deep learning, and career guidance by ML+.