Understanding Embeddings: From Word2Vec to Modern LLMs
Learn how embeddings work in Python, from Word2Vec to modern transformer-based embedding models. Covers vector arithmetic, cosine similarity, and visualizing embeddings with t-SNE.
Khushal Jethava
Feature Engineering for Tabular ML: A Python Guide
Learn practical feature engineering techniques in Python for tabular machine learning — encoding, scaling, binning, interaction features, and target encoding with real examples.
Building a Local AI Chatbot with Ollama and Python
Learn how to build a private, local AI chatbot in Python using Ollama. Covers installation, streaming responses, conversation memory, and a simple Gradio web UI.
Hyperparameter Tuning with Optuna: A Python Guide
Learn hyperparameter tuning in Python with Optuna. Covers search spaces, pruning, multi-objective optimization, and a complete example tuning an XGBoost model.
Prompt Caching Explained: How It Cuts LLM Costs
Learn how prompt caching works with Claude and OpenAI APIs in Python. See real cost and latency comparisons, and learn how to structure prompts to maximize cache hits.
Building a RAG Evaluation Pipeline with Python
Learn how to evaluate RAG systems in Python using retrieval metrics, faithfulness scoring, and RAGAS. Build a complete evaluation pipeline to catch hallucinations before production.
Python Async/Await for AI Pipelines: A Practical Guide
Learn how to use Python async/await to speed up LLM API calls, batch embeddings, and build concurrent AI pipelines with asyncio and httpx. Includes real benchmarks.
Quantization for LLMs: Run Big Models on Small Hardware
Learn how LLM quantization works in Python — GPTQ, GGUF, and bitsandbytes 4-bit/8-bit. See real memory and speed comparisons and run a quantized model on a laptop GPU.
LangGraph vs LangChain: Building Stateful AI Agents in Python
Compare LangGraph and LangChain for building AI agents in Python. Learn when to use chains vs graphs, how state and cycles work, and build a working multi-step agent with LangGraph.
Vector Databases with Python: A Practical Guide
Learn how vector databases work and how to use them in Python with FAISS, Chroma, and Pinecone. Covers embeddings, similarity search, indexing strategies, and a complete semantic search example.