Learn the fundamentals of machine learning from scratch: what it is, how it works, the three main types, a step-by-step workflow, common pitfalls to avoid, and a simple …
Learn how to build a production-ready Retrieval Augmented Generation (RAG) pipeline: ingest documents, embed by meaning, retrieve relevant context, and generate accurate answers with an LLM—no more hallucinated …
A step-by-step guide to creating a functional web app from scratch using Python and Flask, covering backend, frontend, database persistence, and deployment.
A step-by-step guide to becoming a professional Python developer: from core language fundamentals to tooling, specialization, and real-world skills that hiring managers actually value.
Learn battle-tested Python design patterns—Repository, Strategy, Dependency Injection, Factory, and retry with backoff—that transform textbook code into production-ready systems that handle load, failures, and real-world complexity.
Learn how to build fast, deterministic tests by combining focused mocks with modular fixtures. Avoid common anti-patterns like monolithic fixtures and global autouse mocks with a practical guide …
Learn practical Python testing strategies that go beyond unit tests, including integration tests, smoke tests, and property-based testing, with real-world examples and CI/CD integration tips.
Learn how to use Celery to turn synchronous Python code into a distributed task queue. This guide covers setup with Redis, task patterns like chaining and retries, broker …
Learn how message queues and background workers keep Python APIs responsive by offloading heavy tasks like image processing and file uploads, with practical examples using Celery, Redis, and …
Learn the core concepts of event-driven architecture (EDA) with practical Python examples, from a simple callback pattern to async event buses and webhook dispatchers, and understand when to …
Neural networks explained simply—starting with a single perceptron, building hidden layers, and training via gradient descent—all with Python code you can run and understand.
Demystify machine learning with Python by building linear regression and decision tree models. Learn core concepts like train-test splits, overfitting, and evaluation metrics through real code examples.