learning-resources

Coding articles to level up your development skills

A curated collection of common interview questions to help you prepare for your next interview.

A log of things I'm learning

A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.

Short PHP code snippets for all your development needs

Machine Learning Journal for Intermediate to Advanced Topics.

🧭 Architecture-first system design: 26 bilingual tutorials, 25 architecture templates, and 6 end-to-end cases covering distributed systems, AI-native systems, RAG, coding Agents, and production trade-offs.

👓 A curated list of awesome android learning resources for android app developers.

🌟 A curated collection of free, high quality AI tools 🤖, APIs 🔗, datasets 📊, and learning resources 📚 covering machine learning 🧠, deep learning 🧩, generative AI 🎨, NLP 💬, and data science 📈. Designed to help developers 👩‍💻, researchers 🔬, and creators ✨ explore and build with AI faster ⚡.

A 10-week, 30-minutes-a-day roadmap for LLM inference serving and optimization. vLLM, SGLang, quantization, speculative decoding, benchmarking.

Set of 📝 with 🔗 to help those building Voice AI agents 🎙️🤖

An awesome list for collecting awesome lists related to AI.

A free, self-paced 24-week AI engineering course: Python, machine learning, LLMs, RAG, fine-tuning, agents and MCP, Azure and Vertex and Bedrock, and Databricks. 43 runnable notebooks, one continuous case study. MIT licensed, no signup. By Zorost Intelligence AI Lab.

Awesome Agentic AI Learning Resource by DevKay is a curated roadmap for mastering Agentic AI—from ML foundations to production-ready agents. Features a 0–12+ month path, hands-on projects, top resources, agent architectures, and tools like LangChain and AutoGen.

An evidence-led, eight-locale LLM playbook: a transferable core, the Codex flagship track, and adapters for ChatGPT, Claude Code, Gemini, DeepSeek, and Grok.

A zero-to-100 learning path for applied AI engineering — RAG, embeddings, vector search, agents, MCP, and the production engineering around them. 56 pages, built as a searchable site.

Free AI/ML course with 950+ Jupyter notebooks — Python, deep learning, LLMs, RAG, agents, prompt engineering, fine-tuning, MLOps