docs: Update README.md to Version 2.0 — 2026 Edition

[Copilot]

Modernizes the curriculum README to reflect the current AI/ML landscape by promoting LLMs and agentic AI from subsections to first-class curriculum pillars.

Changes

• Header: Added Version 2.0 — 2026 Edition subtitle
• Curriculum Roadmap: New numbered ASCII flowchart section near the top for quick orientation
• ## Machine Learning (renamed from "Machine Learning and AI"): Drops the LLM subsection; adds Topics Covered and Key Libraries and Frameworks lists (scikit-learn, PyTorch, XGBoost, etc.)
• ## Large Language Models (LLMs) (new top-level): 5 courses (Hugging Face NLP, Stanford CS324, LLM Bootcamp, LLM University, DeepLearning.AI); topics include RAG, fine-tuning, LoRA/QLoRA, alignment; libraries include Transformers, PEFT, vLLM, Ollama
• ## Agentic AI & AI Engineering (new top-level): 9 courses (OpenAI Agents SDK, LangGraph, CrewAI, AutoGen, AG2, OpenHands, MCP, browser agents); topics cover tool calling, multi-agent systems, MCP, HITL workflows; libraries include LangChain, LlamaIndex, DSPy, PydanticAI; capstone project ideas included
• ## MLOps & LLMOps (elevated from subsection): Expanded topics to cover prompt versioning, LLM/agent evaluation, observability, tracing, and cost monitoring
• Recommended Learning Order: Updated to 15-step path reflecting new section ordering
• ## Changelog: Replaces the old "What Changed" section with a versioned entry

[Copilot]

Pull request overview

This PR modernizes the curriculum README for a “Version 2.0 — 2026 Edition” by elevating LLMs, agentic AI, and LLMOps into first-class curriculum pillars and updating the recommended learning path accordingly.

Changes:

• Adds a “Curriculum Roadmap” section and a version subtitle at the top of the README.
• Restructures the ML content into a dedicated ## Machine Learning section (with topics/libraries) and introduces new top-level ## Large Language Models (LLMs) and ## Agentic AI & AI Engineering sections.
• Elevates ## MLOps & LLMOps, expands its topics list, and updates the “Recommended Learning Order” plus a versioned changelog entry.

Suppressed comments (2)

README.md:208

• These course rows use "self-paced" in the "Effort" column, which duplicates "Frequency" and breaks the table’s meaning (Effort is otherwise hours/week or "—"). If weekly effort isn’t known, using "—" for Effort keeps the table consistent.

[OpenAI Agents SDK](https://openai.github.io/openai-agents-python/) | OpenAI | 3-6 hrs | self-paced | self-paced | Python | OpenAI
[Model Context Protocol](https://modelcontextprotocol.io/) | Anthropic | 3-4 hrs | self-paced | self-paced | Python | MCP Documentation
[LangGraph Academy](https://academy.langchain.com/) | LangChain | 8-15 hrs | self-paced | self-paced | Python, LLM basics | LangChain
[CrewAI Documentation](https://docs.crewai.com/) | CrewAI | 4-6 hrs | self-paced | self-paced | Python | CrewAI
[AutoGen Documentation](https://microsoft.github.io/autogen/) | Microsoft | 6-10 hrs | self-paced | self-paced | Python | Microsoft

README.md:269

• This row sets the "Effort" column to "self-paced", which duplicates the "Frequency" column. Using "—" for Effort (or an hours/week estimate) would keep the table consistent.

[Weights & Biases Courses](https://www.wandb.courses/) | Weights & Biases | varies | self-paced | self-paced | Python, ML | W&B

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