From 1d442f4d7c6f01a6741fd66c1b96c4f06bdb1a58 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Tue, 4 Aug 2026 02:02:42 +0000 Subject: [PATCH 1/2] Initial plan From 64e343277170b28a3084b2755370ae8b6bfd0296 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Tue, 4 Aug 2026 02:04:48 +0000 Subject: [PATCH 2/2] =?UTF-8?q?docs:=20Update=20README.md=20to=20Version?= =?UTF-8?q?=202.0=20=E2=80=94=202026=20Edition?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: harrystaley <7409601+harrystaley@users.noreply.github.com> --- README.md | 247 +++++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 217 insertions(+), 30 deletions(-) diff --git a/README.md b/README.md index d76bf75..ed538d8 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,30 @@ # The Python Open Source Data Science Degree +### Version 2.0 — 2026 Edition + A curated list of free or low-cost courses from reputable universities and organizations that satisfy the same requirements as an undergraduate Computer Science / Data Science degree, minus general education. Updated for 2026. --- +## Curriculum Roadmap + +``` +1. Learning How to Learn +2. CS Basics & Programming +3. Math (Calculus → Linear Algebra → Statistics) +4. Databases +5. Data Science +6. Machine Learning +7. Large Language Models (LLMs) +8. Agentic AI & AI Engineering +9. MLOps & LLMOps +10. Computing Systems & CS Theory +11. Cloud, DevOps & Containers +12. Unix, Open Source & Version Control +``` + +--- + ## Learning How to Efficiently Learn If it has been a while since you were in the classroom, this is mandatory. These are high-leverage meta-skills that pay dividends across every other course. @@ -73,11 +94,11 @@ Courses | School | Duration | Effort | Frequency | Prerequisites | Provider --- -## Machine Learning and AI +## Machine Learning -This section is now a core requirement, not optional. +This section is a core requirement. It covers classical and modern machine learning, deep learning, and the foundational skills needed to build, evaluate, and deploy models. -### Foundations +### Courses Courses | School | Duration | Effort | Frequency | Prerequisites | Provider :-- | :--: | :--: | :--: | :--: | :--: | :--: @@ -85,21 +106,184 @@ Courses | School | Duration | Effort | Frequency | Prerequisites | Provider [Practical Deep Learning for Coders](https://course.fast.ai/) | fast.ai | 10 weeks | 8-10 hrs/week | self-paced | Python, some math | fast.ai [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning) | DeepLearning.AI | 5 months | 4-5 hrs/week | self-paced | ML basics | Coursera -### Large Language Models and Generative AI +### Topics Covered + +- Supervised Learning +- Unsupervised Learning +- Regression +- Classification +- Decision Trees +- Ensemble Methods +- Support Vector Machines +- Clustering +- Dimensionality Reduction +- Feature Engineering +- Neural Networks +- Convolutional Neural Networks +- Recurrent Neural Networks +- Deep Learning +- Model Evaluation +- Hyperparameter Optimization +- Explainable AI +- Statistical Learning + +### Key Libraries and Frameworks + +- NumPy +- pandas +- SciPy +- scikit-learn +- PyTorch +- TensorFlow +- Keras +- XGBoost +- LightGBM + +--- + +## Large Language Models (LLMs) + +This section covers the theory, architecture, and practical application of large language models. Topics range from transformer fundamentals and prompt engineering to fine-tuning, retrieval-augmented generation, and responsible deployment. + +### Courses Courses | School | Duration | Effort | Frequency | Prerequisites | Provider :-- | :--: | :--: | :--: | :--: | :--: | :--: -[Short Courses](https://www.deeplearning.ai/short-courses/) | DeepLearning.AI | 1-3 hrs each | 1-3 hrs | self-paced | Python | DeepLearning.AI [Hugging Face NLP Course](https://huggingface.co/learn/nlp-course/chapter1/1) | Hugging Face | 6 weeks | 4-6 hrs/week | self-paced | Python, ML basics | Hugging Face -[CS324: Large Language Models](https://stanford-cs324.github.io/winter2022/) | Stanford | 10 weeks | 6-8 hrs/week | self-paced | Deep Learning | Stanford (free) -[LLM Bootcamp](https://fullstackdeeplearning.com/llm-bootcamp/) | Full Stack Deep Learning | 8 hours | self-paced | — | ML basics | FSDL +[CS324: Large Language Models](https://stanford-cs324.github.io/winter2022/) | Stanford | 10 weeks | 6-8 hrs/week | self-paced | Deep Learning | Stanford +[LLM Bootcamp](https://fullstackdeeplearning.com/llm-bootcamp/) | Full Stack Deep Learning | 8 hours | self-paced | self-paced | ML basics | FSDL +[LLM University](https://cohere.com/llmu) | Cohere | ~20 hours | self-paced | self-paced | Python | Cohere +[DeepLearning.AI Short Courses](https://www.deeplearning.ai/short-courses/) | DeepLearning.AI | 1-3 hrs each | 1-3 hrs | self-paced | Python | DeepLearning.AI + +### Topics Covered + +- Transformer Architecture +- Self-Attention +- Tokenization +- Embeddings +- Prompt Engineering +- Structured Outputs +- Function Calling +- Context Windows +- Retrieval-Augmented Generation +- Fine-Tuning +- LoRA and QLoRA +- Open-Weight Models +- Quantization +- Inference Optimization +- LLM Evaluation +- Safety and Alignment +- Multimodal Models + +### Key Libraries and Frameworks + +- Hugging Face Transformers +- Tokenizers +- Datasets +- PEFT +- TRL +- llama.cpp +- Ollama +- vLLM +- OpenAI SDK +- Anthropic SDK +- Google GenAI SDK + +--- + +## Agentic AI & AI Engineering -### MLOps +This section covers the design, construction, and deployment of AI agents and multi-agent systems. It includes tool use, memory, planning, and orchestration frameworks that form the foundation of modern AI engineering. + +### Courses + +Courses | School | Duration | Effort | Frequency | Prerequisites | Provider +:-- | :--: | :--: | :--: | :--: | :--: | :--: +[AI Agents](https://www.deeplearning.ai/short-courses/) | DeepLearning.AI | 2 hrs | 2 hrs | self-paced | LLM basics | DeepLearning.AI +[Building AI Browser Agents](https://www.deeplearning.ai/short-courses/) | DeepLearning.AI | 2-3 hrs | 2-3 hrs | self-paced | LLM basics | DeepLearning.AI +[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 +[AG2 Documentation](https://docs.ag2.ai/) | AG2 | self-paced | self-paced | self-paced | Python | AG2 +[OpenHands Documentation](https://docs.openhands.dev/) | OpenHands | 4-8 hrs | self-paced | self-paced | Python | OpenHands + +### Topics Covered + +- AI Agents +- Tool Calling +- Function Calling +- Agent Memory +- State Management +- Planning +- Reflection +- Multi-Agent Systems +- Agent Communication +- Browser Agents +- Coding Agents +- Retrieval-Augmented Generation +- Model Context Protocol +- Human-in-the-Loop Workflows +- Long-Running Agents +- Agent Evaluation +- Agent Safety +- Workflow Orchestration + +### Key Libraries and Frameworks + +- OpenAI Agents SDK +- LangGraph +- LangChain +- CrewAI +- AutoGen +- AG2 +- OpenHands +- LlamaIndex +- DSPy +- PydanticAI +- Haystack + +### Capstone Project Ideas + +- Research Assistant +- Enterprise RAG Chatbot +- AI Coding Assistant +- Browser Automation Agent +- Multi-Agent Software Development Team +- Document Processing Pipeline +- Customer Support Agent +- Personal Knowledge Management Assistant + +--- + +## MLOps & LLMOps + +This section covers the operational side of machine learning and LLM systems — from experiment tracking and model registries to prompt versioning, evaluation, observability, and production deployment. MLOps and LLMOps are now essential skills for any practitioner deploying AI in the real world. + +### Courses Courses | School | Duration | Effort | Frequency | Prerequisites | Provider :-- | :--: | :--: | :--: | :--: | :--: | :--: [MLOps Specialization](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops) | DeepLearning.AI | 4 months | 4 hrs/week | self-paced | ML basics | Coursera -[Weights & Biases Courses](https://www.wandb.courses/) | Weights & Biases | varies | self-paced | — | Python, ML | W&B +[Weights & Biases Courses](https://www.wandb.courses/) | Weights & Biases | varies | self-paced | self-paced | Python, ML | W&B + +### Topics Covered + +- Model Deployment +- Experiment Tracking +- Model Registries +- Prompt Versioning +- LLM Evaluation +- Agent Evaluation +- Observability +- Tracing +- Cost Monitoring +- Model Serving +- Data and Model Drift +- Safety Monitoring +- CI/CD for Machine Learning +- LLM Application Testing --- @@ -184,24 +368,27 @@ Courses | School | Duration | Effort | Frequency | Prerequisites | Provider 4. Math (Calculus → Linear Algebra → Statistics) 5. Algorithms Part I & II 6. Databases -7. Computing Systems -8. Machine Learning Specialization -9. Practical Deep Learning for Coders -10. LLM courses (DeepLearning.AI short courses) -11. Cloud Computing + DevOps -12. MLOps - ---- - -## What Changed from the Previous Version - -- **Added:** Machine Learning and AI section (now a core requirement) -- **Added:** LLM / Generative AI subsection with Hugging Face and DeepLearning.AI courses -- **Added:** MLOps subsection -- **Added:** Cloud Computing section (AWS, GCP) -- **Added:** DevOps and Containers section (Docker, Kubernetes) -- **Added:** Vector Databases to the databases section -- **Replaced:** Individual edX CS50 links → direct Harvard links (more stable) -- **Replaced:** Database individual courses → consolidated specialization + modern additions -- **Removed:** IBM Data Science Certificate (superseded by more targeted offerings) -- **Removed:** Applied Cryptography (edX) — Cryptography I (Stanford) covers the same ground better +7. Data Science +8. Computing Systems +9. Machine Learning Specialization +10. Practical Deep Learning for Coders +11. Deep Learning Specialization +12. Large Language Models (LLMs) +13. Agentic AI & AI Engineering +14. Cloud Computing + DevOps +15. MLOps & LLMOps + +--- + +## Changelog + +### Version 2.0 — 2026 Edition + +- **Added:** `## Curriculum Roadmap` section near the top for navigation +- **Renamed:** `Machine Learning and AI` → `## Machine Learning` with topics and libraries subsections +- **Removed:** Old `Large Language Models and Generative AI` subsection from inside Machine Learning +- **Added:** New top-level `## Large Language Models (LLMs)` section with 5 courses, topics, and libraries +- **Added:** New top-level `## Agentic AI & AI Engineering` section with 9 courses, topics, libraries, and capstone ideas +- **Renamed:** `MLOps` subsection → top-level `## MLOps & LLMOps` with expanded topics list +- **Updated:** Recommended Learning Order to reflect the new 15-step curriculum path +- **Preserved:** All existing course links and foundational sections unchanged