A curated, self-contained starting point for learning artificial intelligence and machine learning — maintained by the AI Chapter of the Manipal Open Source Society.
Everything here is free to read, run, and fork:
- Explainers you can read right now —
docs/ - Notebooks and code you can run right now —
notebooks/,code/ - A curated index of the best external books, courses, papers and tools — below
Maintained by Akhil Varanasi, Head of AI. Contributions welcome — see Contributing.
Getting oriented What's in this repository · Quick start · Learning roadmap · Explainers · Notebooks · Code
Resource library Books · Courses · Video tutorials · YouTube channels · Research papers · GitHub repositories · Guides & whitepapers
Specialisations AI agents & LLMs · RAG · MLOps & production · CUDA & GPU programming · Computer vision, NLP & RL
Practice & community Tools & libraries · Datasets · Practice platforms · Newsletters & communities
Project Contributing · Contact · License
| Folder | What it holds | Start with |
|---|---|---|
docs/ |
Written explainers on AI fundamentals, generative AI and Git | Get Started with AI |
notebooks/ |
Runnable Jupyter notebooks with worked examples | KNN on Iris |
code/ |
Standalone from-scratch implementations | Transformer from scratch |
data/ |
Small datasets the notebooks depend on | data/README.md |
reference/ |
PDF cheat sheets and lecture notes | ML cheat sheet |
You need Python 3.9 or newer. Everything here runs locally or on Google Colab — no paid services required.
git clone https://github.com/AkCodes23/MOSS-AI.git
cd MOSS-AI
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -r requirements.txt
jupyter notebookPrefer not to install anything? Open any notebook directly in Colab —
File → Open notebook → GitHub, then paste AkCodes23/MOSS-AI.
Setup checklist
- Python 3.9+ installed and on your
PATH - Virtual environment created and activated
-
pip install -r requirements.txtfinished without errors -
notebooks/knn-iris.ipynbruns top to bottom - Accounts created: GitHub, Kaggle, Google Colab
- Git basics understood — docs/git-and-github.md
A realistic path from zero to building things. Times assume roughly 8–10 hours a week.
| # | Stage | Time | What you learn | Start here |
|---|---|---|---|---|
| 1 | Programming basics | 1–2 weeks | Loops, functions, OOP in Python | Google's Python Class |
| 2 | Mathematics | 2–3 weeks | Linear algebra, calculus, probability | Mathematics for Machine Learning |
| 3 | Statistics & EDA | 2–3 weeks | Hypothesis testing, correlation, pandas | Kaggle: Pandas |
| 4 | Data cleaning | 1–2 weeks | Missing data, outliers, feature scaling | Kaggle: Data Cleaning |
| 5 | Machine learning | 3–4 weeks | Regression, KNN, trees, ensembles | Andrew Ng's ML Specialization |
| 6 | Deep learning | 4–6 weeks | Neural nets, CNNs, RNNs, transformers | Deep Learning Specialization |
| 7 | Projects | ongoing | Ship something end to end | Kaggle Competitions |
Practise as you go. Each stage has a matching notebook in this repo — stage 5 maps to
knn-iris.ipynb and random-forests.ipynb,
stage 6 to perceptron.ipynb and code/transformers/.
Three things that matter more than the order:
- Build, break, and fix. Tutorials teach recognition; projects teach recall.
- Finish small things. A working model beats a half-read textbook.
- Use AI tools to learn faster — but read the code they write before you trust it.
For the full topic-by-topic breakdown, see the AI syllabus.
Plain-language write-ups, no setup required.
| Document | What it covers |
|---|---|
| Get Started with AI | What AI is, why it's hard to define, the four capability types |
| Generative AI: An Overview | Why ChatGPT changed things, and what LLMs can actually do |
| Generative AI In Depth | The tech stack, what drives progress, ethics and limitations |
| AI Syllabus | Complete topic checklist, linear algebra through research methodology |
| Git and GitHub | Version control basics every contributor needs |
| Notebook | Topic | Level |
|---|---|---|
knn-iris.ipynb |
K-Nearest Neighbours with full EDA on the Iris dataset | Beginner |
random-forests.ipynb |
Random Forests — compact, focused walkthrough | Beginner |
random-forests-deep-dive.ipynb |
Random Forests in depth, with diagrams and theory | Intermediate |
gradient-boosting.ipynb |
XGBoost, CatBoost and LightGBM compared | Intermediate |
perceptron.ipynb |
The perceptron, built from scratch in TensorFlow | Intermediate |
Dependencies, contributors and known gaps: notebooks/README.md.
code/transformers/ — a minimal GPT built from nothing but PyTorch
primitives: self-attention, multi-head attention, and a full training loop in under 200 lines.
cd code/transformers
python tiny_gpt.pyEvery resource appears once, in the section matching what it is. Free resources are marked.
A note on links. Some URLs use LinkedIn's
lnkd.inshortener, inherited from where the resource was originally shared — they work, but you can't see the destination before clicking. When adding anything new, please use the canonical URL. See CONTRIBUTING.md.
Start here. No purchase needed, and these are genuinely among the best available.
| Book | Author(s) | Why read it |
|---|---|---|
| Mathematics for Machine Learning | Deisenroth, Faisal, Ong | The maths you actually need, nothing more |
| Understanding Deep Learning | Simon Prince | Modern, visual, current — the best free DL text today |
| Deep Learning | Goodfellow, Bengio, Courville | The canonical reference. Dense, but definitive |
| Dive into Deep Learning | Zhang, Lipton, Li, Smola | Every concept paired with runnable code |
| Speech and Language Processing | Jurafsky & Martin | NLP from n-grams to transformers |
| Pattern Recognition and Machine Learning | Christopher Bishop | The Bayesian view of ML |
| Reinforcement Learning: An Introduction | Sutton & Barto | The RL textbook, free from the authors |
- Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow — Aurélien Géron The best first ML book. The whole classical pipeline, then neural networks. Roadmap stages 4–6.
- AI Engineering — Chip Huyen Building production systems on top of foundation models.
- Machine Learning Book (mirror) Foundational algorithms and concepts.
- Mathematics for Machine Learning — see Free and open above
- 45+ Mathematics Books Every Data Scientist Needs — a reference shelf, not a reading list
Don't try to finish a maths textbook before starting ML. Learn linear algebra and probability to working depth, then come back when a model confuses you.
The fastest-dating category — check publication dates before buying.
| Book | Author | Focus |
|---|---|---|
| Build a Large Language Model (From Scratch) | Sebastian Raschka | Implementing a GPT end to end |
| Hands-On Large Language Models | Alammar & Grootendorst | Using and adapting LLMs, heavily illustrated |
| The LLM Engineering Handbook | — | Deployment, evaluation and operations |
| AI Agents: The Definitive Guide | Nicole Koenigstein | Agent architectures and design |
| Building Applications with AI Agents | Michael Albada | Applied agent systems |
| AI Agents with MCP | Kyle Stratis | Model Context Protocol in practice |
Reading order: Build a Large Language Model (From Scratch) → AI Engineering → the agent books.
| Course | Provider | Time | Covers |
|---|---|---|---|
| Google's Python Class | ~2 weeks | Python syntax, strings, lists, files | |
| Kaggle Learn | Kaggle | 3–4 hrs each | Bite-sized, interactive, free certificates |
| Kaggle: Pandas | Kaggle | 4 hrs | Data manipulation — the daily-driver skill |
| Kaggle: Data Cleaning | Kaggle | 4 hrs | Missing values, scaling, dates, encodings |
| Machine Learning Crash Course | ~15 hrs | ML fundamentals with TensorFlow |
- Machine Learning Specialization — Andrew Ng / DeepLearning.AI The course that taught a generation. Still the best first ML course. Roadmap stage 5.
- Machine Learning Theory (CS229) — Stanford Online The rigorous version, with full derivations.
| Course | Provider | Approach |
|---|---|---|
| Deep Learning Specialization | Andrew Ng / DeepLearning.AI | Bottom-up: intuition first, then models |
| Practical Deep Learning for Coders | fast.ai | Top-down: train a working model in lesson one |
| Introduction to Deep Learning (6.S191) | MIT | Fast, current, lecture-style |
| Neural Networks: Zero to Hero | Andrej Karpathy | Build backprop, then a GPT, from scratch |
| Language Modeling From Scratch | — | The mechanics of training a language model |
Pick one and finish it. fast.ai for fast results; Andrew Ng for theory first; Karpathy if you learn by watching someone type.
- 400+ Data Science Resources — a large curated collection
- Python Data Science Library — comprehensive Python DS guide
- Premium Data Science Interview Resources — interview preparation
Agent and LLM courses are grouped under AI agents & LLMs.
Single videos for when you need exactly one topic.
| Video | Length | Covers |
|---|---|---|
| Python for Everybody | 4 hrs | Complete Python from zero |
| Object-Oriented Programming in Python | 1 hr | Classes, inheritance, methods |
| Data Structures & Algorithms | 5 hrs | The interview fundamentals |
| Video | Length | Covers |
|---|---|---|
| Pandas Tutorial | 1 hr | Keith Galli's practical walkthrough |
| NumPy Tutorial | 1 hr | Arrays, broadcasting, vectorisation |
| Matplotlib Tutorial | 1 hr | Plotting and visualisation |
| Data Loading Techniques | — | Reading data efficiently at scale |
Full lecture series are listed under Courses — Stanford's CS229 and MIT's 6.S191 are both there, and both are free on YouTube.
Thirty channels, grouped by what you'd use them for. If you subscribe to only two, make them 3Blue1Brown and StatQuest.
| Channel | Why |
|---|---|
| 3Blue1Brown | The maths behind AI, made visual. Watch the neural network and linear algebra series |
| StatQuest | Josh Starmer explains statistics and ML algorithms clearly, and makes it fun |
| Serrano Academy | Luis Serrano's step-by-step breakdowns — excellent when a concept won't click |
| CodeEmporium | Algorithm explanations with clean visualisations |
| Channel | Why |
|---|---|
| Stanford Online | Full CS229, CS231n and CS224n lectures, free |
| MIT OpenCourseWare | Rigorous theory across the whole curriculum |
| Andrej Karpathy | Neural networks built from scratch, live. Rare clarity |
| Steve Brunton | Scientific ML, control theory, dynamical systems |
| Channel | Why |
|---|---|
| Umar Jamil | Transformers and LLMs implemented line by line |
| Jeremy Howard | Practical deep learning, the fast.ai philosophy |
| DeepLearning.AI | Structured paths from Andrew Ng's team |
| Hugging Face | Modern open-source tooling, from the people building it |
| sentdex | Python ML projects, start to finish |
| Data School | scikit-learn and pandas for beginners, done properly |
| Codebasics | Real-world use cases and career-focused projects |
| freeCodeCamp | Multi-hour complete courses, free |
| Channel | Why |
|---|---|
| Yannic Kilcher | Paper deep dives with genuine technical criticism |
| Two Minute Papers | Research summaries, fast |
| Arxiv Insights | Beginner-friendly explanations of hard papers |
| Machine Learning Street Talk | Long technical debates between researchers |
| AI Explained | Careful analysis of new models and capabilities |
| AI Coffee Break with Letitia | Accessible research explainers |
| Hamel Husain | LLM evaluation, RAG and fine-tuning, from practice |
| Channel | Why |
|---|---|
| Kaggle | Competition walkthroughs and real workflows |
| Google Cloud Tech | Deploying and managing models at scale |
| Matt Wolfe | What shipped this week in AI tooling |
| The AI Advantage | Applying AI to actual business work |
| Siraj Raval | Creative, project-driven AI |
| Channel | Why |
|---|---|
| Lex Fridman | Long-form interviews with leading researchers |
| Tina Huang | Learning strategy and career navigation |
All links go to free arXiv or publisher pages. Read the abstract and figures first — full papers are for the second pass.
| Paper | Year | Why it matters |
|---|---|---|
| Attention Is All You Need | 2017 | The transformer. Start here — everything modern descends from it |
| Deep Residual Learning (ResNet) | 2015 | Skip connections made very deep networks trainable |
| Batch Normalization | 2015 | Why training got dramatically faster and more stable |
| Dropout | 2014 | The regularisation idea you'll use in every model |
| Adam: A Method for Stochastic Optimization | 2014 | The default optimiser, and why |
| Efficient Estimation of Word Representations (word2vec) | 2013 | Where embeddings began |
| Paper | Year | Why it matters |
|---|---|---|
| BERT | 2018 | Bidirectional pre-training; the encoder-only branch |
| Language Models are Few-Shot Learners (GPT-3) | 2020 | Scale as a capability unlock; in-context learning |
| Training LMs to Follow Instructions (InstructGPT) | 2022 | RLHF — how raw models became assistants |
| LoRA: Low-Rank Adaptation | 2021 | Fine-tuning large models on a single GPU |
| Paper | Year | Why it matters |
|---|---|---|
| Generative Adversarial Networks | 2014 | The generator-vs-discriminator idea |
| An Image is Worth 16x16 Words (ViT) | 2020 | Transformers took over vision too |
| Denoising Diffusion Probabilistic Models | 2020 | The basis of Stable Diffusion and friends |
| Paper | Year | Why it matters |
|---|---|---|
| Playing Atari with Deep RL (DQN) | 2013 | Deep networks as value functions |
| Proximal Policy Optimization (PPO) | 2017 | The workhorse policy-gradient method, and the engine behind RLHF |
| Paper | Why it matters |
|---|---|
| Chain-of-Thought Prompting | Reasoning through intermediate steps |
| ReAct: Synergizing Reasoning and Acting | The think-act-observe loop nearly every agent uses |
| Toolformer | Models teaching themselves to call tools |
| Reflexion | Agents that critique and retry their own work |
| Tree of Thoughts | Searching over reasoning paths instead of one chain |
| Generative Agents | Believable simulated behaviour from LLMs |
RAG papers are grouped under RAG.
- Papers with Code — papers paired with working implementations
- Connected Papers — visual maps of a research area
- ArXiv Sanity Preserver — better arXiv browsing and filtering
Repos worth reading end to end, not just starring.
| Repo | What it is |
|---|---|
| Machine Learning for Beginners | Microsoft's 12-week, 26-lesson ML curriculum |
| AI Agents for Beginners | Microsoft's agent course, lesson by lesson |
| LLM Course | Maxime Labonne's complete LLM roadmap with notebooks |
| Hands-On AI Engineering | Practical AI engineering walkthroughs |
| Repo | What it is |
|---|---|
| Awesome Generative AI Guide | Papers, courses and interview prep for GenAI |
| GenAI Agents | Working agent implementations, many patterns |
| GenAI Agents Collection | Further curated agent resources |
| Designing Machine Learning Systems | ML system design patterns |
| Repo | What it is |
|---|---|
| Made with ML | Production ML from design through deployment |
| Prompt Engineering Guide | The reference for prompting techniques |
| Kaggle Competitions | Real problems with real leaderboards. Roadmap stage 7 |
- Google's Agent Whitepaper — comprehensive agent design guide
- Google's Agent Companion — supplementary agent material
- Building Effective Agents by Anthropic — patterns that hold up in production
- Claude Code Best Agentic Coding Practices — agentic coding patterns
- OpenAI's Practical Guide to Building Agents — OpenAI's agent framework
Everything agent-related, ordered as a path rather than a pile. Prerequisites: comfortable Python, and a working understanding of neural networks.
Don't build agents on top of a black box. Build the box.
- Neural Networks: Zero to Hero — backprop, then a GPT
- Introduction to Large Language Models — what an LLM is and how it's trained
- LLMs from Scratch — a language model end to end
code/transformers/— in this repo: a working minimal GPT- Attention Is All You Need — the paper behind all of it
- Agentic AI Overview (Stanford) — the landscape
- Building an Agent from Scratch — the loop, minus the frameworks
- Building Effective Agents — patterns that survive production
- HuggingFace Agents Course — the best structured on-ramp
- Philo Agents playlist — a full development series
- Learn the basics first — if any of the above lost you
- Agent Memory — short- and long-term memory design
- Building Vector Databases with Pinecone — vector storage fundamentals
- Vector Databases: from Embeddings to Applications — the fuller treatment
- MCP with Anthropic — the Model Context Protocol
- Building Agents with MCP — MCP in practice
- Computer Use with Anthropic — agents that drive a desktop
- Building Browser Agents — web automation
Retrieval gets its own section: RAG.
- Agent Design Patterns — the recurring architectures
- Multi-Agent Use — coordinating several agents
- Multi-Agent Systems — collaboration and delegation
The step most people skip, and the reason most agents fail.
- Building and Evaluating Agents — construction plus measurement
- Evaluating AI Agents — how to know if your agent actually works
- Improving LLM Accuracy — prompting, tuning, grounding
- LLMOps — running LLM systems in production
- Berkeley LLM Agents MOOC — the foundational semester
- Berkeley Advanced LLM Agents MOOC — the follow-up
Also relevant: agent books · agent papers · agent repos · guides & whitepapers
Giving a model access to knowledge it wasn't trained on. Work through in order.
- What is RAG — the concept in plain terms
- How to use RAG — the practical version
- RAG from Scratch — build it yourself, no framework
- CMU Advanced NLP: RAG — the academic treatment
- Stanford Transformers V3: RAG — retrieval in the transformer context
| Paper | Why it matters |
|---|---|
| Retrieval-Augmented Generation (Lewis et al.) | The original RAG paper |
| RAG for LLMs: A Survey | The map of the whole landscape |
| Self-RAG | Models that decide when to retrieve |
| Corrective RAG | Recovering when retrieval returns junk |
- RAG Techniques — implementations of every major variant
- Awesome RAG — curated collection
- Awesome RAG (alternate) — a second, differently-scoped list
- Need the basics first? — start here and come back
- For weekly coverage of AI engineering and research, see Newsletters — Gradient Ascent is the closest fit
Getting a model out of a notebook and keeping it working.
| Topic | Resource |
|---|---|
| CI/CD | Continuous Integration & Deployment — automated testing and release |
| Model versioning | Model Versioning & Registry — managing versions and artefacts |
| Experiment tracking | MLflow / Weights & Biases — track runs and hyperparameters |
| Data versioning | DVC — version control for datasets |
| Monitoring | Monitoring & Drift Detection — catch degradation early |
| Topic | Resource |
|---|---|
| Data pipelines | ETL / ELT |
| Feature stores | Feast / Tecton |
| Packaging | Docker / ONNX |
| Deployment | Batch / real-time / edge |
| Orchestration | Airflow / Prefect / Kubeflow |
| Observability | Prometheus / Grafana |
- Batch vs online inference — choosing the right pattern
- Shadow / canary / blue-green deployments — shipping without breaking things
- Retraining & continuous learning — keeping models current
- Feedback loops & drift correction — handling degradation
For when the bottleneck is the hardware and you want to write the kernel yourself.
The book
- Programming Massively Parallel Processors — Kirk & Hwu. The standard text. Start and mostly finish here.
Videos
- CUDA Crash Course — NotesByNick. Real kernels, real performance
- NVIDIA GTC on-demand — filter to intermediate to skip the sales talks
Practice
- LeetGPU — kernel challenges in the browser, no GPU needed
- GPU programming exercises — where a lot of engineers start
- Progressive CUDA exercises — graduated difficulty, not toy problems
- OpenCV — the classical CV library, still essential
- PyImageSearch — practical tutorials with working code
- Papers: ResNet · Vision Transformer
- spaCy — industrial-strength NLP pipelines
- NLTK — the classic teaching toolkit
- Hugging Face Transformers — the modern default for anything pretrained
- Book: Speech and Language Processing (free)
- Reinforcement Learning: An Introduction — Sutton & Barto, free. The textbook
- Spinning Up in Deep RL — OpenAI's practical companion to the theory
- Gymnasium — the maintained successor to OpenAI Gym
- Stable-Baselines3 — reliable implementations of standard algorithms
- Papers: DQN · PPO
- In this repo:
reference/reinforcement-learning-notes.pdf
| Tool | Use it for |
|---|---|
| Google Colab | Free notebooks with GPU access — no install needed |
| Jupyter | The local notebook standard |
| VS Code | Editing, debugging, notebooks in one place |
| Tool | Use it for |
|---|---|
| PyTorch | Research and most new work. The default in this repo |
| TensorFlow | Production pipelines and mobile/edge deployment |
| Keras | High-level model building on top of either |
| scikit-learn | Everything that isn't a neural network |
| Tool | Use it for |
|---|---|
| pandas | Tabular data manipulation |
| NumPy | Numerical computing, the layer under everything |
| Matplotlib | Plotting |
| Seaborn | Statistical plots with sane defaults |
| Tool | Use it for |
|---|---|
| Weights & Biases | Experiment tracking and comparison |
| MLflow | Open-source lifecycle management |
| DVC | Version control for data and models |
| Hugging Face Hub | Sharing and hosting models |
| Docker | Reproducible environments |
| Replicate | Deploying a model without managing infrastructure |
| Source | Best for |
|---|---|
| Kaggle Datasets | Breadth, plus notebooks showing what others did with them |
| Hugging Face Datasets | NLP, vision and multimodal, loadable in one line |
| UCI ML Repository | The classic small benchmarks |
| OpenML | Datasets with published results to compare against |
| Google Dataset Search | Finding data that isn't on the usual platforms |
| Papers with Code Datasets | Whatever a specific paper benchmarked on |
In this repo: data/iris.csv — see data/README.md.
| Platform | What you get |
|---|---|
| Kaggle | Competitions, datasets, notebooks, free GPUs |
| DrivenData | Data science competitions for social-good problems |
| HackerRank AI Track | ML-specific coding challenges |
| LeetCode | The algorithm practice interviews still test |
| Newsletter | Focus |
|---|---|
| Gradient Ascent | Weekly AI/ML news and analysis |
| DecodingML | Deep technical ML content, by Paul |
| Deep (Learning) Focus | Research trends, by Cameron |
| NeoSage | AI insights and analysis, by Shivani |
| Jam with AI | Practical applications, by Shirin and Shantanu |
| Data Hustle | Data science careers and learning, by Sai |
| Community | Best for |
|---|---|
| r/MachineLearning | Research discussion and paper threads |
| Hugging Face Forums | Transformers, datasets, practical troubleshooting |
| Kaggle Discussions | Competition tactics and what actually works |
| AI Stack Exchange | Specific technical questions with citable answers |
Contributions from society members and the wider community are welcome — new notebooks, clearer explanations, fixed links, or resources worth adding.
Read CONTRIBUTING.md for file naming, notebook conventions, and how to submit a resource. In short: fork, branch, commit, open a pull request.
Found a broken link? Open an issue.
Akhil Varanasi — Head of AI, Manipal Open Source Society Email: akhilvaranasi23@gmail.com GitHub: @AkCodes23
Released under the MIT License. External resources linked from this repository remain the property of their respective authors.
Happy learning and building.