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The-Ultimate-ML-Course
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# The Ultimate ML Course <p align="center"> <img src="images/TCC-logos.jpeg" width="200"/> </p> <p align="center"> <strong>A comprehensive, production-ready machine learning journey from fundamentals to enterprise deployment</strong> </p> <p align="center"> <img src="https://img.shields.io/badge/Python-3.12+-blue.svg" alt="Python Version"/> <img src="https://img.shields.io/badge/PyTorch-2.9+-EE4C2C.svg" alt="PyTorch"/> <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License"/> <img src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg" alt="PRs Welcome"/> </p> --- ## Table of Contents - [Overview](#overview) - [Key Highlights](#key-highlights) - [Repository Structure](#repository-structure) - [Technologies & Frameworks](#technologies--frameworks) - [Learning Path](#learning-path) - [Getting Started](#getting-started) - [Featured Projects](#featured-projects) - [Acknowledgments](#acknowledgments) --- ## Overview This repository contains a comprehensive collection of machine learning implementations, tutorials, and production-ready examples spanning the entire ML/AI stack. From foundational statistics to cutting-edge LLM fine-tuning and production deployment on Kubernetes, this course demonstrates full-stack ML engineering expertise. **What makes this special:** - **150+ hands-on notebooks** covering theory and implementation - **Production-ready examples** with Docker, Kubernetes, and cloud deployment - **Modern AI topics** including LLMs, RAG systems, diffusion models, and RLHF - **From scratch implementations** to understand core algorithms - **Enterprise MLOps** practices with MLflow, KServe, and AWS SageMaker This repository corresponds to [The Ultimate Machine Learning Course](https://thecuriouscurator.in/course/ultimate-machine-learning-course-recordings-only/) offered by **The Curious Curator**. --- ## Key Highlights ### Deep Learning Architectures - **9+ CNN architectures** from scratch: LeNet, AlexNet, VGG, GoogLeNet, ResNet, DenseNet - **RNN variants**: Character-level models, Seq2Seq, attention mechanisms - **Image Captioning**: CNN+LSTM multimodal architecture - **Distributed Training**: Multi-GPU with DDP, mixed precision (AMP), CUDA optimization ### Generative AI - **Diffusion Models**: Training, sampling, conditional generation, fast inference - **GANs**: Vanilla, DCGAN, Conditional GAN, CycleGAN - **Variational Autoencoders**: Latent space modeling - **Transformers**: From scratch implementations, HuggingFace integration ### LLMs & Agent Systems - **RAG from Scratch**: 18+ notebooks covering query translation, routing, indexing, retrieval - **LangGraph Academy**: Complete agent framework (24 modules) with memory, human-in-the-loop, sub-graphs - **Multimodal LLMs**: Gemini API, ColPali document understanding ### Reinforcement Learning - **Bandit Algorithms**: On/off-policy learning, OPE with real-world ZOZOTOWN data - **Policy Gradient**: REINFORCE, PPO, A2C with Stable Baselines 3 - **Value-Based**: DQN, SAC, HER (Hindsight Experience Replay) - **Hyperparameter Tuning**: Optuna integration for RL algorithms - **Unity ML-Agents**: Training environments and executables ### Probabilistic Programming - **15+ Pyro tutorials**: Bayesian Data Analysis, MCMC, NUTS sampler - **Advanced Topics**: Causal inference, multilevel models, Gaussian processes, measurement error - **Model Comparison**: Information criteria, regularization techniques ### Production & MLOps - **Model Serving**: MLflow, KServe, TorchServe, ONNX export - **Kubernetes Deployment**: Complete EKS setup with automated scripts - **AWS SageMaker**: Training pipelines, real-time endpoints, batch transform, autoscaling ### Classical ML - **Comprehensive Coverage**: Classification, regression, clustering, ensemble methods - **From Scratch**: Vanilla Python implementations before scikit-learn - **Statistical Tests as Linear Models**: Deep dive into the mathematical foundations showing how t-tests, ANOVA, and other statistical tests are special cases of linear models - **A/B Testing**: Statistical methodology and practical examples - **Convex Optimization**: CVXPY tutorials for mathematical programming --- ## Repository Structure ``` UltimateMLCourse/ │ ├── 📚 Foundations │ ├── Python/ # Software engineering patterns │ ├── Numpy + Pytorch Primitives/ # Numerical computing fundamentals │ ├── DataAnalysis + Pandas/ # Data wrangling and analysis │ ├── Probability + Statistics/ # Statistical foundations │ └── statistical + tests + are + LinearModels/ # ⭐ Statistical tests as linear models (highly recommended) │ ├── 🤖 Classical Machine Learning │ ├── ML + Classical/ # Sklearn, clustering, ensembles │ ├── AB-Test/ # Experimental design │ └── Optimization + cvxpy + julia/ # Mathematical optimization │ ├── 🧠 Deep Learning │ ├── DL+Pytorch/ # CNN/RNN architectures (9+ models) │ ├── Distributed+AMP+TrainingOptimization/ # Multi-GPU training │ └── OperationalizingModels-Production/ # TorchServe, ONNX, Docker │ ├── 🎨 Generative AI │ ├── GenAI + Pytorch/Transformer/ # Transformer implementations │ ├── GenAI + Pytorch/DiffusionModel-sprite/ # Diffusion models (4 labs) │ ├── GenAI + Pytorch/GAN/ # GAN variants (4 types) │ └── GenAI + Pytorch/VariationalAutoencoders/ # VAE implementations │ ├── 🎲 Probabilistic Programming │ └── ProbabilisticProgramming + BDA (SR)/ # Pyro + Bayesian analysis (15+ notebooks) │ ├── 🎮 Interactive Systems & RL │ ├── Interactive-Systems/ # Bandits, PPO, DQN, SAC, Optuna tuning │ └── GenAI + Pytorch/Transformer/huggingface-LLM-RL/ # Unity ML-Agents environments │ ├── 🤗 LLMs, RAG & Agents │ ├── LLM-RAG-Agents/rag-from-scratch/ # RAG pipeline (18+ notebooks) │ ├── LLM-RAG-Agents/LangGraph/ # Agent frameworks (24 modules) │ └── LLM-RAG-Agents/LLMs/ # LLM fundamentals, multimodal │ ├── ☁️ Cloud & Production │ ├── AWS-DS/Sagemaker + HuggingFace/ # SageMaker workshops (4 complete) │ ├── serving/ # MLflow + KServe + EKS deployment │ └── Coder->SE/ # Software engineering best practices │ └── 🛠️ Tools & Documentation ├── Git/ # Git workflows and tutorials └── Talks/ # Conference materials ``` --- ## Technologies & Frameworks ### Core ML/AI Stack      ### LLMs & NLP    ### Production & MLOps     ### Key Libraries - **Probabilistic Programming**: Pyro, PyMC - **RL Frameworks**: Stable Baselines 3, Gymnasium - **Optimization**: CVXPY, Optuna - **Serving**: TorchServe, KServe, FastAPI - **Cloud**: SageMaker, EKS, S3 --- ## Learning Path This repository is organized as a progressive learning journey: ``` 1. Foundations (4-6 weeks) └─→ Python, NumPy, Pandas, Statistics, Statistical Tests as Linear Models ⭐ 2. Classical ML (6-8 weeks) └─→ Sklearn, Classification, Regression, Clustering, Ensembles 3. Deep Learning Fundamentals (8-10 weeks) └─→ CNNs, RNNs, Transfer Learning, Distributed Training 4. Advanced DL & Generative Models (8-10 weeks) └─→ GANs, VAEs, Diffusion, Transformers 5. Probabilistic & Causal ML (6-8 weeks) └─→ Bayesian Analysis, Pyro, MCMC, Causal Inference 6. Interactive Systems (6-8 weeks) └─→ Bandits, Policy Gradient, Model-Based RL, Hyperparameter Tuning 7. Modern LLMs & Agents (8-10 weeks) └─→ RAG Systems, LangGraph, Agent Frameworks, LLM Fine-tuning 8. Production & MLOps (6-8 weeks) └─→ Model Serving, Kubernetes, AWS, Cloud Deployment ``` **Total Duration**: ~12-18 months for comprehensive coverage --- ## Getting Started ### Prerequisites - Python 3.12 or higher - CUDA-capable GPU (recommended for deep learning modules) - Docker and Kubernetes (for deployment modules) - AWS account (optional, for cloud modules) ### Installation 1. **Clone the repository** ```bash git clone https://github.com/yourusername/UltimateMLCourse.git cd UltimateMLCourse ``` 2. **Set up Python environment** (using uv) ```bash # Install uv package manager curl -LsSf https://astral.sh/uv/install.sh | sh # Install dependencies uv sync ``` 3. **Alternative: Using pip/conda** ```bash # Create virtual environment python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # Install dependencies pip install -r requirements.txt ``` 4. **Launch Jupyter** ```bash jupyter lab ``` ### Quick Start Examples **Classical ML:** ```bash jupyter notebook "ML + Classical/S2-sklearn-classification.ipynb" ``` **Deep Learning:** ```bash jupyter notebook "DL+Pytorch/S1-from-scratch-mnist.ipynb" ``` **LLM & RAG:** ```bash jupyter notebook "LLM-RAG-Agents/rag-from-scratch/L1-overview-with-openai-api.ipynb" ``` **Production Deployment:** ```bash cd serving/L4_EKS_kserve_mlflow ./scripts/setup-eks.sh ``` --- ## Featured Projects ### Project 1: End-to-End Image Captioning **Location**: `DL+Pytorch/S9-cnn-lstm-image-captioning.ipynb` - CNN encoder + LSTM decoder architecture - Attention mechanism implementation - Custom data pipeline for COCO dataset ### Project 2: Production ML on Kubernetes **Location**: `serving/L4_EKS_kserve_mlflow/` - Complete MLOps pipeline - Model versioning with MLflow - Scalable inference with KServe on EKS - Automated deployment scripts ### Project 3: RAG System from Scratch **Location**: `LLM-RAG-Agents/rag-from-scratch/` - 18 progressive notebooks - Query translation, routing, and retrieval - Comparison of OpenAI vs open-source LLMs - RAPTOR: Recursive tree-organized retrieval ### Project 4: Diffusion Models for Image Generation **Location**: `GenAI + Pytorch/DiffusionModel-sprite/` - Training from scratch - Conditional generation with context - Fast sampling techniques - Custom sprite generation ### Project 5: Bayesian Causal Inference **Location**: `ProbabilisticProgramming + BDA (SR)/Session13a-social-relations-instrumental-variables.ipynb` - Causal DAGs and d-separation - Instrumental variables - Social network analysis - Gaussian Process regression --- ## Project Statistics - **Total Notebooks**: 150+ - **Lines of Code**: 50,000+ - **Topics Covered**: 40+ - **Production Projects**: 5 complete end-to-end systems - **Deep Learning Models**: 25+ architectures implemented - **Cloud Deployments**: AWS SageMaker, EKS, EC2 --- ## Development Tools & Best Practices This repository follows industry best practices: - **Code Quality**: Scripts for linting and quality checks - **Version Control**: Git workflows documented - **Documentation**: Inline comments and markdown explanations - **Reproducibility**: Fixed seeds, environment specifications --- ## Contributing Contributions are welcome! If you'd like to: - Fix bugs or typos - Add new tutorials or implementations - Improve documentation - Share your insights Please open an issue or submit a pull request. --- ## Acknowledgments This repository corresponds to [The Ultimate Machine Learning Course](https://thecuriouscurator.in/course/ultimate-machine-learning-course-recordings-only/) offered by **[The Curious Curator](https://thecuriouscurator.in/)**. The content represents a curated and enhanced collection of ML implementations, drawing from: - Course materials from The Curious Curator - Official PyTorch, HuggingFace, and AWS tutorials - Research papers and open-source implementations - Personal projects and production experience Special thanks to the open-source ML community for the incredible tools and frameworks that make this learning journey possible. --- ## License This project is licensed under the MIT License - see individual directories for specific attributions. --- ## Contact & Connect - **GitHub**: [Your GitHub Profile] - **LinkedIn**: [Your LinkedIn] - **Email**: [Your Email] - **Course Website**: [The Curious Curator](https://thecuriouscurator.in/) --- <p align="center"> <strong>⭐ If you find this repository helpful, please consider giving it a star! ⭐</strong> </p> <p align="center"> <sub>Built with passion for machine learning and production excellence</sub> </p>
