A comprehensive, structured learning path for mastering AI engineering from fundamentals to advanced applications.
This repository contains a complete AI learning roadmap divided into 10 phases, each building upon the previous to create a solid foundation in AI engineering. The curriculum is designed to take you from basic concepts to advanced AI applications and production deployment.
Status: ✅ COMPLETED
- Mathematics for AI: Linear Algebra, Calculus, Probability & Statistics
- Python Programming: Core Python, Advanced Python, Data Science Libraries
- Data Analysis: EDA, Visualization, Statistical Analysis
- Projects: ThinkBoard Analytics Dashboard, Linear Algebra Operations, Gradient Descent Simulation
- Certification: IBM Data Science Professional Certificate
Status: 🚧 IN PROGRESS
- Supervised Learning: Linear/Logistic Regression, Decision Trees, SVM, Random Forest
- Unsupervised Learning: K-Means, PCA, Hierarchical Clustering
- Model Evaluation: Cross-validation, Metrics, Hyperparameter Tuning
- Projects: House Price Prediction, Customer Segmentation, Loan Default Classification
- Certification: Machine Learning Specialization (Coursera)
Status: 📋 PLANNED
- Neural Networks: MLP, CNN, RNN, LSTM, GRU
- Advanced Architectures: Transformers, BERT, GPT
- Computer Vision: Object Detection, Image Segmentation
- NLP: Text Processing, Language Models, Sentiment Analysis
- Projects: Custom Image Classifier, Handwritten Digit Recognizer, Text Sentiment Classifier
- Certification: Deep Learning Specialization (Coursera)
Status: 📋 PLANNED
- NLP: Text preprocessing, transformer models, sentiment analysis
- Computer Vision: Image processing, object detection, segmentation
- Advanced Techniques: BERT, GPT, YOLO, Mask R-CNN
- Projects: Face Mask Detector, News Article Classifier, OCR App
- Certification: NLP & Computer Vision Specializations
Status: 📋 PLANNED
- Generative AI: LLMs, Transformers, Diffusion Models, VAEs, GANs
- RAG & Vector Databases: LangChain, LlamaIndex, ChromaDB, Weaviate
- Agentic AI: LangGraph, memory management, tracing
- Specialization Tracks: Computer Vision, NLP, Multimodal AI
- Projects: 25 projects across mini, main, and specialization tracks
- Certification: AI Specialization Certifications
Status: 📋 PLANNED
- System Design: OOPS principles, design patterns, scalability
- Reinforcement Learning: Q-learning, DQN, A3C, PPO
- Explainable AI: SHAP, LIME, model interpretability
- Projects: RL Game Agent, XAI Dashboard, System Design Documentation
- Certification: System Design & RL/XAI Certifications
Status: 📋 PLANNED
- RAG Architecture: Document processing, embedding generation, retrieval
- Vector Databases: FAISS, ChromaDB, Pinecone, Weaviate
- Advanced RAG: Multi-modal RAG, hybrid search, reranking
- Projects: PDF Chatbot, AI Knowledge Base Bot, Custom RAG System
- Certification: Vector Database & RAG Certifications
Status: 📋 PLANNED
- MLOps: Model serving, CI/CD, experiment tracking, monitoring
- Time Series: ARIMA, LSTM, Prophet, forecasting pipelines
- Deployment: Docker, Kubernetes, FastAPI, MLflow
- Projects: End-to-End MLOps Pipeline, Real-time Forecasting System
- Certification: MLOps & Time Series Certifications
Status: 📋 PLANNED
- Cloud Platforms: AWS, Azure, GCP deployment
- Data Engineering: ETL pipelines, big data technologies
- Scalability: Microservices, distributed systems
- Projects: Cloud ML Platform, Data Pipeline System
- Certification: Cloud & Data Engineering Certifications
Status: 📋 PLANNED
- Portfolio Development: Showcase projects, documentation
- Internship Preparation: Industry-ready skills, interview prep
- Advanced Projects: Research-level implementations
- Career Development: Networking, job applications
- Certification: Portfolio & Career Certifications
- ThinkBoard: Data Analytics Dashboard (Deployed on Railway)
- Linear Algebra Operations: Vector and matrix operations
- Gradient Descent Simulation: Optimization visualization
- Universal Favicon: Cross-browser compatibility
- Phase 2: Machine Learning & Data Science Fundamentals
- Advanced ML algorithms and techniques
- Model evaluation and validation
- Phase 3-10: Deep Learning, AI Specialization, System Design, MLOps, Cloud Deployment
- Advanced AI applications and research
- Industry-specific solutions
- Python: Core programming language
- NumPy: Numerical computing
- Pandas: Data manipulation
- Matplotlib/Seaborn: Data visualization
- Flask: Web framework
- Streamlit: Data app framework
- Git/GitHub: Version control
- Scikit-learn: Machine learning
- TensorFlow/PyTorch: Deep learning
- Hugging Face: NLP models
- LangChain/LlamaIndex: RAG frameworks
- MLflow: Experiment tracking
- Kubernetes: Container orchestration
- AWS/Azure/GCP: Cloud platforms
Learning-AI/
├── PHASE_1_Foundational_Core/ # ✅ Completed
│ ├── Projects/
│ │ ├── ThinkBoard/ # Data Analytics Dashboard
│ │ └── Mini_Projects/
│ │ ├── Linear_Algebra_Operations/
│ │ └── Gradient_Descent_Simulation/
│ └── README.md
├── PHASE_2_ML_Data_Science_Fundamentals/ # 🚧 In Progress
│ └── README.md
├── PHASE_3_Deep_Learning/ # 📋 Planned
│ └── README.md
├── PHASE_4_MLOps_Production/ # 📋 Planned (NLP & Computer Vision)
│ └── README.md
├── PHASE_5_AI_Specialization/ # 📋 Planned
│ └── README.md
├── PHASE_6_Research_Innovation/ # 📋 Planned (System Design & RL/XAI)
│ └── README.md
├── PHASE_7_RAG_Vector_Databases/ # 📋 Planned
│ └── README.md
├── PHASE_8_MLOps_Time_Series/ # 📋 Planned
│ └── README.md
├── PHASE_9_Cloud_Deployment_Data_Engineering/ # 📋 Planned
│ └── README.md
├── PHASE_10_Portfolio_Internship_Projects/ # 📋 Planned
│ └── README.md
└── README.md # This file
- Foundation (Phase 1): Mathematics, Python, Data Analysis
- ML Fundamentals (Phase 2): Algorithms, Model Evaluation
- Deep Learning (Phase 3): Neural Networks, Advanced AI
- NLP & Computer Vision (Phase 4): Text & Image Processing
- AI Specialization (Phase 5): Generative AI, RAG, Agentic AI
- System Design (Phase 6): Scalable Systems, RL, XAI
- RAG & Vector DBs (Phase 7): Document AI, Vector Search
- MLOps & Time Series (Phase 8): Production ML, Forecasting
- Cloud & Data Engineering (Phase 9): Scalable Deployment
- Portfolio & Career (Phase 10): Industry Readiness
- Phase 1: IBM Data Science Professional Certificate
- Phase 2: Machine Learning Specialization (Coursera)
- Phase 3: Deep Learning Specialization (Coursera)
- Phase 4: NLP & Computer Vision Specializations
- Phase 5: AI Specialization Certifications
- Phase 6: System Design & RL/XAI Certifications
- Phase 7: Vector Database & RAG Certifications
- Phase 8: MLOps & Time Series Certifications
- Phase 9: Cloud & Data Engineering Certifications
- Phase 10: Portfolio & Career Certifications
- ✅ Phase 1: 100% Complete
- 🚧 Phase 2: 0% Complete (Starting)
- 📋 Phase 3: 0% Complete (Planned)
- 📋 Phase 4: 0% Complete (Planned)
- 📋 Phase 5: 0% Complete (Planned)
- 📋 Phase 6: 0% Complete (Planned)
- 📋 Phase 7: 0% Complete (Planned)
- 📋 Phase 8: 0% Complete (Planned)
- 📋 Phase 9: 0% Complete (Planned)
- 📋 Phase 10: 0% Complete (Planned)
- Structured Learning: Step-by-step progression across 10 phases
- Project-Based: Hands-on experience with real projects
- Certification Path: Industry-recognized credentials
- Production Ready: Deployable applications
- Cross-Platform: Universal compatibility
- Community Driven: Open-source contributions
- Free & Open Source: All tools and models are free/open-source
- Start with Phase 1: Complete the foundational core
- Follow the timeline: Each phase has a structured learning path
- Build projects: Hands-on experience is crucial
- Get certified: Validate your skills with certifications
- Contribute: Share your projects and improvements
- Issues: Report bugs or request features
- Discussions: Ask questions and share insights
- Contributions: Welcome improvements and additions
This project is licensed under the MIT License - see the LICENSE file for details.
🎯 Goal: Complete AI Engineering Mastery from Fundamentals to Advanced Applications
⏱️ Timeline: 10 phases with flexible duration
🏆 Outcome: Full-stack AI engineer with production-ready skills