Full Stack Data Science with Agentic AI & Generative AI
What You’ll Learn
This is the most comprehensive Data Science course available — starting from Python all the way to Advanced Agentic AI. Learn Machine Learning, Deep Learning, NLP, Generative AI (LLMs, RAG), and Multi-Agent Systems together through real-world projects. Placement assistance is included.
Benefits
- Python — Advanced to Mastery
- Statistics & Probability for ML
- Machine Learning (Supervised + Unsupervised)
- Deep Learning: ANN, CNN, RNN, LSTM, Transformers
- Natural Language Processing (NLP)
- Generative AI: LLMs, Prompt Engineering, RAG
- Agentic AI: LangGraph, CrewAI, n8n
- MLOps: Docker, MLflow, AWS
- Placement Assistance
Curriculum
🐍 Module 1 — Python & Statistics Foundation
- Python: OOPs, File I/O, Error Handling, Generators, Decorators
- NumPy, Pandas, Matplotlib, Seaborn for Data Analysis
- Statistics: Descriptive Stats, Probability, Distributions
- Hypothesis Testing, A/B Testing, Confidence Intervals
🧠 Module 2 — Machine Learning
- Supervised: Linear Regression, Logistic Regression, Decision Trees, Random Forest, XGBoost
- Unsupervised: K-Means, DBSCAN, PCA, Hierarchical Clustering
- Model Evaluation: Bias-Variance, Cross-Validation, GridSearchCV
- Feature Engineering, Data Preprocessing Pipelines (sklearn)
- Projects: House Price Prediction, Customer Churn, Credit Risk
🔬 Module 3 — Deep Learning & NLP
- ANN: Forward/Backward Propagation, Activation Functions, Optimizers
- CNN: Convolutions, Pooling, Transfer Learning (VGG, ResNet, EfficientNet)
- RNN, LSTM, GRU: Sequence Models, Time-Series Forecasting
- NLP: Tokenization, Word2Vec, BERT, Text Classification, Sentiment Analysis
- Projects: Image Classifier, Stock Forecast, Movie Review Sentiment
🤖 Module 4 — Generative AI & LLMs
- Transformer Architecture, Attention Mechanism, GPT/BERT internals
- Prompt Engineering: Zero-shot, Few-shot, CoT, ReAct patterns
- Fine-tuning: LoRA, QLoRA, PEFT, RLHF techniques
- RAG Systems: ChromaDB, Pinecone, FAISS, Weaviate vector stores
- OpenAI, Gemini, Mistral, Llama APIs — practical usage
- Projects: PDF Chatbot, Code Generator, Resume Analyzer
⚡ Module 5 — Agentic AI Frameworks
- LangChain: Chains, Memory, Tools, Callbacks, Output Parsers
- LangGraph: State Management, Conditional Edges, Agent Loops
- CrewAI: Role-based Agents, Task Delegation, Multi-Agent Systems
- n8n: No-code AI Workflow Automation & Enterprise Pipelines
- Projects: AI Research Agent, Multi-Agent Hiring System
🚀 Module 6 — MLOps & Deployment
- Docker: Containerization, Docker Compose for ML apps
- MLflow: Experiment Tracking, Model Registry, Versioning
- FastAPI: REST APIs for ML models
- AWS: EC2, S3, SageMaker Basics, Lambda for serverless inference
- CI/CD for ML: GitHub Actions, Automated Testing
