3.0 Ultimate Data Science & Gen Ai
12 Months
Launch Your Data Career
Zero prerequisites, one ultimate course: go from absolute beginner to building and deploying production-ready data science and AI solutions.
This course is a comprehensive journey through modern Data Science and AI, designed to take learners from Python programming basics all the way to next-generation Agentic AI systems. You will begin by mastering Python, statistics, and core machine learning techniques, then progress into deep learning, generative AI, RAG, and model lifecycle management with MLflow. By the end of this year-long program, you will understand how the world is moving towards autonomous AI systems that reason, retrieve, and collaborate.
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No Experience Required: The curriculum begins with Python programming and basic math, ensuring that even beginners can follow along. A general curiosity about technology, problem-solving, and AI systems is enough to get started.
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A Truly Comprehensive Curriculum: Progress from core ML to Deep Learning, then master Generative AI, RAG, and Agentic AI frameworks like LangGraph and MCP. You will also learn to process big data with PySpark for distributed computing.
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Build End-to-End, Production-Ready Projects: Gain hands-on experience by building end-to-end solutions spanning data engineering, machine learning, deep learning, generative AI and Agentic AI. You will implement CI/CD pipelines using GitHub Actions and deploy agent systems via Docker and AWS.
What You Will Learn
Master the full data science lifecycle, from Python programming and statistical analysis to building and deploying next-generation Generative AI and autonomous agents.
Python & Data Analysis Toolkit
Write efficient, reusable Python code and manipulate data with NumPy, Pandas, and Streamlit. Process and analyze large-scale datasets using PySpark for distributed computing.
Statistics & Machine Learning
Use statistical reasoning to explore, interpret, and validate real-world datasets. Build, evaluate, tune, and manage regression, classification, and clustering models using MLflow.
Deep Learning & NLP
Work with RNNs, LSTMs, GRUs, Attention, and Transformer architectures (BERT, GPT). Clean, process, and represent text using TF-IDF and Word2Vec.
Generative AI & RAG
Understand Generative AI architectures and use vector databases (Chroma, Pinecone, FAISS) for embedding-based search and retrieval. Build and optimize RAG systems using LangChain and LLMs.
LangChain & LangGraph Orchestration
Create LLM workflows, agents, and chains using LangChain. Design agent architectures for research, automation, and reasoning using LangGraph and MCP.
Agentic AI & End-to-End Deployment
Understand the concepts of Agentic AI and multi-agent collaboration. Implement CI/CD pipelines using GitHub Actions and deploy agent systems via Docker and AWS.
Database Management
Manage and query data using SQL (PostgreSQL + Supabase) and NoSQL (MongoDB Atlas). Create data-driven web applications with Streamlit and FastAPI for interactive and production-ready insights.
Advanced ML & System Optimization
Implement advanced techniques like Random Forests, Gradient Boosting, and XGBoost. Integrate human feedback, memory, and RAG for adaptive AI agents.
Projects You'll Build
In this course, you'll gain hands-on experience in implementing end-to-end AI projects. Each project reinforces core concepts while giving you practical experience with CI/CD deployment, scalable pipelines, and production-ready model integration.
Project 1: Python and Data Engineering Projects
Build an End-to-End Review Scraper Project and develop a Supabase and Streamlit Database Application.
Project 2: Machine Learning Projects
Apply your machine learning skills to a critical security problem by building a Network Intrusion Detection System.
Project 3: Deep Learning Projects
Develop advanced neural network applications focusing on Text Summarization, Machine Translation, and Question Answering.
Project 4: Generative AI & RAG Projects
Implement a complete RAG Q&A System with CI/CD Integration to handle complex data retrieval and generation.
Project 5: Agentic AI Projects
Design a Multi-Agent System for Research Analysis and Generation Automation, culminating your journey into autonomous systems.
Course Curriculum
Python Foundations
- 1Overview of Python and comparison with other programming languages
- 2Python objects: Numbers, Booleans, and Strings
- 3Container objects and mutability, Operators: Arithmetic, Bitwise, Comparison, and Assignment, Operator precedence
- 4Conditional statements (if, if-elif-else), Loops (for, while), Break and continue statements, Range function
- 5String basics, inbuilt methods, splitting and joining, formatting, Basic data structures in Python: Lists, Tuples, Sets, and Dictionaries, List and Dictionary comprehensions, Dictionary view objects
- 6Function basics and parameter passing, Iterators and generator functions, Lambda functions, Map function and functional style programming
Learn from Industry Experts
Why This Course
Go from Fundamentals to Next-Gen AI Expert
Whether you are completely new to coding or looking to align your skills with cutting-edge industry requirements, this course provides a structured path from ground-up Python foundations and statistics to building, deploying, and monitoring complex autonomous AI systems.
Master the Entire Modern AI Stack
This isn't limited to a single tool. You will master a comprehensive array of core and advanced technologies: Python, NumPy, Pandas, SQL (Supabase), NoSQL (MongoDB Atlas), PySpark, FastAPI, MLflow, LangChain, and LangGraph.
Build an Enterprise-Ready Portfolio
You'll graduate with robust, end-to-end deployment projects that span data engineering, machine learning, deep learning, and generative AI. From a production-ready network intrusion detection system to a multi-agent system for research analysis automation, these projects demonstrate scalable, real-world skills.
Learn Cutting-Edge Ecosystem Standards: MCP & Advanced RAG
Stay ahead of the industry curve with dedicated modules on the Model Context Protocol (MCP) for standardized model-to-tool communication, alongside enterprise architectures like Vectorless RAG (PageIndex) and LLM Gateways (Bifrost) for automated structured fallbacks.
Master Production-Ready Deployment & MLOps
Learn how production AI models are shipped and managed. Master the end-to-end lifecycle using MLflow for experiment tracking, build robust APIs with FastAPI, implement automated CI/CD pipelines with GitHub Actions, and containerize systems using Docker for AWS deployment.
Build Safe, Reliable & Evaluated AI Systems
Ensure your autonomous systems operate with trust and precision. This course teaches you enterprise-level observability and testing using the RAG Triad, LLM-as-a-Judge, and advanced tracing tools like LangSmith and Opik AI to analyze intermediate outputs and manage feedback loops.
Skills You Will Acquire
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