Python Data Science, Generative AI & Agentic AI Complete Course
Python • Data Science • Machine Learning • Deep Learning • LLMs • RAG • Agentic AI & AI Agents
Progress from Python programming and data analysis to machine learning, deep learning, LLM applications, RAG systems, AI agents, multi-agent workflows, and production-ready agentic architectures.
What You Will Master in 12 Weeks
Progress from Python programming and data analysis to machine learning, deep learning, LLM applications, RAG systems, AI agents, multi-agent workflows, and production-ready agentic architectures.
Program Highlights
Daily & Weekly Learning Routine
- Daily 90-minute structured classes with live interactive demonstrations
- Daily hands-on coding challenges in Jupyter and VS Code
- Weekly twice Show & Tell architecture defense sessions
- Weekly doubt-clearing and personalized mentoring
- Monthly mini projects and final multi-agent capstone presentation
Technologies & Core Competencies
Targeted industry stack covered through live coding, architecture blueprints, and dedicated sandbox labs.
Core Competencies Covered
Enterprise Tools & Platforms
Comprehensive 12-Week Curriculum
Exact, comprehensive weekly topics taken directly from official CES program specifications. Every week features hands-on assignments and code reviews.
Python Programming for Data Science
Mastering Python syntax, object-oriented concepts, and scientific programming patterns
- Python syntax, dynamic typing, operators, and string formatting
- Collections: lists, tuples, dictionaries, and sets with comprehensions
- Functions, lambda expressions, map, filter, *args, **kwargs, and closures
- Object-oriented Python: classes, inheritance, dunder methods, and dataclasses
- Exception handling, file handling (JSON, CSV), and path operations with pathlib
- Virtual environments and package management with pip
Automated Data Ingestion & Sanitization Pipeline in Pure Python
NumPy & Pandas for Exploratory Data Analysis (EDA)
Transforming, cleaning, and visualizing structured datasets with NumPy, Pandas, and Seaborn
- NumPy arrays, broadcasting, vectorization, slicing, and mathematical operations
- Pandas Series and DataFrame structures: indexing, selecting, and filtering
- Handling missing data, duplicate records, type casting, and string manipulations
- Data transformation: groupby, aggregations, pivot tables, merging, and joining
- Data visualization: Matplotlib plots, Seaborn statistical distributions, and heatmaps
Comprehensive Exploratory Data Analysis & Visual Dashboard on Global E-Commerce Data
Statistics, Probability & SQL for Data Science
Applying statistical concepts and SQL querying to extract business intelligence
- Descriptive statistics: mean, median, mode, variance, standard deviation, and IQR
- Probability distributions: Normal, Binomial, Poisson, and Central Limit Theorem
- Inferential statistics: hypothesis testing, p-values, t-tests, and confidence intervals
- SQL for data science: aggregations, joins, window functions (RANK, LEAD, LAG)
- Feature engineering: scaling (MinMax, Standard), encoding (One-Hot, Target), and outlier handling
Statistical A/B Testing Analysis & Feature Engineering Pipeline
Supervised Machine Learning & Model Evaluation
Training, evaluating, and tuning classical machine learning models with Scikit-Learn
- Machine learning workflow: train-test split, cross-validation, and data leakage prevention
- Regression algorithms: Linear Regression, Ridge, Lasso, and evaluation metrics (MSE, RMSE, R2)
- Classification algorithms: Logistic Regression, Decision Trees, Random Forests, and XGBoost
- Evaluation metrics: Confusion Matrix, Precision, Recall, F1-Score, and ROC-AUC curve
- Hyperparameter optimization using GridSearchCV and RandomizedSearchCV
Production Customer Churn Prediction Model with Complete Hyperparameter Tuning
Unsupervised Learning & Deep Learning Foundations
Discovering hidden data patterns and building neural networks with PyTorch
- Unsupervised learning: K-Means clustering, hierarchical clustering, and silhouette scores
- Dimensionality reduction: Principal Component Analysis (PCA) and t-SNE visualization
- Neural network foundations: perceptron, activation functions (ReLU, Sigmoid, Softmax)
- Feedforward neural networks, loss functions, backpropagation, and gradient descent
- PyTorch foundations: tensors, autograd, building nn.Module architectures, and training loops
Customer Segmentation System with PCA and PyTorch Deep Learning Classifier
Generative AI Foundations & LLM Architecture
Understanding Transformer architectures, foundation models, and prompt engineering principles
- From RNNs and sequence models to the Transformer revolution (Attention Is All You Need)
- Self-attention mechanisms, multi-head attention, positional encodings, and encoder-decoder designs
- Foundation models landscape: GPT-4, Claude, LLaMA 3, Mistral, and open-source models
- Tokenization, vocabulary, context window limitations, and temperature parameters
- Prompt engineering techniques: zero-shot, few-shot, chain-of-thought (CoT), and role prompting
Interactive Multi-Persona Contextual LLM Assistant with Structured Output Enforcement
Text Embeddings & Vector Databases
Generating semantic embeddings and managing high-dimensional vector search engines
- Dense vector representations vs sparse keyword search (BM25 vs embeddings)
- Embedding models: text-embedding-3, BGE, and sentence-transformers
- Distance metrics: Cosine Similarity, Euclidean Distance, and Dot Product
- Vector database architecture: indexing algorithms (HNSW, IVF), metadata filtering
- Hands-on with vector databases: ChromaDB, Pinecone, and FAISS vector index
High-Performance Semantic Code & Documentation Search Engine with ChromaDB
Production RAG (Retrieval-Augmented Generation)
Building enterprise knowledge retrieval engines with LangChain and LlamaIndex
- RAG architectural pipeline: ingestion, chunking, indexing, retrieval, generation
- Document chunking strategies: fixed-size, recursive character, semantic, and document-specific
- Advanced retrieval techniques: hybrid search (dense + sparse), contextual compression, reranking
- Building RAG workflows with LangChain Expression Language (LCEL) and LlamaIndex query engines
- RAG evaluation frameworks: Ragas, TruLens, measuring faithfulness, answer relevancy, and context recall
Enterprise Legal & Financial Document Q&A RAG Engine with Contextual Re-Ranking
Agentic AI Foundations & Tool Use
Transitioning from passive LLMs to autonomous AI agents capable of planning and action
- Agentic AI definition: autonomy, decision-making, observation, and goal-directed actions
- The ReAct framework: Reasoning + Acting loop in autonomous execution
- Tool calling and function calling: JSON schemas, arguments extraction, and execution validation
- Equipping agents with tools: web search (Tavily), database querying (SQL), and Python code interpreters
- Agent memory architectures: short-term buffer, summary memory, and vector-backed long-term memory
Autonomous Data Analyst AI Agent Capable of Querying SQL and Plotting Charts
Stateful Graphs & Multi-Agent Orchestration
Designing collaborative multi-agent systems using LangGraph and CrewAI
- Limitations of linear agent chains and the need for stateful cyclic graphs
- LangGraph architecture: StateGraph, Nodes, Edges, Conditional branching, and checkpointers
- Human-in-the-loop patterns: review, approval, and state interruption in LangGraph
- CrewAI framework: defining Agents, Tasks, Tools, and Process management (sequential, hierarchical)
- Multi-agent collaboration patterns: Supervisor-Worker, Peer-to-Peer, and Consensus building
Collaborative Multi-Agent Software Development Team with LangGraph and CrewAI
Agent Safety, Production Deployment & MLOps
Hardening autonomous agents for production and packaging them as containerized APIs
- AI safety and guardrails: input moderation, prompt injection defense, and output sanitization (NeMo Guardrails)
- Error handling, infinite loop detection, and fallback strategies in agent execution
- Serving agent workflows as high-performance REST APIs with FastAPI and WebSockets for streaming
- Observability and tracing: LangSmith, Langfuse, tracking token costs, latency, and step-by-step traces
- Packaging and containerizing agent services using Docker
Production-Grade Streaming Agent API with FastAPI, LangSmith Tracing, and Docker
Enterprise Agentic Capstone Project & Portfolio Defense
Delivering a comprehensive multi-agent enterprise product and conducting technical defense
- End-to-end integration of capstone multi-agent market intelligence platform
- Benchmarking agent execution: task completion rate, cost per query, and latency optimization
- Publishing open-source portfolio repositories, architectural C4 diagrams, and demo videos
- Comprehensive technical mock interview covering ML, LLMs, RAG, and Agentic AI system design
- Live Show & Tell capstone defense before senior AI research and engineering directors
Autonomous Financial Market Research & Investment Analysis Multi-Agent Platform
Industry Projects & Capstone Deliverables
Graduation requires building and presenting an enterprise portfolio project following the standard software development lifecycle.
Autonomous Financial Market Research Multi-Agent System
Collaborative multi-agent system with researcher, analyst, and writer agents using LangGraph and CrewAI to fetch market data, execute tools, and generate synthesized reports.
Enterprise Knowledge Base RAG Assistant
Production retrieval-augmented generation engine with hybrid dense-sparse search, contextual reranking, conversation history, and hallucination guardrails.
Who Can Join This Program
- Developers and tech professionals aiming to lead the frontier of Generative AI and autonomous Agentic AI systems
- Data analysts and software engineers looking to build intelligent multi-agent systems and enterprise RAG
- Graduates in engineering, CS, math, or sciences wanting a comprehensive portfolio in modern AI
Laptop & System Requirements
- Basic understanding of programming logic and mathematics
- Curiosity about machine learning, generative models, and autonomous AI agents
- Broadband internet connection for cloud model API access
Target Career Opportunities & Preparation
Target Job Roles
Interview Preparation Structure
- Data science mathematical foundations: linear algebra, gradient descent, bias-variance tradeoff
- LLM mechanics: self-attention, context windows, tokenization, hallucinations, and temperature
- RAG architectural patterns: chunking strategies, dense vs sparse retrieval, reranking, and evaluation
- Agentic patterns: ReAct prompting, tool use, memory architectures, graph state management, and guardrails
- Live mock interviews, technical architecture defense, and portfolio GitHub review
Questions About This Program
How is this course different from traditional AI/ML courses?
This course goes far beyond standard machine learning by dedicating extensive curriculum to Generative AI, Retrieval-Augmented Generation (RAG), and cutting-edge Agentic AI, where autonomous agents utilize tools, plan tasks, and collaborate in multi-agent workflows.
Do I need an expensive high-end GPU for this course?
No. While modern AI models are covered in depth, course labs utilize cloud model APIs (OpenAI, Claude, open-source via Hugging Face) and cloud Jupyter sandboxes, allowing you to build and test agents on standard modern laptops.
Ready to Begin the Python Data Science, Generative AI & Agentic AI Complete Course?
Register your profile for skill baseline evaluation or speak directly with an academic advisor via WhatsApp to confirm the next batch schedule.