- E2E Data Science and Machine Learning
This comprehensive AI & Data Science Mastery Program covers Python, SQL, Machine Learning, Deep Learning, Generative AI, LLMs, and Agentic AI. Students learn end-to-end model building, deployment, and real-world project development, gaining hands-on experience with modern AI tools to become industry-ready data and AI professionals.
Course Info
Module 1: Foundational Tools & Data Handling
This module builds strong foundations in spreadsheets, database systems, and business intelligence tools. Students learn how to collect, clean, manipulate, and visualize data using Excel, SQL, NoSQL, and Power BI.
Includes:
MS Excel basics + advanced functions for data cleaning
SQL queries, joins, data manipulation
NoSQL & vector database fundamentals
Power BI dashboards and data visualization
Module 2: Python Programming & Data Science
Students learn Python from scratch and progress to using essential data science libraries. This module enables them to structure, analyze, and visualize data efficiently using industry-standard tools.
Includes:
Python basics: syntax, loops, functions
Data structures: lists, tuples, sets, dictionaries
Error handling, file I/O
NumPy for numerical computing
Pandas, Matplotlib, Seaborn for data analysis & visualization
Module 3: Mathematics & Statistics for AI
A strong mathematical foundation for building AI/ML models. This module covers probability, statistics, calculus, and linear algebra concepts that power machine learning algorithms and data interpretation.
Includes:
Probability, distributions, statistical measures
Linear algebra basics (vectors, matrices)
Calculus essentials for ML
Hypothesis testing & statistical inference
Module 4: Core Machine Learning
Students learn the complete ML pipeline—from understanding algorithms to evaluating and tuning models. Covers both supervised and unsupervised learning with hands-on implementation.
Includes:
Supervised Learning:
Linear & multiple regression
Logistic regression & classification
Decision trees, random forests, boosting
Evaluation techniques: cross-validation, metrics
Unsupervised Learning:
Clustering techniques & PCA
SVM and K-Nearest Neighbors
Dimensionality reduction
Advanced Topics:
Time series forecasting (ARIMA, SARIMA)
Introduction to Reinforcement Learning
Module 5: Deep Learning & NLP
Introduces neural networks, deep learning frameworks, and natural language processing. Students explore advanced architectures, embeddings, transformers, and basics of computer vision.
Includes:
Deep Learning:
Neural network architectures (CNNs, RNNs)
TensorFlow & PyTorch frameworks
NLP:
NLP fundamentals
Text preprocessing, embeddings
Transformers & attention mechanisms
Computer vision essentials
Module 6: Generative AI & LLMs
This module unlocks the world of modern AI—transformers, LLMs, prompt engineering, GANs, and diffusion models. Students learn how to build, fine-tune, and evaluate generative systems.
Includes:
Introduction to Generative AI, LLMs, transformers
Prompt engineering & no-code AI tools
GANs & diffusion models
Fine-tuning LLMs (PEFT, LoRA)
Evaluating hallucination, bias & safety
Module 7: Agentic AI & Project Development
Students learn to build AI Agents using modern frameworks. They understand autonomous tool use, reasoning patterns, multi-agent collaboration, and building RAG-enabled intelligent systems.
Includes:
AI agent architecture & RAG
LangChain, CrewAI, Autogen frameworks
Planning, reasoning & ReAct pattern
Single/multi-agent workflows & collaboration
Module 8: End-to-End Projects & Deployment
You deploy real applications and handle full lifecycle MLOps. Students complete two large projects—one in ML/DL and another in Generative/Agentic AI.
Includes:
Building real apps (e.g., chatbot with Gradio)
Deployment using LangServe, AWS, Azure, GCP
ML/DL industry project
GenAI/Agentic AI capstone project
Responsible & ethical AI guidelines
Material Includes
Learners will receive:
Comprehensive eBooks, PDFs, and module-wise study guides
Hands-on notebooks (Python, ML, DL, NLP, GenAI)
Real datasets for practice and projects
Access to coding assignments & practical labs
Industry-level case studies and real-world examples
Video lectures and recorded sessions
Free access to GenAI tools, LLM playgrounds, and no-code AI platforms
Pre-built templates for MLOps, deployment, and agent-based systems
Source code for all end-to-end projects
Certification upon completion of final assessments
Requirements
To enroll in this course, learners should have:
Basic computer operation skills (Windows/Mac/Linux)
A laptop with minimum specifications:
8 GB RAM (16 GB recommended)
i5/Ryzen 5 or equivalent processor
Stable internet connection
No prior programming or mathematics experience is compulsory—everything is taught from scratch
Optional but useful:
Basic understanding of logical reasoning or school-level math
Curiosity about technology, automation, and AI