Data Foundations

• Numpy / Pandas
• Data frames
• EDA
• Data Plotting and visualisation basics
• Probability Foundations

ML Coding (Applied AI-ML)

Supervised learning

• Linear Regression, regression metrics and Sklearn pipelines
• Logistic Regression
• Classification Metrics
• KNN
• Decision Trees
• Bagging & Boosting
• Naive Bayes

Unsupervised learning, Time series & Recommender systems

• Clustering
• Anomlay Detection, GMM
• Dimensionality Reduction
• Time series pre-processing and decomopisiton
• Time series analysis
• Collobrative filtering & ARM
• Content based filtering

Neural network

• Neural Networks, Optimizers, Loss function, activation functions, weight intialization, regularization

Computer vision and Natural Language processing

• OpenCV
• Applied Deep Learning (CNNs, LSTM, GRU, Autoencoders, etc)
• Transfer Learning
• Advance Computer Vision - Image gen, Face detection / recognition, segmentation
• Text Pre-Processing (NLTK/spaCy), topic modelling, Sentiment Analysis
• LSTM, RNN, Attention
• Advanced NLP - Transformers / GPT

Agentic AI, Advanced MLE and applications

The Agentic AI Architect

• LLMs, Prompt Engineering & Security
• RAG Architecture & Vector Search
• Evaluation Pipelines & Observability
• AI Agents & Cognitive Architectures
• Multi-Agent Systems & Orchestration
• Model Context Protocol (MCP) & Tool Integration
• Multimodal AI: Vision, Audio & Voice Agents
• Model Fine-Tuning & Specialization
• LLMOps & Production Deployment
• Implementation-Driven: Learning with End-to-end development of real-world AI systems, covering RAG, agents, deployment, and monitoring.

MLOps

• ML lifecycle
• CI/CD pipelines
• Feature Stores for ML
• Serving ML Models
• ML Monitoring, Experiment tracking, ML flow, Docker, Optuna
• AWS Sagemaker

System design

• ML System Design fundamentals
• Microservices
• Design Patterns
• Scalability and Cost

Big Data (Recorded)

• Introduction to Big Data & Ecosystem Overview
• Distributed Systems, Hadoop & Data Processing Basics
• Apache Spark Deep Dive
• Advanced Spark + Case Study + Visualization

Reinforcement Learning (Recorded)

• Foundations of RL
• Model-Free RL
• Policy-Based RL
• Advanced Topics in RL

Advanced ML, Advanced Deep Learning, Advanced NLP

Advanced Machine learning and deep learning 1

• Prob and Stats
• Math for ML
• Optimisation Theory, Loss Functions
• Linear Models
• Logistic Regression
• Decision Tree and Forests
• Support Vector Machines
• Clustering
• Dimensionality Reduction
• Statistical Learning Theory

Advanced Machine learning and deep learning 2

• Neural Networks
• Model Interpretability
• Backpropagation
• Advanced NLP - Transformers / GPT
• Attention Mechanisms

DSA

DSA for Professionals

• Time and Space Complexity Analysis
• Array Problem Solving Patterns (Prefix, Sliding Windows, Subarrays, Subsets, Subsequences)
• Bit Manipulation
• Maths for Problem Solving
• Recursion
• Backtracking
• Sorting
• Searching (Binary Search)
• Two Pointers
• Hashing
• String Problem Solving Patterns, String Pattern Matching
• Linked Lists
• Stacks
• Queues and Deques
• Trees and BST
• Tries
• Heaps
• Greedy
• Dynamic Programming
• Graphs

Advanced AI Engineering by CEC, IIT Roorkee

• Campus Immersion
• AI in Healthcare
• LLMs for Productivity in Job