A curated, interactive course by Dr. Rajdeep Chatterjee — from DSA foundations to applied AI.
Core Data Structures & Algorithms track — build a solid foundation in C, memory, and classic data structures before moving to applied AI.
Understand why Data Structures & Algorithms matter for real-world software and AI systems, with context on India's tech ecosystem.
Start Module →Core C programming concepts — pointers, memory, and structures — needed before diving into data structures.
Start Module →Deep dive into arrays: memory layout, operations, and interactive visualizations of sorting algorithms.
Start Module →Definitions, ADTs, applications, types of DS mapped to real apps/OS you use daily, and Big-O/Θ/Ω complexity with animated growth-rate visualizations.
Start Module →Selection, Bubble, Insertion, Merge, Heap, Quick & Radix sort — pseudocode, complexity, C code, and a fully animated bar-chart visualizer with speed control and play/pause/step/stop.
Start Module →Infix, prefix & postfix notation, step-by-step infix↔postfix/prefix conversion, expression evaluation, C code, and an animated visualizer with speed control and play/pause/stop.
Start Module →Artificial Neural Networks track — from the 1943 McCulloch–Pitts neuron to activation functions and the learning Perceptron, with interactive simulators for every concept.
The 1943 origin of neural computation: threshold logic, the inhibitory veto principle, and interactive AND/OR/NOT/NAND/XOR gate builders.
Start Module →Step, sigmoid, bipolar sigmoid, hard-limit and more — an interactive calculator/plotter plus worked numerical examples.
Start Module →Single-input perceptron rigor, demerits, learning-algorithm types, and an animated multi-layer forward pass alongside the interactive OR-classifier trainer.
Start Module →The Widrow–Hoff LMS/delta rule, gradient-descent derivation, an interactive ADALINE trainer with live MSE plot, and the MADALINE multi-unit network that solves XOR.
Start Module →