DSA and GENAI Course

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Description

This is a Live course of Data structure and Algorithm and GENAI.
 

Course start Date: 1 Dec 2025
Class Timing: Mon-fri (9pm) Live
Recording will be available after the class
DSA course: C++
GenAI course: Javascript

 

Data Structures and Algorithms in C++: From Beginner to Advanced

Course Description: Welcome to the ultimate guide to Data Structures and Algorithms (DSA) in C++! This comprehensive course is designed to take you from the basic principles of programming to a level of proficiency where you can confidently solve complex computational problems. We will start with a solid foundation in C++, explore fundamental and advanced data structures, master key algorithmic paradigms, and even touch upon modern applications. By the end of this course, you will not only understand the theory but also possess the practical skills to implement and analyze efficient solutions in C++. We will solve 300 problems

Course Philosophy:

  • Concept First: We focus on understanding the "why" behind each data structure and algorithm.
  • Implementation Focused: Every concept will be backed by a clean, well-explained C++ implementation.
  • Problem Solving: We will solve numerous problems to solidify understanding and prepare you for technical interviews and real-world challenges.

Detailed Course Syllabus

Module 1: C++ Foundations for DSA

This module ensures you have the necessary C++ fundamentals before we dive into complex algorithms.

  • 1.1. Introduction to C++ & Course Setup
    • Setting up your development environment (Compiler, IDE like VS Code).
    • Writing your first program: "Hello, World!"
    • Compilation and Execution process.
  • 1.2. Variables, Data Types & I/O
    • Primitive Data Types (int, char, float, double, bool).
    • Input/Output streams (cin, cout).
  • 1.3. Operators, Conditionals & Loops
    • Arithmetic, Relational, Logical, and Bitwise Operators.
    • if, else if, else, switch-case.
    • for, while, do-while loops.
  • 1.4. Functions in C++
    • Function declaration, definition, and calling.
    • Parameters and return values.
    • Pass by Value vs. Pass by Reference.
  • 1.5. Pointers & Dynamic Memory
    • Understanding memory addresses and the & and operators.
    • Pointers and Arrays.
    • Dynamic Memory Allocation (new and delete keywords).

Module 2: Core Concepts of Algorithmic Analysis

Here, we build the theoretical framework for analyzing and comparing algorithms.

  • 2.1. Time & Space Complexity
    • Measuring algorithm efficiency.
    • Asymptotic Notations: Big O, Big Omega (Ω), and Big Theta (Θ).
    • Analyzing iterative and recursive code.
    • Best, Average, and Worst-case analysis.
  • 2.2. Recursion
    • The concept of recursion and the call stack.
    • Writing base cases and recursive relations.
    • Solving problems like Factorial, Fibonacci Series, and Tower of Hanoi.

Module 3: Basic Data Structures & Algorithms

We begin our DSA journey with the most fundamental structures and algorithms.

  • 3.1. Arrays & Dynamic Arrays
    • Static vs. Dynamic Arrays.
    • Implementing a Dynamic Array (Vector).
    • Common array operations and problems.
  • 3.2. Strings
    • C-style strings vs. C++ std::string.
    • String manipulation and common string problems.
  • 3.3. Searching Algorithms
    • Linear Search (Time Complexity: O(n)).
    • Binary Search (Time Complexity: O(log n)) - Iterative and Recursive.
  • 3.4. Sorting Algorithms
    • Basic Sorts: Bubble Sort, Selection Sort, Insertion Sort (O(n²)).
    • Efficient Sorts (using Divide & Conquer): Merge Sort, Quick Sort (O(n log n)).
    • In-depth analysis of Quick Sort (pivoting strategies, worst-case).

Module 4: The C++ Standard Template Library (STL)

Master the powerful, pre-built tools in C++ to write code faster and more efficiently.

  • 4.1. Introduction to STL
    • Overview of Containers, Iterators, and Algorithms.
  • 4.2. STL Containers
    • Sequence Containers: vector, string, list, deque.
    • Container Adaptors: stack, queue, priority_queue.
    • Associative Containers: set, map, multiset, multimap.
    • Unordered Associative Containers: unordered_set, unordered_map.
  • 4.3. STL Algorithms & Functors
    • sort, binary_search, reverse, max_element, and more.

Module 5: Object-Oriented Programming (OOP) for Data Structures

Learn to design and implement your own data structures using the principles of OOP.

  • 5.1. Classes and Objects
  • 5.2. The Four Pillars of OOP
    • Encapsulation, Abstraction, Inheritance, Polymorphism.
  • 5.3. Constructors & Destructors
  • 5.4. Building Data Structures as Classes

Module 6: Linear Data Structures

Explore data structures that store data sequentially.

  • 6.1. Linked Lists
    • Singly, Doubly, and Circular Linked Lists.
    • Implementation: Insertion, Deletion, Traversal, Searching.
    • Solving common Linked List problems.
  • 6.2. Stacks
    • LIFO (Last-In, First-Out) principle.
    • Implementation using Arrays and Linked Lists.
    • Applications: Expression evaluation, balancing parentheses, etc.
  • 6.3. Queues
    • FIFO (First-In, First-Out) principle.
    • Implementation using Arrays and Linked Lists.
    • Circular Queues, Deques.
    • Applications: Schedulers, BFS traversal.

Module 7: Non-Linear Data Structures

Dive into hierarchical and network-based data structures.

  • 7.1. Trees - Core Concepts
    • Terminology: Root, Node, Edge, Height, Depth, etc.
    • Binary Trees: Representation and Traversals (In-order, Pre-order, Post-order, Level-order).
  • 7.2. Binary Search Trees (BST)
    • Properties, Searching, Insertion, Deletion.
    • Time complexity analysis.
  • 7.3. Heaps & Priority Queues
    • Binary Heaps (Min-Heap, Max-Heap).
    • Implementation: insert, extract, heapify.
    • Applications: Heap Sort, implementing Priority Queues.
  • 7.4. Hashing
    • Hash functions, collision handling (Chaining, Open Addressing).
    • Implementing a Hash Table (Map).

Module 8: Advanced Algorithmic Paradigms

Master powerful techniques to solve a wide range of complex problems.

  • 8.1. Backtracking
    • The state-space tree concept.
    • Solving problems: N-Queens, Sudoku Solver, Maze problems.
  • 8.2. Divide and Conquer
    • Formalizing the technique (Revisiting Merge/Quick Sort).
    • Other applications.
  • 8.3. Greedy Algorithms
    • The "locally optimal choice" strategy.
    • Problems: Fractional Knapsack, Activity Selection, Huffman Coding.
  • 8.4. Dynamic Programming (DP)
    • Identifying DP problems (Overlapping Subproblems & Optimal Substructure).
    • Techniques: Memoization (Top-Down) vs. Tabulation (Bottom-Up).
    • Classic DP Problems: 0/1 Knapsack, Coin Change, Longest Common Subsequence, Longest Increasing Subsequence, Longest Palindromic Subsequence, Longest Palindromic Substring, Edit Distance etc.
  • 8.5. Sliding Window Technique
    • Solving problems on arrays/strings related to finding optimal subarrays/substrings.

Module 9: Graphs

Explore the most versatile data structure used to model networks and relationships.

  • 9.1. Graph Theory Fundamentals
    • Types of Graphs (Directed, Undirected, Weighted).
    • Graph Representation: Adjacency Matrix vs. Adjacency List.
  • 9.2. Graph Traversal Algorithms
    • Breadth-First Search (BFS) and its applications.
    • Depth-First Search (DFS) and its applications.
  • 9.3. Key Graph Algorithms
    • Minimum Spanning Tree (MST): Prim's and Kruskal's Algorithms.
    • Shortest Path Algorithms: Dijkstra's Algorithm, Bellman-Ford Algorithm, Floyd Warshall Algorithm .
    • Topological Sort for Directed Acyclic Graphs (DAGs), Kahn’s Algorithm.
    • Strongly Connected Components(SCC) : Kosaraju’s Algorithm, Tarjan’s Algorithm.

Module 10: Advanced Data Structures & Topics

Topics for those who want to go the extra mile and explore specialized, high-performance structures.

  • 10.1. Bit Manipulation
    • Bitwise operators in-depth.
    • Techniques for solving problems efficiently.
  • 10.2. Tries (Prefix Trees)
    • Implementation and applications in string searching, autocomplete.
  • 10.3. Self-Balancing Trees
    • The need for balancing (avoiding worst-case BST).
    • Concept and Rotations in AVL Trees.
  • 10.4. B-Trees and B+ Trees
    • Structure and use cases, especially in database indexing.
  • 10.5. A Glimpse into Vector Databases (Conceptual)
    • What are vector embeddings?
    • The problem of high-dimensional search.
    • Core Algorithm

3 Final Project / Capstone:

GenAI Course

Course Title: The Complete Generative AI Engineering Bootcamp: Build & Deploy Autonomous AI Agents

Course Description: This is the definitive course for anyone serious about building the next generation of AI. We will take you on a comprehensive journey from zero to hero, starting with the fundamental concepts of Generative AI and the Transformer architecture. You will then immediately apply this knowledge to build, test, and deploy sophisticated, autonomous AI agents capable of reasoning, planning, and using tools to solve complex problems. Using the entire modern stack, including LangChain, LangGraph, and LangSmith, you will graduate with a portfolio of intelligent agents and the complete skill set of a modern AI Engineer.

PART I: CORE CONCEPTS & THEORETICAL FOUNDATIONS

Module 1: The New Age of AI: Introduction to Generative AI

  • 1.1. The AI Landscape: Differentiating between AI, Machine Learning, Deep Learning, and Generative AI.
  • 1.2. What is Generative AI? Understanding the shift from discriminative to generative models.
  • 1.3. A High-Level Look at LLMs: What are they, what can they do, and what are their limitations?
  • 1.4. The GenAI Ecosystem: Key Players (OpenAI, Google, Meta), Models (GPT, Llama), and the Open-Source Movement.

Module 2: How Language Models "Think": Tokens, Prompts, and Predictions

  • 2.1. From Text to Numbers: The Concept of Tokenization.
  • 2.2. The Core Interaction: Prompts, Completions, and the Context Window.
  • 2.3. The Predictive Engine: An intuitive look at the autoregressive nature of LLMs.
  • 2.4. Parametric Knowledge vs. Source Knowledge: Understanding why external data is crucial for building smart agents.

Module 3: Unlocking the Black Box: An Intuition for Deep Learning

  • 3.1. The Building Blocks: Artificial Neurons and Neural Networks.
  • 3.2. The Learning Process: A clear, non-mathematical explanation of Loss Functions, Gradient Descent, and Backpropagation.
  • 3.3. Why "Deep" Learning? The power of multiple layers in learning complex patterns.

Module 4: The Engine of Modern LLMs: The Transformer Architecture

  • 4.1. The Core Innovation: The Self-Attention Mechanism.
  • 4.2. Positional Encodings: How a model that processes everything at once still understands word order.
  • 4.3. Deconstructing the Transformer: A walkthrough of its key components.

Module 5: Representing Meaning: Embeddings & Vector Databases

  • 5.1. What are Vector Embeddings? The powerful idea of representing data as points in a high-dimensional space.
  • 5.2. The Need for Vector Databases: Why SQL and NoSQL databases are not enough for AI.
  • 5.3. Inside the Search Engine: Intuition behind Approximate Nearest Neighbor (ANN) search algorithms.
  • 5.4. Hands-On with Vector DBs: Setting up and using databases like Qdrant, Pinecone, or PG Vector.

PART II: BUILDING APPLICATIONS & AUTONOMOUS AGENTS

Module 6: The Modern AI Stack: Introduction to LangChain

  • 6.1. Why LangChain? The philosophy of composing LLM calls into chains and applications.
  • 6.2. Core LangChain Components: Models, Prompts, and Output Parsers.
  • 6.3. Building Your First LLM Chain: A hands-on "Hello, World!" for GenAI applications.

Module 7: Building the Knowledge Base for Agents: RAG In-Depth

  • 7.1. Architecting the Full RAG Pipeline: Ingestion, Chunking, Embedding, Indexing, Retrieval, and Generation.
  • 7.2. Practical Implementation: Building a robust "Chat with Your Documents" application.
  • 7.3. Laying the Foundation: Understanding how RAG provides the critical knowledge retrieval tool for intelligent agents.

Module 8: Building Your First Autonomous AI Agent

  • 8.1. The Anatomy of an AI Agent: Deconstructing the core reasoning loop .
  • 8.2. Giving Your Agent Superpowers: Enabling it to browse the web, run code, and use APIs as tools.
  • 8.3. Creating an Agent with Memory: Implementing short-term and long-term memory.
  • *8.4. Project: Build a Research Assistant Agent that can autonomously browse the web and summarize findings on any topic.

Module 9: Building Multi-Agent Systems with LangGraph

  • 9.1. The Limits of Single Agents and the Need for Graphs.
  • 9.2. Introduction to LangGraph: Defining states, nodes, and edges to orchestrate complex agentic workflows.
  • 9.3. Designing for Control: Implementing Human-in-the-Loop interruptions for critical oversight.
  • *9.4. Project: Build a Collaborative AI Team where a planner agent delegates tasks to specialized worker agents.

Module 10: Making Agents Smarter: Advanced Data Techniques

  • 10.1. Graph RAG: Equipping your agents with knowledge graphs to understand complex relationships.
  • 10.2. Multi-Modal Agents: Building systems that can process and integrate text, images, and other data types.

PART III: PRODUCTION, EVALUATION, AND MASTERY

Module 11: Taking Your Agents to Production: Deployment and Scaling

  • 11.1. Architecting for Scale: Designing your AI agent as a robust microservice.
  • 11.2. Cloud Deployment Mastery: Hosting and scaling your agent on a cloud platform like AWS.
  • **11.3.**Introducing the MCP (Model Control Plane) Server concept for efficiently deploying and managing your AI microservice.
  • 11.4.The Art of Fine-Tuning: Customizing a base model to create a specialized agent.

Module 12: Evaluating and Debugging Your Agents

  • 12.1. Observability for LLMs: Using LangSmith to trace, monitor, and debug your agent's reasoning process.
  • 12.2. Measuring Agent Performance: Key metrics for evaluating the success and reliability of agentic systems.
  • 12.3. Automated Evaluation: Implementing the powerful "LLM as a Judge" technique to score your agent's outputs.

Module 13: Capstone Project: Build a Full-Stack Autonomous AI Agent

  • Build the Major project using All knowledge of GenAI

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