Free Course 
We have 12 videos where we will start from scratch regarding DP and go to Advance Level.

Advance Dynamic Programming

Rohit Negi

This will be complete LIVE course on&nbsp;Web Development and Blockchain from Beginner to Advance Level. It also include DSA course which is pre-Recorded.
<ul>
	<li>Build robust web applications and secure blockchain solutions: Gain hands-on experience in HTML, CSS, JavaScript, and blockchain fundamentals. Master full-stack development and explore the potential of decentralized applications.</li>
	<li>Master the synergy between web and blockchain: This course equips you with the skills to create innovative solutions by combining the power of web development with the security and transparency of blockchain technology.</li>
	<li>Unlock new career opportunities: Learn to develop cutting-edge applications that leverage the potential of both web and blockchain. Gain in-demand skills and become a sought-after developer.</li>
</ul>
&nbsp;
No Prerequisite Required 
Course Start Date: 16 sep 2024 
Class Timing: 9-10 pm (Mon to Fri) LIVE 
Instructor: Rohit Negi 
Doubt Support: Discord Group 
Recording: It will be available after the class 
Industry Grade Projects 
 
 
Course Content
1: Web Development
● Basic of Internet
● HTML
● CSS
● JAVASCRIPT
● React JS
● TypeScript
● NODE JS
● Express JS
● MONGODB and Mongoose
● Authentication and Authorization
● WebSockets and WebRTC
● API
● Advance Projects with Deployment
&nbsp;
2: BlockChain
● Blockchain Introduction
● Cryptographic Foundations
● Blockchain Internal Working
● Ethereum and it&rsquo;s Ecosystem
● Solana client side apps
● Smart Contracts
● Solidity from Basic to Advance
● Rust Basic to Advance
● Advance Projects with Deployment 
 
3: Data Structure and Algorithm
<ul>
	<li>What is coding</li>
	<li>FlowChart</li>
	<li>Time &amp; Space Complexity</li>
	<li>Variables &amp; DataTypes in C++</li>
	<li>Operators, Conditionals &amp; Loops Array,</li>
	<li>Searching Algorithms</li>
	<li>Sorting Algorithms</li>
	<li>Concepts Dynamic Arrays</li>
	<li>String</li>
	<li>Basic Maths &amp; Pointers</li>
	<li>Standard Template Library</li>
	<li>Function &amp; in-depth Knowledge of flow</li>
	<li>Recursion &amp; Backtracking&nbsp; &nbsp;</li>
	<li>Object Oriented Programming</li>
	<li>Linked List</li>
	<li>Stack &amp; Queues</li>
	<li>Sliding Window Problems</li>
	<li>Bit Manipulation &nbsp; &nbsp;&nbsp;</li>
	<li>Trees</li>
	<li>Heaps</li>
	<li>Divide &amp; Conquer Technique</li>
	<li>Graphs</li>
	<li>Greedy Algorithms</li>
	<li>Dynamic Programming</li>
	<li>Hashing &amp; Tries</li>
</ul>
 
DSA includes: Beginner friendly (No Pre-requisite Required)
<ol>
	<li>150+ hours Recorded&nbsp;Videos</li>
	<li>300+ Coding Questions (asked by Top Companies)</li>
	<li>Completion Certificate</li>
</ol>
&nbsp;
<ul>
	<li>Other Feature: Recorded&nbsp;classes</li>
	<li>Course Start On: 16 September&nbsp;2024&nbsp;</li>
	<li>Course Validity: 3&nbsp;Year&nbsp;</li>
	<li>Programming Language: C++</li>
	<li>Course will be in Hinglis</li>
</ul>
&nbsp;
&nbsp;
4: High Level Design: (start from 7 july 2025) 
 
 
1: Foundation &amp; Basics (Theory + Mini Use Cases)
<ol>
	<li>What is System Design? Goals, Trade-Os, protocols</li>
	<li>CAP Theorem + Consistency vs Availability</li>
	<li>Architecture Patterns : Monolith vs Microservices</li>
	<li>HLD Approach: Interview Framework &amp; Thinking Process</li>
	<li>Load Balancing + Horizontal vs Vertical Scaling</li>
	<li>Consistent Hashing</li>
	<li>Caching (Types, When/Where/Why)</li>
	<li>Database Design (SQL vs NoSQL, Sharding, Indexing)</li>
	<li>Messaging Queues, Pub-Sub, Kafka, Rabbit-MQ etc</li>
	<li>Scaling to Millions of Users</li>
</ol>
Module 2: Real-World Features with Concepts
<ol>
	<li>Designing Authentication &amp; Authorization</li>
	<li>Rate Limiting &amp; API Gateway</li>
	<li>Building Rate Limiter (Leaky/Token Bucket etc)</li>
	<li>CDN + Static Content Delivery</li>
	<li>Monitoring, Logging &amp; Health Checks</li>
	<li>Designing Search (Inverted Index, Ranking, Autocomplete)</li>
	<li>Notification System (Email/SMS/Push)</li>
	<li>Building File Upload System (Like Google Drive)</li>
</ol>
Module 3: Complete System Design Problems
<ol>
	<li>Building URL Shortener like (Bit.ly)</li>
	<li>Building Instagram Feed Design</li>
	<li>Building Zomato/Swiggy</li>
	<li>Building YouTube (Video Service)</li>
	<li>Building WhatsApp/Chat App</li>
	<li>Building Google Docs (Collaborative Editing)</li>
	<li>Design Twitter</li>
</ol>
5: Resume Making and Linkedin Profile Optimization
&nbsp;
 
 
Contact us:&nbsp;
<table cellpadding="0">
	<tbody>
		<tr>
			<td colspan="2">negibhairohit9@gmail.com</td>
		</tr>
	</tbody>
</table>
&nbsp;
&nbsp;

Web Development + Block Chain + System Design+ Data Structure and Algorithm

This is complete course of&nbsp;Data Structure and Algorithm. All the videos are prerecorded, you can watch them at your own pace and get all the required knowledge.
What is coding 
FlowChart 
Time &amp; Space Complexity 
Variables &amp; DataTypes in C++ 
Operators, Conditionals &amp; Loops Array, 
Searching Algorithms 
Sorting Algorithms 
Concepts Dynamic Arrays 
String 
Basic Maths &amp; Pointers 
Standard Template Library 
Function &amp; in-depth Knowledge of flow 
Recursion &amp; Backtracking&nbsp; &nbsp; 
Object Oriented Programming 
Linked List 
Stack &amp; Queues 
Sliding Window Problems 
Bit Manipulation &nbsp; &nbsp;&nbsp; 
Trees 
Heaps 
Divide &amp; Conquer Technique 
Graphs 
Greedy Algorithms 
Dynamic Programming 
Hashing &amp; Tries 
 
This Course also includes: Beginner friendly (No Pre-requisite Required) 
150+ hours Recorded&nbsp;Videos 
300+ Coding Questions (asked by Top Companies) 
Linkedin Hack To Get Referral 
Completion Certificate 
 
Other Feature: Recorded&nbsp;classes 
Course Start On: 16 September&nbsp;2024&nbsp; 
Course Validity: 3&nbsp;Year&nbsp; 
Programming Language: C++ 
Course will be in Hinglish

Data Structure and Algorithm Course

What is system Design, we will talk about it in detail. Hackathon will be organised here and total price will be 1lakh. Get ready to level up your self. 
It is will be 8 week course and video will come on coder army channel from mon-friday. 
Course completion certificate will be given to those student who will participate in hackathon.

System Design Course

High level Design Course

This is a Live course of Data structure and Algorithm and GENAI. 
&nbsp;
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
&nbsp;
<h3>Data Structures and Algorithms in C++: From Beginner to Advanced</h3>
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:
<ul>
	<li>Concept First: We focus on understanding the &quot;why&quot; behind each data structure and algorithm.</li>
	<li>Implementation Focused: Every concept will be backed by a clean, well-explained C++ implementation.</li>
	<li>Problem Solving: We will solve numerous problems to solidify understanding and prepare you for technical interviews and real-world challenges.</li>
</ul>
<hr />
<h3>Detailed Course Syllabus</h3>
<h3>Module 1: C++ Foundations for DSA</h3>
This module ensures you have the necessary C++ fundamentals before we dive into complex algorithms.
<ul>
	<li>1.1. Introduction to C++ &amp; Course Setup
	<ul>
		<li>Setting up your development environment (Compiler, IDE like VS Code).</li>
		<li>Writing your first program: &quot;Hello, World!&quot;</li>
		<li>Compilation and Execution process.</li>
	</ul>
	</li>
	<li>1.2. Variables, Data Types &amp; I/O
	<ul>
		<li>Primitive Data Types (<code>int</code>, <code>char</code>, <code>float</code>, <code>double</code>, <code>bool</code>).</li>
		<li>Input/Output streams (<code>cin</code>, <code>cout</code>).</li>
	</ul>
	</li>
	<li>1.3. Operators, Conditionals &amp; Loops
	<ul>
		<li>Arithmetic, Relational, Logical, and Bitwise Operators.</li>
		<li><code>if</code>, <code>else if</code>, <code>else</code>, <code>switch-case</code>.</li>
		<li><code>for</code>, <code>while</code>, <code>do-while</code> loops.</li>
	</ul>
	</li>
	<li>1.4. Functions in C++
	<ul>
		<li>Function declaration, definition, and calling.</li>
		<li>Parameters and return values.</li>
		<li>Pass by Value vs. Pass by Reference.</li>
	</ul>
	</li>
	<li>1.5. Pointers &amp; Dynamic Memory
	<ul>
		<li>Understanding memory addresses and the <code>&amp;</code> and operators.</li>
		<li>Pointers and Arrays.</li>
		<li>Dynamic Memory Allocation (<code>new</code> and <code>delete</code> keywords).</li>
	</ul>
	</li>
</ul>
<h3>Module 2: Core Concepts of Algorithmic Analysis</h3>
Here, we build the theoretical framework for analyzing and comparing algorithms.
<ul>
	<li>2.1. Time &amp; Space Complexity
	<ul>
		<li>Measuring algorithm efficiency.</li>
		<li>Asymptotic Notations: Big O, Big Omega (&Omega;), and Big Theta (&Theta;).</li>
		<li>Analyzing iterative and recursive code.</li>
		<li>Best, Average, and Worst-case analysis.</li>
	</ul>
	</li>
	<li>2.2. Recursion
	<ul>
		<li>The concept of recursion and the call stack.</li>
		<li>Writing base cases and recursive relations.</li>
		<li>Solving problems like Factorial, Fibonacci Series, and Tower of Hanoi.</li>
	</ul>
	</li>
</ul>
<h3>Module 3: Basic Data Structures &amp; Algorithms</h3>
We begin our DSA journey with the most fundamental structures and algorithms.
<ul>
	<li>3.1. Arrays &amp; Dynamic Arrays
	<ul>
		<li>Static vs. Dynamic Arrays.</li>
		<li>Implementing a Dynamic Array (Vector).</li>
		<li>Common array operations and problems.</li>
	</ul>
	</li>
	<li>3.2. Strings
	<ul>
		<li>C-style strings vs. C++ <code>std::string</code>.</li>
		<li>String manipulation and common string problems.</li>
	</ul>
	</li>
	<li>3.3. Searching Algorithms
	<ul>
		<li>Linear Search (Time Complexity: O(n)).</li>
		<li>Binary Search (Time Complexity: O(log n)) - Iterative and Recursive.</li>
	</ul>
	</li>
	<li>3.4. Sorting Algorithms
	<ul>
		<li>Basic Sorts: Bubble Sort, Selection Sort, Insertion Sort (O(n&sup2;)).</li>
		<li>Efficient Sorts (using Divide &amp; Conquer): Merge Sort, Quick Sort (O(n log n)).</li>
		<li>In-depth analysis of Quick Sort (pivoting strategies, worst-case).</li>
	</ul>
	</li>
</ul>
<h3>Module 4: The C++ Standard Template Library (STL)</h3>
Master the powerful, pre-built tools in C++ to write code faster and more efficiently.
<ul>
	<li>4.1. Introduction to STL
	<ul>
		<li>Overview of Containers, Iterators, and Algorithms.</li>
	</ul>
	</li>
	<li>4.2. STL Containers
	<ul>
		<li>Sequence Containers: <code>vector</code>, <code>string</code>, <code>list</code>, <code>deque</code>.</li>
		<li>Container Adaptors: <code>stack</code>, <code>queue</code>, <code>priority_queue</code>.</li>
		<li>Associative Containers: <code>set</code>, <code>map</code>, <code>multiset</code>, <code>multimap</code>.</li>
		<li>Unordered Associative Containers: <code>unordered_set</code>, <code>unordered_map</code>.</li>
	</ul>
	</li>
	<li>4.3. STL Algorithms &amp; Functors
	<ul>
		<li><code>sort</code>, <code>binary_search</code>, <code>reverse</code>, <code>max_element</code>, and more.</li>
	</ul>
	</li>
</ul>
<h3>Module 5: Object-Oriented Programming (OOP) for Data Structures</h3>
Learn to design and implement your own data structures using the principles of OOP.
<ul>
	<li>5.1. Classes and Objects</li>
	<li>5.2. The Four Pillars of OOP
	<ul>
		<li>Encapsulation, Abstraction, Inheritance, Polymorphism.</li>
	</ul>
	</li>
	<li>5.3. Constructors &amp; Destructors</li>
	<li>5.4. Building Data Structures as Classes</li>
</ul>
<h3>Module 6: Linear Data Structures</h3>
Explore data structures that store data sequentially.
<ul>
	<li>6.1. Linked Lists
	<ul>
		<li>Singly, Doubly, and Circular Linked Lists.</li>
		<li>Implementation: Insertion, Deletion, Traversal, Searching.</li>
		<li>Solving common Linked List problems.</li>
	</ul>
	</li>
	<li>6.2. Stacks
	<ul>
		<li>LIFO (Last-In, First-Out) principle.</li>
		<li>Implementation using Arrays and Linked Lists.</li>
		<li>Applications: Expression evaluation, balancing parentheses, etc.</li>
	</ul>
	</li>
	<li>6.3. Queues
	<ul>
		<li>FIFO (First-In, First-Out) principle.</li>
		<li>Implementation using Arrays and Linked Lists.</li>
		<li>Circular Queues, Deques.</li>
		<li>Applications: Schedulers, BFS traversal.</li>
	</ul>
	</li>
</ul>
<h3>Module 7: Non-Linear Data Structures</h3>
Dive into hierarchical and network-based data structures.
<ul>
	<li>7.1. Trees - Core Concepts
	<ul>
		<li>Terminology: Root, Node, Edge, Height, Depth, etc.</li>
		<li>Binary Trees: Representation and Traversals (In-order, Pre-order, Post-order, Level-order).</li>
	</ul>
	</li>
	<li>7.2. Binary Search Trees (BST)
	<ul>
		<li>Properties, Searching, Insertion, Deletion.</li>
		<li>Time complexity analysis.</li>
	</ul>
	</li>
	<li>7.3. Heaps &amp; Priority Queues
	<ul>
		<li>Binary Heaps (Min-Heap, Max-Heap).</li>
		<li>Implementation: <code>insert</code>, <code>extract</code>, <code>heapify</code>.</li>
		<li>Applications: Heap Sort, implementing Priority Queues.</li>
	</ul>
	</li>
	<li>7.4. Hashing
	<ul>
		<li>Hash functions, collision handling (Chaining, Open Addressing).</li>
		<li>Implementing a Hash Table (Map).</li>
	</ul>
	</li>
</ul>
<h3>Module 8: Advanced Algorithmic Paradigms</h3>
Master powerful techniques to solve a wide range of complex problems.
<ul>
	<li>8.1. Backtracking
	<ul>
		<li>The state-space tree concept.</li>
		<li>Solving problems: N-Queens, Sudoku Solver, Maze problems.</li>
	</ul>
	</li>
	<li>8.2. Divide and Conquer
	<ul>
		<li>Formalizing the technique (Revisiting Merge/Quick Sort).</li>
		<li>Other applications.</li>
	</ul>
	</li>
	<li>8.3. Greedy Algorithms
	<ul>
		<li>The &quot;locally optimal choice&quot; strategy.</li>
		<li>Problems: Fractional Knapsack, Activity Selection, Huffman Coding.</li>
	</ul>
	</li>
	<li>8.4. Dynamic Programming (DP)
	<ul>
		<li>Identifying DP problems (Overlapping Subproblems &amp; Optimal Substructure).</li>
		<li>Techniques: Memoization (Top-Down) vs. Tabulation (Bottom-Up).</li>
		<li>Classic DP Problems: 0/1 Knapsack, Coin Change, Longest Common Subsequence, Longest Increasing Subsequence, Longest Palindromic Subsequence, Longest Palindromic Substring, Edit Distance etc.</li>
	</ul>
	</li>
	<li>8.5. Sliding Window Technique
	<ul>
		<li>Solving problems on arrays/strings related to finding optimal subarrays/substrings.</li>
	</ul>
	</li>
</ul>
<h3>Module 9: Graphs</h3>
Explore the most versatile data structure used to model networks and relationships.
<ul>
	<li>9.1. Graph Theory Fundamentals
	<ul>
		<li>Types of Graphs (Directed, Undirected, Weighted).</li>
		<li>Graph Representation: Adjacency Matrix vs. Adjacency List.</li>
	</ul>
	</li>
	<li>9.2. Graph Traversal Algorithms
	<ul>
		<li>Breadth-First Search (BFS) and its applications.</li>
		<li>Depth-First Search (DFS) and its applications.</li>
	</ul>
	</li>
	<li>9.3. Key Graph Algorithms
	<ul>
		<li>Minimum Spanning Tree (MST): Prim&#39;s and Kruskal&#39;s Algorithms.</li>
		<li>Shortest Path Algorithms: Dijkstra&#39;s Algorithm, Bellman-Ford Algorithm, Floyd Warshall Algorithm .</li>
		<li>Topological Sort for Directed Acyclic Graphs (DAGs), Kahn&rsquo;s Algorithm.</li>
		<li>Strongly Connected Components(SCC) : Kosaraju&rsquo;s Algorithm, Tarjan&rsquo;s Algorithm.</li>
	</ul>
	</li>
</ul>
<h3>Module 10: Advanced Data Structures &amp; Topics</h3>
Topics for those who want to go the extra mile and explore specialized, high-performance structures.
<ul>
	<li>10.1. Bit Manipulation
	<ul>
		<li>Bitwise operators in-depth.</li>
		<li>Techniques for solving problems efficiently.</li>
	</ul>
	</li>
	<li>10.2. Tries (Prefix Trees)
	<ul>
		<li>Implementation and applications in string searching, autocomplete.</li>
	</ul>
	</li>
	<li>10.3. Self-Balancing Trees
	<ul>
		<li>The need for balancing (avoiding worst-case BST).</li>
		<li>Concept and Rotations in AVL Trees.</li>
	</ul>
	</li>
	<li>10.4. B-Trees and B+ Trees
	<ul>
		<li>Structure and use cases, especially in database indexing.</li>
	</ul>
	</li>
	<li>10.5. A Glimpse into Vector Databases (Conceptual)
	<ul>
		<li>What are vector embeddings?</li>
		<li>The problem of high-dimensional search.</li>
		<li>Core Algorithm</li>
	</ul>
	</li>
</ul>
<hr />
3 Final Project / Capstone:
<h2>GenAI Course</h2>
<h3>Course Title: The Complete Generative AI Engineering Bootcamp: Build &amp; Deploy Autonomous AI Agents</h3>
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.
<hr />
<h3>PART I: CORE CONCEPTS &amp; THEORETICAL FOUNDATIONS</h3>
<h3>Module 1: The New Age of AI: Introduction to Generative AI</h3>
<ul>
	<li>1.1. The AI Landscape: Differentiating between AI, Machine Learning, Deep Learning, and Generative AI.</li>
	<li>1.2. What is Generative AI? Understanding the shift from discriminative to generative models.</li>
	<li>1.3. A High-Level Look at LLMs: What are they, what can they do, and what are their limitations?</li>
	<li>1.4. The GenAI Ecosystem: Key Players (OpenAI, Google, Meta), Models (GPT, Llama), and the Open-Source Movement.</li>
</ul>
<h3>Module 2: How Language Models &quot;Think&quot;: Tokens, Prompts, and Predictions</h3>
<ul>
	<li>2.1. From Text to Numbers: The Concept of Tokenization.</li>
	<li>2.2. The Core Interaction: Prompts, Completions, and the Context Window.</li>
	<li>2.3. The Predictive Engine: An intuitive look at the autoregressive nature of LLMs.</li>
	<li>2.4. Parametric Knowledge vs. Source Knowledge: Understanding why external data is crucial for building smart agents.</li>
</ul>
<h3>Module 3: Unlocking the Black Box: An Intuition for Deep Learning</h3>
<ul>
	<li>3.1. The Building Blocks: Artificial Neurons and Neural Networks.</li>
	<li>3.2. The Learning Process: A clear, non-mathematical explanation of Loss Functions, Gradient Descent, and Backpropagation.</li>
	<li>3.3. Why &quot;Deep&quot; Learning? The power of multiple layers in learning complex patterns.</li>
</ul>
<h3>Module 4: The Engine of Modern LLMs: The Transformer Architecture</h3>
<ul>
	<li>4.1. The Core Innovation: The Self-Attention Mechanism.</li>
	<li>4.2. Positional Encodings: How a model that processes everything at once still understands word order.</li>
	<li>4.3. Deconstructing the Transformer: A walkthrough of its key components.</li>
</ul>
<h3>Module 5: Representing Meaning: Embeddings &amp; Vector Databases</h3>
<ul>
	<li>5.1. What are Vector Embeddings? The powerful idea of representing data as points in a high-dimensional space.</li>
	<li>5.2. The Need for Vector Databases: Why SQL and NoSQL databases are not enough for AI.</li>
	<li>5.3. Inside the Search Engine: Intuition behind Approximate Nearest Neighbor (ANN) search algorithms.</li>
	<li>5.4. Hands-On with Vector DBs: Setting up and using databases like Qdrant, Pinecone, or PG Vector.</li>
</ul>
<hr />
<h3>PART II: BUILDING APPLICATIONS &amp; AUTONOMOUS AGENTS</h3>
<h3>Module 6: The Modern AI Stack: Introduction to LangChain</h3>
<ul>
	<li>6.1. Why LangChain? The philosophy of composing LLM calls into chains and applications.</li>
	<li>6.2. Core LangChain Components: Models, Prompts, and Output Parsers.</li>
	<li>6.3. Building Your First LLM Chain: A hands-on &quot;Hello, World!&quot; for GenAI applications.</li>
</ul>
<h3>Module 7: Building the Knowledge Base for Agents: RAG In-Depth</h3>
<ul>
	<li>7.1. Architecting the Full RAG Pipeline: Ingestion, Chunking, Embedding, Indexing, Retrieval, and Generation.</li>
	<li>7.2. Practical Implementation: Building a robust &quot;Chat with Your Documents&quot; application.</li>
	<li>7.3. Laying the Foundation: Understanding how RAG provides the critical knowledge retrieval tool for intelligent agents.</li>
</ul>
<h3>Module 8: Building Your First Autonomous AI Agent</h3>
<ul>
	<li>8.1. The Anatomy of an AI Agent: Deconstructing the core reasoning loop .</li>
	<li>8.2. Giving Your Agent Superpowers: Enabling it to browse the web, run code, and use APIs as tools.</li>
	<li>8.3. Creating an Agent with Memory: Implementing short-term and long-term memory.</li>
	<li>*8.4. Project: Build a Research Assistant Agent that can autonomously browse the web and summarize findings on any topic.</li>
</ul>
<h3>Module 9: Building Multi-Agent Systems with LangGraph</h3>
<ul>
	<li>9.1. The Limits of Single Agents and the Need for Graphs.</li>
	<li>9.2. Introduction to LangGraph: Defining states, nodes, and edges to orchestrate complex agentic workflows.</li>
	<li>9.3. Designing for Control: Implementing Human-in-the-Loop interruptions for critical oversight.</li>
	<li>*9.4. Project: Build a Collaborative AI Team where a planner agent delegates tasks to specialized worker agents.</li>
</ul>
<h3>Module 10: Making Agents Smarter: Advanced Data Techniques</h3>
<ul>
	<li>10.1. Graph RAG: Equipping your agents with knowledge graphs to understand complex relationships.</li>
	<li>10.2. Multi-Modal Agents: Building systems that can process and integrate text, images, and other data types.</li>
</ul>
<hr />
<h3>PART III: PRODUCTION, EVALUATION, AND MASTERY</h3>
<h3>Module 11: Taking Your Agents to Production: Deployment and Scaling</h3>
<ul>
	<li>11.1. Architecting for Scale: Designing your AI agent as a robust microservice.</li>
	<li>11.2. Cloud Deployment Mastery: Hosting and scaling your agent on a cloud platform like AWS.</li>
	<li>**11.3.**Introducing the&nbsp;MCP (Model Control Plane) Server&nbsp;concept for efficiently deploying and managing your AI microservice.</li>
	<li>11.4.The Art of Fine-Tuning: Customizing a base model to create a specialized agent.</li>
</ul>
<h3>Module 12: Evaluating and Debugging Your Agents</h3>
<ul>
	<li>12.1. Observability for LLMs: Using LangSmith to trace, monitor, and debug your agent&#39;s reasoning process.</li>
	<li>12.2. Measuring Agent Performance: Key metrics for evaluating the success and reliability of agentic systems.</li>
	<li>12.3. Automated Evaluation: Implementing the powerful &quot;LLM as a Judge&quot; technique to score your agent&#39;s outputs.</li>
</ul>
<h3>Module 13: Capstone Project: Build a Full-Stack Autonomous AI Agent</h3>
<ul>
	<li>Build the Major project using All knowledge of GenAI</li>
</ul>

DSA and GENAI Course

This is a Live course of GENAI. 
&nbsp;
Course start Date: 3&nbsp;Dec 2025 
Class Timing: Wed and Fri&nbsp;(9pm) Live 
Recording will be available after the class 
GenAI course: Javascript
&nbsp;
<h3>&nbsp;</h3>
<h2>GenAI Course</h2>
<h3>Course Title: The Complete Generative AI Engineering Bootcamp: Build &amp; Deploy Autonomous AI Agents</h3>
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.
<hr />
<h3>PART I: CORE CONCEPTS &amp; THEORETICAL FOUNDATIONS</h3>
<h3>Module 1: The New Age of AI: Introduction to Generative AI</h3>
<ul>
	<li>1.1. The AI Landscape: Differentiating between AI, Machine Learning, Deep Learning, and Generative AI.</li>
	<li>1.2. What is Generative AI? Understanding the shift from discriminative to generative models.</li>
	<li>1.3. A High-Level Look at LLMs: What are they, what can they do, and what are their limitations?</li>
	<li>1.4. The GenAI Ecosystem: Key Players (OpenAI, Google, Meta), Models (GPT, Llama), and the Open-Source Movement.</li>
</ul>
<h3>Module 2: How Language Models &quot;Think&quot;: Tokens, Prompts, and Predictions</h3>
<ul>
	<li>2.1. From Text to Numbers: The Concept of Tokenization.</li>
	<li>2.2. The Core Interaction: Prompts, Completions, and the Context Window.</li>
	<li>2.3. The Predictive Engine: An intuitive look at the autoregressive nature of LLMs.</li>
	<li>2.4. Parametric Knowledge vs. Source Knowledge: Understanding why external data is crucial for building smart agents.</li>
</ul>
<h3>Module 3: Unlocking the Black Box: An Intuition for Deep Learning</h3>
<ul>
	<li>3.1. The Building Blocks: Artificial Neurons and Neural Networks.</li>
	<li>3.2. The Learning Process: A clear, non-mathematical explanation of Loss Functions, Gradient Descent, and Backpropagation.</li>
	<li>3.3. Why &quot;Deep&quot; Learning? The power of multiple layers in learning complex patterns.</li>
</ul>
<h3>Module 4: The Engine of Modern LLMs: The Transformer Architecture</h3>
<ul>
	<li>4.1. The Core Innovation: The Self-Attention Mechanism.</li>
	<li>4.2. Positional Encodings: How a model that processes everything at once still understands word order.</li>
	<li>4.3. Deconstructing the Transformer: A walkthrough of its key components.</li>
</ul>
<h3>Module 5: Representing Meaning: Embeddings &amp; Vector Databases</h3>
<ul>
	<li>5.1. What are Vector Embeddings? The powerful idea of representing data as points in a high-dimensional space.</li>
	<li>5.2. The Need for Vector Databases: Why SQL and NoSQL databases are not enough for AI.</li>
	<li>5.3. Inside the Search Engine: Intuition behind Approximate Nearest Neighbor (ANN) search algorithms.</li>
	<li>5.4. Hands-On with Vector DBs: Setting up and using databases like Qdrant, Pinecone, or PG Vector.</li>
</ul>
<hr />
<h3>PART II: BUILDING APPLICATIONS &amp; AUTONOMOUS AGENTS</h3>
<h3>Module 6: The Modern AI Stack: Introduction to LangChain</h3>
<ul>
	<li>6.1. Why LangChain? The philosophy of composing LLM calls into chains and applications.</li>
	<li>6.2. Core LangChain Components: Models, Prompts, and Output Parsers.</li>
	<li>6.3. Building Your First LLM Chain: A hands-on &quot;Hello, World!&quot; for GenAI applications.</li>
</ul>
<h3>Module 7: Building the Knowledge Base for Agents: RAG In-Depth</h3>
<ul>
	<li>7.1. Architecting the Full RAG Pipeline: Ingestion, Chunking, Embedding, Indexing, Retrieval, and Generation.</li>
	<li>7.2. Practical Implementation: Building a robust &quot;Chat with Your Documents&quot; application.</li>
	<li>7.3. Laying the Foundation: Understanding how RAG provides the critical knowledge retrieval tool for intelligent agents.</li>
</ul>
<h3>Module 8: Building Your First Autonomous AI Agent</h3>
<ul>
	<li>8.1. The Anatomy of an AI Agent: Deconstructing the core reasoning loop .</li>
	<li>8.2. Giving Your Agent Superpowers: Enabling it to browse the web, run code, and use APIs as tools.</li>
	<li>8.3. Creating an Agent with Memory: Implementing short-term and long-term memory.</li>
	<li>*8.4. Project: Build a Research Assistant Agent that can autonomously browse the web and summarize findings on any topic.</li>
</ul>
<h3>Module 9: Building Multi-Agent Systems with LangGraph</h3>
<ul>
	<li>9.1. The Limits of Single Agents and the Need for Graphs.</li>
	<li>9.2. Introduction to LangGraph: Defining states, nodes, and edges to orchestrate complex agentic workflows.</li>
	<li>9.3. Designing for Control: Implementing Human-in-the-Loop interruptions for critical oversight.</li>
	<li>*9.4. Project: Build a Collaborative AI Team where a planner agent delegates tasks to specialized worker agents.</li>
</ul>
<h3>Module 10: Making Agents Smarter: Advanced Data Techniques</h3>
<ul>
	<li>10.1. Graph RAG: Equipping your agents with knowledge graphs to understand complex relationships.</li>
	<li>10.2. Multi-Modal Agents: Building systems that can process and integrate text, images, and other data types.</li>
</ul>
<hr />
<h3>PART III: PRODUCTION, EVALUATION, AND MASTERY</h3>
<h3>Module 11: Taking Your Agents to Production: Deployment and Scaling</h3>
<ul>
	<li>11.1. Architecting for Scale: Designing your AI agent as a robust microservice.</li>
	<li>11.2. Cloud Deployment Mastery: Hosting and scaling your agent on a cloud platform like AWS.</li>
	<li>**11.3.**Introducing the&nbsp;MCP (Model Control Plane) Server&nbsp;concept for efficiently deploying and managing your AI microservice.</li>
	<li>11.4.The Art of Fine-Tuning: Customizing a base model to create a specialized agent.</li>
</ul>
<h3>Module 12: Evaluating and Debugging Your Agents</h3>
<ul>
	<li>12.1. Observability for LLMs: Using LangSmith to trace, monitor, and debug your agent&#39;s reasoning process.</li>
	<li>12.2. Measuring Agent Performance: Key metrics for evaluating the success and reliability of agentic systems.</li>
	<li>12.3. Automated Evaluation: Implementing the powerful &quot;LLM as a Judge&quot; technique to score your agent&#39;s outputs.</li>
</ul>
<h3>Module 13: Capstone Project: Build a Full-Stack Autonomous AI Agent</h3>
<ul>
	<li>Build the Major project using All knowledge of GenAI</li>
</ul>

GENAI Live Course

This is a Live course of Data structure and Algorithm. 
&nbsp;
Course start Date: 1 Dec 2025 
Class Timing: Mon, Tue and Thu&nbsp;(9pm) Live 
Recording will be available after the class 
DSA course: C++ 
&nbsp;
&nbsp;
<h3>Data Structures and Algorithms in C++: From Beginner to Advanced</h3>
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:
<ul>
	<li>Concept First: We focus on understanding the &quot;why&quot; behind each data structure and algorithm.</li>
	<li>Implementation Focused: Every concept will be backed by a clean, well-explained C++ implementation.</li>
	<li>Problem Solving: We will solve numerous problems to solidify understanding and prepare you for technical interviews and real-world challenges.</li>
</ul>
<hr />
<h3>Detailed Course Syllabus</h3>
<h3>Module 1: C++ Foundations for DSA</h3>
This module ensures you have the necessary C++ fundamentals before we dive into complex algorithms.
<ul>
	<li>1.1. Introduction to C++ &amp; Course Setup
	<ul>
		<li>Setting up your development environment (Compiler, IDE like VS Code).</li>
		<li>Writing your first program: &quot;Hello, World!&quot;</li>
		<li>Compilation and Execution process.</li>
	</ul>
	</li>
	<li>1.2. Variables, Data Types &amp; I/O
	<ul>
		<li>Primitive Data Types (<code>int</code>, <code>char</code>, <code>float</code>, <code>double</code>, <code>bool</code>).</li>
		<li>Input/Output streams (<code>cin</code>, <code>cout</code>).</li>
	</ul>
	</li>
	<li>1.3. Operators, Conditionals &amp; Loops
	<ul>
		<li>Arithmetic, Relational, Logical, and Bitwise Operators.</li>
		<li><code>if</code>, <code>else if</code>, <code>else</code>, <code>switch-case</code>.</li>
		<li><code>for</code>, <code>while</code>, <code>do-while</code> loops.</li>
	</ul>
	</li>
	<li>1.4. Functions in C++
	<ul>
		<li>Function declaration, definition, and calling.</li>
		<li>Parameters and return values.</li>
		<li>Pass by Value vs. Pass by Reference.</li>
	</ul>
	</li>
	<li>1.5. Pointers &amp; Dynamic Memory
	<ul>
		<li>Understanding memory addresses and the <code>&amp;</code> and operators.</li>
		<li>Pointers and Arrays.</li>
		<li>Dynamic Memory Allocation (<code>new</code> and <code>delete</code> keywords).</li>
	</ul>
	</li>
</ul>
<h3>Module 2: Core Concepts of Algorithmic Analysis</h3>
Here, we build the theoretical framework for analyzing and comparing algorithms.
<ul>
	<li>2.1. Time &amp; Space Complexity
	<ul>
		<li>Measuring algorithm efficiency.</li>
		<li>Asymptotic Notations: Big O, Big Omega (&Omega;), and Big Theta (&Theta;).</li>
		<li>Analyzing iterative and recursive code.</li>
		<li>Best, Average, and Worst-case analysis.</li>
	</ul>
	</li>
	<li>2.2. Recursion
	<ul>
		<li>The concept of recursion and the call stack.</li>
		<li>Writing base cases and recursive relations.</li>
		<li>Solving problems like Factorial, Fibonacci Series, and Tower of Hanoi.</li>
	</ul>
	</li>
</ul>
<h3>Module 3: Basic Data Structures &amp; Algorithms</h3>
We begin our DSA journey with the most fundamental structures and algorithms.
<ul>
	<li>3.1. Arrays &amp; Dynamic Arrays
	<ul>
		<li>Static vs. Dynamic Arrays.</li>
		<li>Implementing a Dynamic Array (Vector).</li>
		<li>Common array operations and problems.</li>
	</ul>
	</li>
	<li>3.2. Strings
	<ul>
		<li>C-style strings vs. C++ <code>std::string</code>.</li>
		<li>String manipulation and common string problems.</li>
	</ul>
	</li>
	<li>3.3. Searching Algorithms
	<ul>
		<li>Linear Search (Time Complexity: O(n)).</li>
		<li>Binary Search (Time Complexity: O(log n)) - Iterative and Recursive.</li>
	</ul>
	</li>
	<li>3.4. Sorting Algorithms
	<ul>
		<li>Basic Sorts: Bubble Sort, Selection Sort, Insertion Sort (O(n&sup2;)).</li>
		<li>Efficient Sorts (using Divide &amp; Conquer): Merge Sort, Quick Sort (O(n log n)).</li>
		<li>In-depth analysis of Quick Sort (pivoting strategies, worst-case).</li>
	</ul>
	</li>
</ul>
<h3>Module 4: The C++ Standard Template Library (STL)</h3>
Master the powerful, pre-built tools in C++ to write code faster and more efficiently.
<ul>
	<li>4.1. Introduction to STL
	<ul>
		<li>Overview of Containers, Iterators, and Algorithms.</li>
	</ul>
	</li>
	<li>4.2. STL Containers
	<ul>
		<li>Sequence Containers: <code>vector</code>, <code>string</code>, <code>list</code>, <code>deque</code>.</li>
		<li>Container Adaptors: <code>stack</code>, <code>queue</code>, <code>priority_queue</code>.</li>
		<li>Associative Containers: <code>set</code>, <code>map</code>, <code>multiset</code>, <code>multimap</code>.</li>
		<li>Unordered Associative Containers: <code>unordered_set</code>, <code>unordered_map</code>.</li>
	</ul>
	</li>
	<li>4.3. STL Algorithms &amp; Functors
	<ul>
		<li><code>sort</code>, <code>binary_search</code>, <code>reverse</code>, <code>max_element</code>, and more.</li>
	</ul>
	</li>
</ul>
<h3>Module 5: Object-Oriented Programming (OOP) for Data Structures</h3>
Learn to design and implement your own data structures using the principles of OOP.
<ul>
	<li>5.1. Classes and Objects</li>
	<li>5.2. The Four Pillars of OOP
	<ul>
		<li>Encapsulation, Abstraction, Inheritance, Polymorphism.</li>
	</ul>
	</li>
	<li>5.3. Constructors &amp; Destructors</li>
	<li>5.4. Building Data Structures as Classes</li>
</ul>
<h3>Module 6: Linear Data Structures</h3>
Explore data structures that store data sequentially.
<ul>
	<li>6.1. Linked Lists
	<ul>
		<li>Singly, Doubly, and Circular Linked Lists.</li>
		<li>Implementation: Insertion, Deletion, Traversal, Searching.</li>
		<li>Solving common Linked List problems.</li>
	</ul>
	</li>
	<li>6.2. Stacks
	<ul>
		<li>LIFO (Last-In, First-Out) principle.</li>
		<li>Implementation using Arrays and Linked Lists.</li>
		<li>Applications: Expression evaluation, balancing parentheses, etc.</li>
	</ul>
	</li>
	<li>6.3. Queues
	<ul>
		<li>FIFO (First-In, First-Out) principle.</li>
		<li>Implementation using Arrays and Linked Lists.</li>
		<li>Circular Queues, Deques.</li>
		<li>Applications: Schedulers, BFS traversal.</li>
	</ul>
	</li>
</ul>
<h3>Module 7: Non-Linear Data Structures</h3>
Dive into hierarchical and network-based data structures.
<ul>
	<li>7.1. Trees - Core Concepts
	<ul>
		<li>Terminology: Root, Node, Edge, Height, Depth, etc.</li>
		<li>Binary Trees: Representation and Traversals (In-order, Pre-order, Post-order, Level-order).</li>
	</ul>
	</li>
	<li>7.2. Binary Search Trees (BST)
	<ul>
		<li>Properties, Searching, Insertion, Deletion.</li>
		<li>Time complexity analysis.</li>
	</ul>
	</li>
	<li>7.3. Heaps &amp; Priority Queues
	<ul>
		<li>Binary Heaps (Min-Heap, Max-Heap).</li>
		<li>Implementation: <code>insert</code>, <code>extract</code>, <code>heapify</code>.</li>
		<li>Applications: Heap Sort, implementing Priority Queues.</li>
	</ul>
	</li>
	<li>7.4. Hashing
	<ul>
		<li>Hash functions, collision handling (Chaining, Open Addressing).</li>
		<li>Implementing a Hash Table (Map).</li>
	</ul>
	</li>
</ul>
<h3>Module 8: Advanced Algorithmic Paradigms</h3>
Master powerful techniques to solve a wide range of complex problems.
<ul>
	<li>8.1. Backtracking
	<ul>
		<li>The state-space tree concept.</li>
		<li>Solving problems: N-Queens, Sudoku Solver, Maze problems.</li>
	</ul>
	</li>
	<li>8.2. Divide and Conquer
	<ul>
		<li>Formalizing the technique (Revisiting Merge/Quick Sort).</li>
		<li>Other applications.</li>
	</ul>
	</li>
	<li>8.3. Greedy Algorithms
	<ul>
		<li>The &quot;locally optimal choice&quot; strategy.</li>
		<li>Problems: Fractional Knapsack, Activity Selection, Huffman Coding.</li>
	</ul>
	</li>
	<li>8.4. Dynamic Programming (DP)
	<ul>
		<li>Identifying DP problems (Overlapping Subproblems &amp; Optimal Substructure).</li>
		<li>Techniques: Memoization (Top-Down) vs. Tabulation (Bottom-Up).</li>
		<li>Classic DP Problems: 0/1 Knapsack, Coin Change, Longest Common Subsequence, Longest Increasing Subsequence, Longest Palindromic Subsequence, Longest Palindromic Substring, Edit Distance etc.</li>
	</ul>
	</li>
	<li>8.5. Sliding Window Technique
	<ul>
		<li>Solving problems on arrays/strings related to finding optimal subarrays/substrings.</li>
	</ul>
	</li>
</ul>
<h3>Module 9: Graphs</h3>
Explore the most versatile data structure used to model networks and relationships.
<ul>
	<li>9.1. Graph Theory Fundamentals
	<ul>
		<li>Types of Graphs (Directed, Undirected, Weighted).</li>
		<li>Graph Representation: Adjacency Matrix vs. Adjacency List.</li>
	</ul>
	</li>
	<li>9.2. Graph Traversal Algorithms
	<ul>
		<li>Breadth-First Search (BFS) and its applications.</li>
		<li>Depth-First Search (DFS) and its applications.</li>
	</ul>
	</li>
	<li>9.3. Key Graph Algorithms
	<ul>
		<li>Minimum Spanning Tree (MST): Prim&#39;s and Kruskal&#39;s Algorithms.</li>
		<li>Shortest Path Algorithms: Dijkstra&#39;s Algorithm, Bellman-Ford Algorithm, Floyd Warshall Algorithm .</li>
		<li>Topological Sort for Directed Acyclic Graphs (DAGs), Kahn&rsquo;s Algorithm.</li>
		<li>Strongly Connected Components(SCC) : Kosaraju&rsquo;s Algorithm, Tarjan&rsquo;s Algorithm.</li>
	</ul>
	</li>
</ul>
<h3>Module 10: Advanced Data Structures &amp; Topics</h3>
Topics for those who want to go the extra mile and explore specialized, high-performance structures.
<ul>
	<li>10.1. Bit Manipulation
	<ul>
		<li>Bitwise operators in-depth.</li>
		<li>Techniques for solving problems efficiently.</li>
	</ul>
	</li>
	<li>10.2. Tries (Prefix Trees)
	<ul>
		<li>Implementation and applications in string searching, autocomplete.</li>
	</ul>
	</li>
	<li>10.3. Self-Balancing Trees
	<ul>
		<li>The need for balancing (avoiding worst-case BST).</li>
		<li>Concept and Rotations in AVL Trees.</li>
	</ul>
	</li>
	<li>10.4. B-Trees and B+ Trees
	<ul>
		<li>Structure and use cases, especially in database indexing.</li>
	</ul>
	</li>
	<li>10.5. A Glimpse into Vector Databases (Conceptual)
	<ul>
		<li>What are vector embeddings?</li>
		<li>The problem of high-dimensional search.</li>
		<li>Core Algorithm</li>
	</ul>
	</li>
</ul>
<hr />
3 Final Project / Capstone:
<h2>&nbsp;</h2>
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