Coding_knowledge
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Master DSA ๐Ÿš€

DSA MASTER TREE
โ”‚
โ”œโ”€โ”€ 1. Foundations
โ”‚ โ”œโ”€โ”€ What is Data Structure
โ”‚ โ”œโ”€โ”€ What is Algorithm
โ”‚ โ”œโ”€โ”€ Time Complexity
โ”‚ โ”‚ โ”œโ”€โ”€ Big-O
โ”‚ โ”‚ โ”œโ”€โ”€ Big-ฮฉ
โ”‚ โ”‚ โ””โ”€โ”€ Big-ฮ˜
โ”‚ โ”œโ”€โ”€ Space Complexity
โ”‚ โ””โ”€โ”€ Recurrence Relations
โ”‚
โ”œโ”€โ”€ 2. Mathematical Basics
โ”‚ โ”œโ”€โ”€ Logarithms
โ”‚ โ”œโ”€โ”€ Modular Arithmetic
โ”‚ โ”œโ”€โ”€ Prime Numbers
โ”‚ โ”œโ”€โ”€ GCD / LCM
โ”‚ โ””โ”€โ”€ Sieve of Eratosthenes
โ”‚
โ”œโ”€โ”€ 3. Arrays
โ”‚ โ”œโ”€โ”€ Traversal
โ”‚ โ”œโ”€โ”€ Searching
โ”‚ โ”‚ โ”œโ”€โ”€ Linear Search
โ”‚ โ”‚ โ””โ”€โ”€ Binary Search
โ”‚ โ”œโ”€โ”€ Prefix Sum
โ”‚ โ”œโ”€โ”€ Sliding Window
โ”‚ โ”œโ”€โ”€ Two Pointers
โ”‚ โ”œโ”€โ”€ Kadaneโ€™s Algorithm
โ”‚ โ””โ”€โ”€ Matrix / 2D Arrays
โ”‚
โ”œโ”€โ”€ 4. Strings
โ”‚ โ”œโ”€โ”€ String Manipulation
โ”‚ โ”œโ”€โ”€ Pattern Matching
โ”‚ โ”‚ โ”œโ”€โ”€ Naive
โ”‚ โ”‚ โ”œโ”€โ”€ KMP
โ”‚ โ”‚ โ”œโ”€โ”€ Rabin-Karp
โ”‚ โ”‚ โ””โ”€โ”€ Z Algorithm
โ”‚ โ”œโ”€โ”€ Palindrome Problems
โ”‚ โ”œโ”€โ”€ String Hashing
โ”‚ โ””โ”€โ”€ Trie
โ”‚
โ”œโ”€โ”€ 5. Linked Lists
โ”‚ โ”œโ”€โ”€ Singly Linked List
โ”‚ โ”œโ”€โ”€ Doubly Linked List
โ”‚ โ”œโ”€โ”€ Circular Linked List
โ”‚ โ”œโ”€โ”€ Reverse Linked List
โ”‚ โ”œโ”€โ”€ Cycle Detection (Floyd)
โ”‚ โ””โ”€โ”€ Merge Lists
โ”‚
โ”œโ”€โ”€ 6. Stack
โ”‚ โ”œโ”€โ”€ Stack Implementation
โ”‚ โ”œโ”€โ”€ Balanced Parentheses
โ”‚ โ”œโ”€โ”€ Next Greater Element
โ”‚ โ”œโ”€โ”€ Monotonic Stack
โ”‚ โ””โ”€โ”€ Min Stack
โ”‚
โ”œโ”€โ”€ 7. Queue
โ”‚ โ”œโ”€โ”€ Queue Implementation
โ”‚ โ”œโ”€โ”€ Circular Queue
โ”‚ โ”œโ”€โ”€ Deque
โ”‚ โ”œโ”€โ”€ Priority Queue
โ”‚ โ””โ”€โ”€ Monotonic Queue
โ”‚
โ”œโ”€โ”€ 8. Hashing
โ”‚ โ”œโ”€โ”€ Hash Tables
โ”‚ โ”œโ”€โ”€ Collision Handling
โ”‚ โ”‚ โ”œโ”€โ”€ Chaining
โ”‚ โ”‚ โ””โ”€โ”€ Open Addressing
โ”‚ โ”œโ”€โ”€ Load Factor
โ”‚ โ””โ”€โ”€ Rehashing
โ”‚
โ”œโ”€โ”€ 9. Trees
โ”‚ โ”œโ”€โ”€ Binary Tree
โ”‚ โ”‚ โ”œโ”€โ”€ Traversals
โ”‚ โ”‚ โ”‚ โ”œโ”€โ”€ Inorder
โ”‚ โ”‚ โ”‚ โ”œโ”€โ”€ Preorder
โ”‚ โ”‚ โ”‚ โ””โ”€โ”€ Postorder
โ”‚ โ”‚ โ”œโ”€โ”€ Height / Depth
โ”‚ โ”‚ โ””โ”€โ”€ Diameter
โ”‚ โ”œโ”€โ”€ Binary Search Tree
โ”‚ โ”œโ”€โ”€ AVL Tree
โ”‚ โ”œโ”€โ”€ Red-Black Tree
โ”‚ โ”œโ”€โ”€ Segment Tree
โ”‚ โ”œโ”€โ”€ Fenwick Tree
โ”‚ โ””โ”€โ”€ Heap
โ”‚ โ”œโ”€โ”€ Min Heap
โ”‚ โ””โ”€โ”€ Max Heap
โ”‚
โ”œโ”€โ”€ 10. Graphs
โ”‚ โ”œโ”€โ”€ Graph Representation
โ”‚ โ”‚ โ”œโ”€โ”€ Adjacency Matrix
โ”‚ โ”‚ โ””โ”€โ”€ Adjacency List
โ”‚ โ”œโ”€โ”€ BFS
โ”‚ โ”œโ”€โ”€ DFS
โ”‚ โ”œโ”€โ”€ Topological Sort
โ”‚ โ”œโ”€โ”€ Cycle Detection
โ”‚ โ”œโ”€โ”€ Shortest Path
โ”‚ โ”‚ โ”œโ”€โ”€ Dijkstra
โ”‚ โ”‚ โ”œโ”€โ”€ Bellman-Ford
โ”‚ โ”‚ โ””โ”€โ”€ Floyd-Warshall
โ”‚ โ”œโ”€โ”€ Minimum Spanning Tree
โ”‚ โ”‚ โ”œโ”€โ”€ Kruskal
โ”‚ โ”‚ โ””โ”€โ”€ Prim
โ”‚ โ””โ”€โ”€ Disjoint Set (Union-Find)
โ”‚
โ”œโ”€โ”€ 11. Recursion & Backtracking
โ”‚ โ”œโ”€โ”€ Recursion Basics
โ”‚ โ”œโ”€โ”€ Subsets
โ”‚ โ”œโ”€โ”€ Permutations
โ”‚ โ”œโ”€โ”€ N-Queens
โ”‚ โ””โ”€โ”€ Sudoku Solver
โ”‚
โ”œโ”€โ”€ 12. Greedy Algorithms
โ”‚ โ”œโ”€โ”€ Activity Selection
โ”‚ โ”œโ”€โ”€ Huffman Coding
โ”‚ โ”œโ”€โ”€ Fractional Knapsack
โ”‚ โ””โ”€โ”€ Job Scheduling
โ”‚
โ”œโ”€โ”€ 13. Dynamic Programming
โ”‚ โ”œโ”€โ”€ Memoization
โ”‚ โ”œโ”€โ”€ Tabulation
โ”‚ โ”œโ”€โ”€ 1D DP
โ”‚ โ”œโ”€โ”€ 2D DP
โ”‚ โ”œโ”€โ”€ Knapsack Variants
โ”‚ โ”œโ”€โ”€ Longest Common Subsequence
โ”‚ โ”œโ”€โ”€ Longest Increasing Subsequence
โ”‚ โ””โ”€โ”€ Matrix Chain Multiplication
โ”‚
โ”œโ”€โ”€ 14. Bit Manipulation
โ”‚ โ”œโ”€โ”€ Bitwise Operators
โ”‚ โ”œโ”€โ”€ Set / Clear Bits
โ”‚ โ”œโ”€โ”€ Count Set Bits
โ”‚ โ””โ”€โ”€ XOR Tricks
โ”‚
โ”œโ”€โ”€ 15. Advanced DSA
โ”‚ โ”œโ”€โ”€ Sparse Table
โ”‚ โ”œโ”€โ”€ Heavy-Light Decomposition
โ”‚ โ”œโ”€โ”€ Treap
โ”‚ โ”œโ”€โ”€ Splay Tree
โ”‚ โ””โ”€โ”€ Skip List
โ”‚
โ””โ”€โ”€ 16. Interview Patterns
โ”œโ”€โ”€ Two Pointer Pattern
โ”œโ”€โ”€ Sliding Window Pattern
โ”œโ”€โ”€ Binary Search Pattern
โ”œโ”€โ”€ BFS / DFS Pattern
โ”œโ”€โ”€ Greedy Choice Pattern
โ””โ”€โ”€ DP Pattern Recognition
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Best ATS Resume Templates.pdf
45 KB
Best ATS Resume Templates ๐Ÿฉท

HCL Tech Hiring Link ๐Ÿ”— : https://careers.hcltech.com/go/India/9553955/
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7 claude prompts to automate you bussiness.pdf
10.9 MB
7 claude prompts to automate you bussiness โ˜•๏ธ

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๐Ÿš€ Generative AI Basics You Should Know

๐Ÿ‘‰ Generative AI = AI that can CREATE new content

Instead of just predicting, it can generate:
- Text
- Images
- Code
- Audio
- Videos

๐ŸŽฏ Real-Life Examples
- ChatGPT โ†’ generates answers
- DALLยทE / Midjourney โ†’ generate images
- GitHub Copilot โ†’ writes code
- AI voice tools โ†’ generate speech

๐Ÿ”ฅ Why Generative AI is Important
- Highest demand skill in AI
- Used in almost every industry
- Huge salary boost
- Fastest growing field

๐Ÿ”น How Generative AI Works (Big Idea)
๐Ÿ‘‰ Model learns patterns from huge data
๐Ÿ‘‰ Then generates new similar content
Example: Trained on millions of texts โ†’ Generates new sentences

๐Ÿ”น Types of Generative AI Models
- Large Language Models (LLMs) โญ
- Work with text
- Examples: GPT (ChatGPT), BERT, LLaMA
- What they do: Answer questions, Summarize, Translate, Chat
- Diffusion Models
- Used for image generation
- How: Start with noise, Gradually create image
- Examples: Stable Diffusion, DALLยทE
- GANs
- Generate realistic fake data
- Used for: Face generation, Deepfake videos

๐Ÿ”น Prompt Engineering (Very Important ๐Ÿ”ฅ)
๐Ÿ‘‰ How you talk to AI matters
Example:
โŒ Bad prompt: "Tell me about AI"
โœ… Good prompt: "Explain AI in simple terms with real-world examples for beginners"
๐Ÿ‘‰ Better prompt = Better output

๐Ÿ”น Common Generative AI Tasks
- Text generation
- Image generation
- Code generation
- Chatbots
- Content creation

๐Ÿ› ๏ธ Tools You Must Learn
- OpenAI APIs
- Hugging Face
- LangChain
- Vector databases (basic idea)

๐ŸŽฏ Where Generative AI is Used
- Content creation
- Marketing
- Customer support
- Coding assistants
- Education

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CLAUDE_CAN_NOW_PREPARE_YOUR_PRESENTATIONS_USING_THE_EXACT_FRAMEWORK.pdf
3.8 MB
CLAUDE CAN NOW PREPARE YOUR PRESENTATIONS USING THE EXACT FRAMEWORK PATRICK WINSTON TAUGHT MIT STUDENTS FOR 40 YEARS โ˜•๏ธ

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โค18๐Ÿ‘3
๐Ÿš€ I just shared something important about DSA that most people donโ€™t talk about.

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โค9
โœ… Artificial Intelligence (AI) Learning Roadmap ๐Ÿค–๐Ÿง 

1๏ธโƒฃ Programming Foundations
- Learn Python (must-have)
- Practice with NumPy, Pandas, Matplotlib

2๏ธโƒฃ Math for AI
- Linear Algebra: Vectors, matrices
- Probability & Statistics
- Calculus (basics: derivatives, gradients)
- Optimization (gradient descent)

3๏ธโƒฃ Machine Learning Basics
- Supervised vs Unsupervised Learning
- Regression, classification, clustering
- Learn scikit-learn
- Evaluation metrics (accuracy, F1, confusion matrix)

4๏ธโƒฃ Deep Learning
- Neural networks: forward pass, backpropagation
- Activation functions, loss functions
- Use TensorFlow or PyTorch
- CNNs, RNNs, LSTMs

5๏ธโƒฃ Natural Language Processing (NLP)
- Tokenization, stemming, embeddings
- Transformer architecture (BERT, GPT)
- Sentiment analysis, summarization, translation

6๏ธโƒฃ Computer Vision
- Image classification, object detection
- Libraries: OpenCV, YOLO, Mediapipe

7๏ธโƒฃ Generative AI
- GANs (Generative Adversarial Networks)
- Diffusion models
- Prompt engineering & LLMs (ChatGPT, Claude, Gemini)

8๏ธโƒฃ AI Project Ideas
- Chatbot
- Image caption generator
- AI-powered recommendation system
- Text-to-image generator

9๏ธโƒฃ AI Ethics & Safety
- Bias in AI
- Privacy, fairness
- Responsible AI development

๐Ÿ”Ÿ Tools to Learn
- OpenAI API, Hugging Face, LangChain
- Git & GitHub
- Docker (for deployment)

1๏ธโƒฃ1๏ธโƒฃ Deployment Skills
- Streamlit / Flask for web apps
- Deploy AI models on Hugging Face, Vercel, or AWS

1๏ธโƒฃ2๏ธโƒฃ Stay Updated
- Follow arXiv, PapersWithCode
- Join AI communities (Discord, Reddit, LinkedIn)

๐Ÿ’ผ Pro Tip: Build 2โ€“3 AI projects, share them on GitHub, and write a blog/post about your learnings.

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โค9
7 Prompts that will outperform 99% of agents.pdf
11.2 MB
7 Prompts that will outperform 99% of agents ๐Ÿš€

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