GFG Learning Hub

Includes:

• •Array and string manipulation techniques
• •Linked lists, stacks, and queues implementation
• •Tree and graph traversal algorithms
• •Hash tables and collision resolution

Includes:

• •Sorting algorithms and their complexity
• •Binary search and its variations
• •Dynamic programming approach and problems
• •Greedy algorithms with real-world applications

Includes:

• •Process scheduling and synchronization
• •Memory management techniques
• •File system implementation
• •Deadlock prevention and recovery

Includes:

• •SQL query optimization techniques
• •Database normalization and design
• •Transaction management and ACID properties
• •NoSQL databases and their use cases

Includes:

• •TCP/IP protocol suite in depth
• •Network security fundamentals
• •Routing algorithms and protocols
• •Socket programming and network applications

Includes:

• •Finite automata and regular expressions
• •Context-free grammars and pushdown automata
• •Turing machines and computability
• •Complexity classes (P, NP, NP-complete)

Includes:

• •Memory management and pointers
• •Object-oriented programming in C++
• •STL containers and algorithms
• •Multi-threading and concurrency

Includes:

• •Core Java and OOP concepts
• •Collections framework and generics
• •Multithreading and concurrency
• •Java frameworks (Spring, Hibernate)

Includes:

• •Python fundamentals and data structures
• •Object-oriented programming in Python
• •Libraries for data science (NumPy, Pandas)
• •Web frameworks (Django, Flask)

Includes:

• •ES6+ features and syntax
• •DOM manipulation and events
• •Asynchronous JavaScript
• •Web APIs and fetch
• •Modern ES6+ features

Includes:

• •Go syntax and basic constructs
• •Concurrency with goroutines and channels
• •Error handling patterns
• •Web services and microservices

Includes:

• •Ownership, borrowing, and lifetimes
• •Pattern matching and error handling
• •Concurrency without data races
• •Systems programming and WebAssembly

Includes:

• •Two-pointer techniques
• •Sliding window algorithms
• •String manipulation and pattern matching
• •Matrix operations and traversals

Includes:

• •Singly and doubly linked lists
• •Fast and slow pointer techniques
• •Cycle detection algorithms
• •Merge and sort operations

Includes:

• •Tree traversals (in-order, pre-order, post-order)
• •Binary search trees and balancing
• •Graph representations and traversals
• •Shortest path algorithms

Includes:

• •Memoization and tabulation techniques
• •Classic DP problems (knapsack, LCS)
• •State transitions and recurrence relations
• •Optimization problems

Includes:

• •Activity selection problems
• •Huffman coding and data compression
• •Minimum spanning trees
• •Interval scheduling algorithms

Includes:

• •Contest strategies and time management
• •Problem-solving patterns
• •Advanced algorithm techniques
• •Mock contests and practice problems

Includes:

• •Semantic HTML5 elements and structure
• •CSS layouts (Flexbox, Grid)
• •Responsive design principles
• •CSS animations and transitions

Includes:

• •DOM manipulation and events
• •Asynchronous JavaScript
• •Web APIs and fetch
• •Modern ES6+ features

Includes:

• •Component architecture
• •State management (Context, Redux)
• •Hooks and functional components
• •Performance optimization techniques

Includes:

• •Express.js framework
• •RESTful API design
• •Database integration
• •Authentication and authorization

Includes:

• •Client-server architecture
• •API integration and design
• •Database design and ORM
• •Deployment and DevOps basics

Includes:

• •OWASP top 10 vulnerabilities
• •Authentication and authorization
• •HTTPS and TLS
• •Cross-site scripting (XSS) prevention

Includes:

• •Supervised and unsupervised learning
• •Linear regression and classification
• •Decision trees and ensemble methods
• •Model evaluation and validation

Includes:

• •Feedforward and convolutional neural networks
• •Recurrent neural networks (RNNs, LSTMs)
• •Backpropagation and optimization
• •TensorFlow and PyTorch frameworks

Includes:

• •Text preprocessing and tokenization
• •Sentiment analysis and classification
• •Named entity recognition
• •Transformer models and BERT

Includes:

• •Image processing and feature extraction
• •Object detection and recognition
• •Convolutional neural networks for vision
• •OpenCV and image manipulation

Includes:

• •Data cleaning and preprocessing
• •Statistical analysis and hypothesis testing
• •Data visualization with matplotlib/seaborn
• •Pandas and NumPy for data manipulation

Includes:

• •Bias detection and mitigation
• •Fairness in machine learning
• •Privacy-preserving AI techniques
• •Explainable AI and interpretability

Includes:

• •Problem-solving approaches
• •Time and space complexity analysis
• •Common interview patterns
• •Mock interview practice

Includes:

• •Scalability and performance
• •Microservices architecture
• •Database design and sharding
• •Load balancing and caching

Includes:

• •SOLID principles
• •Design patterns (Creational, Structural, Behavioral)
• •UML diagrams and modeling
• •Code refactoring techniques

Includes:

• •STAR method for answering questions
• •Leadership and teamwork examples
• •Conflict resolution scenarios
• •Communication skills practice

Includes:

• •Resume formatting and structure
• •Project descriptions and impact statements
• •Skills section optimization
• •ATS-friendly resume techniques

Includes:

• •Technical coding interviews
• •System design interview practice
• •Behavioral question preparation
• •Feedback and improvement strategies