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Master Tech Stacks.
Build Real Clarity.

Learn Frontend , Backend and System Design through structured roadmaps, deep explanations and Interview-focused learning

  • Beginner to Advanced structured roadmaps
  • Real-world examples & mental models
  • Interview-focused learning paths
Master Tech Stacks

Learning Stacks

Structured learning paths that guide you step by step, helping you build practical skills used in real projects and jobs.

Frontend - (Website Development)

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JavaScript

Bring websites to life.

  • Core JavaScript concepts
  • DOM manipulation
  • Async programming
  • Writing clean, readable code

React

Build scalable user interfaces.

  • Component thinking
  • State & props
  • Hooks & rendering flow
  • Building real projects

Redux

Manage and scale application state.

  • Redux concepts and architecture
  • State management patterns
  • store, actions, reducers
  • Debugging and optimizing state flow

Next Js

Build fast, SEO-friendly

  • Server-Side Rendering (SSR)
  • Static Site Generation (SSG)
  • Client and Server Components (App Router)
  • SEO optimization and metadata handling

Web Fundamentals

how web and browsers actually work.

  • How the web works
  • Cookies, LocalStorage, and SessionStorage
  • HTTP basics, status codes, and headers
  • Same-origin policy and CORS fundamentals

Website Performance

Build fast, scalable, and high-performing.

  • web performance (LCP, FCP, CLS, TTI)
  • Optimizing asset loading
  • Code splitting and lazy loading strategies
  • Improving rendering performance and reflows

Frontend System Design

Design scalable and production-ready

  • Designing large-scale frontend applications
  • right state management approach
  • Performance and scalability considerations
  • Code splitting and module organization

Frontend - (Mobile App Development)

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React Native

Build cross-platform mobile apps.

  • React Native components & layout system
  • Styling with Flexbox
  • Navigation and screen management
  • Building real-world mobile apps

java

Core Java programming and backend fundamentals.

  • Core Java & OOP principles
  • Collections & generics
  • Concurrency & multithreading
  • JVM tooling and debugging

springboot

Build production-ready Java backend services with Spring Boot.

  • Spring Boot fundamentals & starters
  • REST API design with Spring MVC
  • Spring Data JPA & database integration
  • Security, testing, and deployment

Databases

Design, query and scale databases for production.

  • Relational schema design
  • Indexing & query optimization
  • Transactions & ACID
  • Replication & scaling patterns

Node.js

Build scalable backend services with JavaScript.

  • Node runtime & async patterns
  • Express and middleware
  • REST API design & authentication
  • Testing and deployment

APIs

Connect applications seamlessly.

  • RESTful API design
  • Authentication & security
  • Error handling
  • API documentation

AI & Machine Learning

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Python Fundamentals

The language for AI and ML development.

  • Python syntax and data structures
  • Libraries: NumPy, Pandas, Scikit-learn
  • Data manipulation and analysis
  • Writing efficient and clean code

Machine Learning Basics

Build intelligent systems from data.

  • ML fundamentals and algorithms
  • Supervised vs unsupervised learning
  • Feature engineering and preprocessing
  • Model training and evaluation

Deep Learning

Harness neural networks for complex tasks.

  • Neural networks and backpropagation
  • TensorFlow and PyTorch frameworks
  • CNNs for image processing
  • RNNs and Transformers for sequence learning

Natural Language Processing

Teach computers to understand language.

  • Text preprocessing and tokenization
  • Word embeddings and representations
  • Building NLP models with transformers
  • Sentiment analysis and text classification

Large Language Models (LLMs)

Work with state-of-the-art AI models.

  • Understanding LLM architectures
  • Prompt engineering best practices
  • Fine-tuning and transfer learning
  • Integrating LLMs into applications

AI/ML Project Development

Deploy AI models in production.

  • Project structure and best practices
  • Data collection and preprocessing pipelines
  • Model deployment and serving
  • Monitoring and maintaining ML systems

Problem Solving & DSA

Comprehensive problem-solving and DSA guides covering common patterns, coding challenges, and system design concepts to build strong algorithmic skills.

Problem Solving

Problem Solving Questions

Build algorithmic thinking and structured problem-solving approaches.

  • Problem decomposition
  • Pattern recognition
  • Complexity analysis (time & space)
  • Practice strategies & mock tests

DSA Questions

Master core data structures and algorithmic techniques.

  • Arrays & Strings
  • Linked Lists & Trees
  • Graphs & BFS/DFS
  • Dynamic Programming & Greedy