CURRICULUM & TRACKS

The 3 Core Domains

CodeLABS is structured into 3 dedicated technical tracks at JMIT Radaur. Explore each domain's curriculum, tools, and project tracks.

DOMAIN 01

AI & Machine Learning

Track: Intelligence & Data

Domain Scope & Objectives

The AI and ML domain introduces students to modern machine learning concepts, data science, neural networks, computer vision, and natural language processing. Members learn to work with real-world datasets and build practical intelligent applications.

LEARNING TRACK MODULES:

01. Python & Data Foundations: NumPy, Pandas, Matplotlib, exploratory data analysis, and mathematical fundamentals.
02. Machine Learning Algorithms: Regression, classification, clustering, decision trees, and model evaluation using Scikit-Learn.
03. Deep Learning & Neural Networks: PyTorch and TensorFlow fundamentals, Convolutional Neural Networks for vision, and transfer learning.
04. NLP & Modern AI Applications: Text processing, sentiment analysis, Hugging Face transformers, and integrating AI models into web apps.

PRIMARY TOOLCHAIN:

Python PyTorch TensorFlow Scikit-Learn OpenCV Pandas / NumPy Hugging Face

SAMPLE PROJECTS:

Campus sentiment tracker, real-time object detection apps, student question-answering assistants, and Kaggle competition entries.

DOMAIN 02

Development (Frontend, Backend, Fullstack)

Track: MERN & Java Fullstack

Domain Scope & Objectives

The Development domain covers modern web and software engineering across both popular fullstack ecosystems: MERN Stack (MongoDB, Express, React, Node.js) and Java Enterprise Development (Core Java, Spring Boot, REST APIs, Microservices). Students build full real-world applications from UI to database.

LEARNING TRACK MODULES:

01. Frontend Engineering: HTML5, CSS3, Modern JavaScript, React, Next.js, and responsive UI design systems.
02. MERN Stack Backend: Node.js, Express REST APIs, MongoDB, JWT authentication, and middleware architecture.
03. Java Backend & Spring Boot: Core Java, OOP design, Spring Boot 3, Spring Data JPA, Hibernate, and enterprise microservices.
04. Databases & Deployment: SQL (MySQL, PostgreSQL), NoSQL (MongoDB), Git/GitHub collaboration, Docker basics, and cloud deployment.

PRIMARY TOOLCHAIN:

React & Next.js Node.js & Express Java Spring Boot MongoDB PostgreSQL REST APIs Git / GitHub

SAMPLE PROJECTS:

College club portal, event ticketing platforms, fullstack e-commerce in Java Spring Boot and React, and real-time chat apps.

DOMAIN 03

Technical (Data Structures & Algorithms)

Track: DSA in C++ & Java

Domain Scope & Objectives

The Technical and DSA domain is focused on mastering core algorithmic problem solving in both C++ and Java. We train students from foundational arrays and recursion up to advanced dynamic programming, trees, and graphs for coding rounds and contests.

LEARNING TRACK MODULES:

01. Language Mastery & Standard Libraries: C++ Standard Template Library (vectors, maps, sets, priority queue) and Java Collections Framework.
02. Linear & Non-Linear Data Structures: Arrays, Strings, Two Pointers, Sliding Window, Linked Lists, Stacks, Queues, Binary Trees, and BSTs.
03. Graph Algorithms & Greedy: BFS, DFS, Dijkstra, Bellman-Ford, Disjoint Set Union, and Topological Sort.
04. Dynamic Programming & Contest Prep: 1D/2D DP, Knapsack variations, recursion with memoization, LeetCode daily streaks, and Codeforces practice.

PRIMARY PLATFORMS:

C++ STL Java Collections LeetCode Codeforces GeeksforGeeks CodeChef

WEEKLY ACTIVITIES:

Weekly CodeLABS problem sets, internal peer contests, problem discussion sessions, and coding interview preparation roadmaps.

CHOOSE YOUR DOMAIN AND JOIN CODELABS

Applications for all 3 domain tracks will open through our external application form in September 2026.

Apply Now →