CampusX (YouTube channel)

What the channel covers

CampusX is run by Nitish Singh, who describes it as an online gateway into data science with a belief that quality education should be a right, not a privilege. The channel teaches in Hindi, which makes it easy to follow for learners across India and Pakistan who think more comfortably in Hindi or Urdu than in English. Technical terms stay in English, so you still learn the vocabulary you will need at work.

What sets the channel apart is depth. Topics are taught like a university course: intuition first, then the maths, then code. Playlists run to dozens of videos and are designed to be followed in order.

Playlists to start with

• Maths for Machine Learning, Probability Basics (ML Roadmap 2022) and Advance Statistics (ML Roadmap 2022): the foundations many learners skip.
• SQL for Data Science: the query skills every data role needs.
• 100 Days of Deep Learning and Practical Deep Learning using PyTorch | CampusX: neural networks from first principles to working models.
• Deep Learning Optimizers, Regularization, XgBoost and Machine Learning Metrics: focused deep dives.
• Generative AI using LangChain, RAG Playlist, Agentic AI using LangGraph and Model Context Protocol: the modern AI engineering stack.
• FastAPI for Machine Learning | CampusX and 100 Days of MLOps: serving and running models in production.
• LLM Evaluation and Memory in LLMs: newer playlists on making LLM apps reliable.

Who it suits

Students from a non-elite university who want a full, free course rather than scattered videos; career switchers into data science; and anyone who learns better in Hindi or Urdu. Absolute beginners can start with the maths and SQL playlists. Developers who already code can jump straight to the generative AI playlists.

How to learn from it without getting stuck in tutorial loops

The long playlists can take months, so set rules before you start:

• Fix a pace. One or two videos a day, with notes, is better than weekend binges.
• Do one Kaggle notebook per topic. After a module, apply it to a dataset of your choice.
• Build one project per playlist. A deep learning image classifier, a RAG app over your notes, a FastAPI model server. Deploy at least one.
• Explain it back in English. Write a short English summary of each module; it helps when you discuss your work in English later.

Pair it with on Sefism

• Follow the Data science roadmap or Machine learning roadmap, with the data science cheat sheet for revision.
• For LLM apps, the AI engineering roadmap and MLOps and LLMOps roadmap.
• Practise on Kaggle Learn and Kaggle Competitions.
• For a second explanation of statistics, try StatQuest with Josh Starmer.

Best for: Hindi and Urdu speaking learners who want a complete, free, course-style path through data science and AI.