The-Grand-Complete-Data-Science-Materials
A curated directory of YouTube playlists covering data science from beginner Python through machine learning, deep learning, and MLOps, no code included, just links to structured video learning paths.
This repository is a curated collection of links to data science and machine learning learning materials, almost all of which are video playlists on YouTube. The content covers a broad path from beginner Python programming through to production deployment and generative AI topics.
The list is organized into 14 sections. Early sections cover Python fundamentals, statistics, SQL, and version control with Git and GitHub, with playlists available in both English and Hindi. Later sections move into exploratory data analysis, feature engineering, and machine learning. Deep learning and natural language processing topics have their own playlists. There are also sections dedicated to deployment frameworks like Flask, Gradio, and BentoML, cloud services through AWS SageMaker, and MLOps tooling including Docker, MLflow, and model monitoring.
Section 11 gathers end-to-end project walkthroughs that show the full lifecycle of a machine learning project from data preparation through to deployment. Section 12 covers generative AI topics including OpenAI usage and the Langchain library, though those playlists are noted as still in progress. A PySpark tutorial series for large-scale data processing is also included.
The README links to a tracker spreadsheet for planning a learning path, a list of interview questions hosted on a separate GitHub repository, and an internship listing. All the video content lives on the Krish Naik YouTube channels, which are also linked. There is no code in the repository itself; everything here is a directory of external learning resources.
Where it fits
- Follow a structured video curriculum to learn data science from Python basics to production deployment.
- Find curated YouTube playlists on specific topics like NLP, deep learning, or MLOps without searching manually.
- Use the tracker spreadsheet to plan and monitor your own learning path through the material.
- Prepare for data science interviews using the linked question bank and end-to-end project walkthroughs.