This repository serves as a comprehensive, beginner-friendly guide to machine learning, offering curated resources for learning, practice, and career development. It targets individuals new to ML, students, and aspiring data scientists by providing structured learning paths, tutorials, datasets, and community engagement opportunities.

How It Works

The project is a curated list of links and resources, organized by topic, covering the entire machine learning workflow from data collection to deployment. It includes theoretical explanations, practical tutorials, recommended books, datasets for practice, relevant GitHub repositories, YouTube channels, forums, courses, projects, and internship/interview preparation materials. This approach aims to provide a centralized, accessible knowledge base for self-directed learning.

Quick Start & Requirements

This repository is a collection of links and does not require installation. Users can directly access the resources via their web browser.

Highlighted Details

• Extensive coverage of the ML workflow, including data preparation, model selection, training, evaluation, optimization, and deployment.
• Curated lists of popular ML algorithms with links to tutorials and explanations.
• A wide array of learning resources, including books (free and paid), datasets from Kaggle, UCI, and Google, and numerous YouTube channels and courses.
• Dedicated sections for project ideas, internship opportunities, and interview preparation.

Maintenance & Community

The repository is maintained by recodehive and has 142 contributors. It links to the recodehive website and social media profiles.

Licensing & Compatibility

The repository itself is a collection of links and does not have a specific license. The linked resources may have their own licenses.

Limitations & Caveats

As a curated list, the quality and availability of external resources are subject to change. The repository does not host any code or models directly, relying entirely on external links.