This repository offers a comprehensive, 60-day curriculum designed to guide individuals from Python basics to advanced machine learning concepts and projects. It targets aspiring data scientists and ML engineers seeking practical, industry-aligned skills through a structured learning path. The primary benefit is a curated collection of resources and project implementations to build proficiency.

How It Works

The repository is structured as a day-by-day learning plan, with each day focusing on specific Python concepts, mathematical foundations, data manipulation libraries (Pandas, NumPy), data preprocessing techniques, various ML algorithms (regression, classification, clustering), and deep learning frameworks (TensorFlow, PyTorch). Each topic is accompanied by code implementations and project examples, often linked to external resources for deeper dives.

Quick Start & Requirements

• Install/Run: No specific installation or run commands are provided. The content consists of links to external articles and project explanations.
• Prerequisites: Python, familiarity with data science libraries (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch), and potentially access to external articles and YouTube videos.
• Setup Time: N/A, as it's a curated list of learning resources rather than a runnable codebase.
• Links: Numerous links to external articles and YouTube videos are embedded throughout the README.

Highlighted Details

• Covers 60 days of structured learning, progressing from Python fundamentals to advanced ML.
• Includes practical project implementations for key ML algorithms and deep learning models.
• Features resources for ML system design, tech interview preparation, and data visualization.
• Integrates popular libraries like Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, and BERT.

Maintenance & Community

• The repository is maintained by Coder-World04.
• Links to other curated series (MLOps, NLP, Data Engineering) and resource lists are provided, suggesting an active curation effort.

Licensing & Compatibility

• The repository itself does not specify a license. The linked external resources will have their own licenses.

Limitations & Caveats

This repository is a curated list of links and does not contain executable code directly. Users must follow the external links to access the actual content and projects, which may have varying quality and licensing. The "60 days" structure implies a self-paced learning plan rather than a runnable program.