This repository provides a curated roadmap for software engineers to learn machine learning fundamentals. It offers a structured path covering essential math, programming, core ML concepts, and career preparation, aiming to enable independent exploration of ML topics.

How It Works

The roadmap is a collection of links to high-quality, often free, educational resources from reputable institutions and creators. It prioritizes essential topics and efficient learning, guiding users through foundational knowledge to advanced areas like LLMs, computer vision, and ML engineering. The structure is designed to build a solid understanding progressively.

Quick Start & Requirements

• Install/Run: No installation required; it's a guide to external resources.
• Prerequisites: Basic programming knowledge is assumed. Specific resources may have their own requirements (e.g., Python, Git).
• Setup Time: N/A.
• Links:
  • Society's Backend (Newsletter)
  • MLforswes GitHub

Highlighted Details

• Comprehensive coverage from programming and math fundamentals to advanced ML topics and job skills.
• Includes resources for specific ML domains like NLP, Computer Vision, and Reinforcement Learning.
• Provides guidance on ML engineering, cloud platforms (GCP, AWS, Azure), and DevOps tools.
• Lists options for free GPU access (Google Colab, Kaggle Notebooks, Lightning AI).

Maintenance & Community

The project is maintained by loganthorneloe, who also runs the "Society's Backend" newsletter. Direct contact is encouraged for feedback or resource removal.

Licensing & Compatibility

The repository itself is likely under a permissive license (e.g., MIT, Apache 2.0) as it aggregates links to external resources. The licensing of the linked resources varies, with many being free or open-source. Compatibility for commercial use depends on the licenses of the individual resources used.

Limitations & Caveats

This is a curated list of resources, not a self-contained learning platform. The quality and availability of external links are subject to change. The roadmap's effectiveness relies on the user's self-discipline and ability to navigate diverse learning materials.