This is an incredible effort and a fantastic resource for the community. You have done the hard work that stops most people. Its well organised, saves people countless hours of searching and helps them avoid low-quality material. Thank you for that!. Here are a few thoughts: Inspiration for Non Technical Learners: Your journey from the "AI for Everyone" course to this is super inspiring. It's powerful proof for others with non-technical backgrounds that the AI world is accessible. It shows that passion and a structured approach are more important than a specific degree. The GitHub : Your point about GitHub is spot on and a crucial tip for newcomers. Many people see it as just a place for code and miss out on the incredible books, courses (like from Microsoft), and learning resources that are hosted there. Highlighting this is a huge help. Practical Structure: The fact that you didn't just dump links is key. By categorizing the courses (Beginner, Intermediate, Advanced) and including related fields like Data Science and interview prep, you've created a genuine learning path, not just a list. Being on this similar situation few years i have also took almost similar path. At that period i also joined some online courses so i should have some practical exposure of Project work experience also. You can also include LogicMojo in the list. Its AI course focus on project work. Many of my projects are also listed in GitHub. I can share that if any one need for preparation. Thanks for sharing the results with all of us.This list you share can help this community to build stronger. I'm saving this post and will definitely be diving into that PDF More on reddit.com

This is what AI says to take: There are many great courses available to learn about AI. Here are a few options: Introduction to Artificial Intelligence (AI) by Stanford University: This is a comprehensive course that covers a wide range of topics in AI, including machine learning, natural language processing, and robotics. Deep Learning by Andrew Ng: This is a popular course that covers the fundamentals of deep learning, including how to build neural networks and how to apply them to real-world problems. Machine Learning by Coursera: This course, also by Andrew Ng, covers a wide range of machine learning techniques and algorithms, including supervised and unsupervised learning, and how to apply them to real-world problems. Artificial Intelligence for Robotics by Georgia Tech: This course teaches students how to design intelligent agents, including robots, that can perceive, reason, and act. Introduction to Artificial Intelligence (AI) by edX: This is a self-paced online course that covers the fundamentals of AI, including machine learning, natural language processing, and computer vision. There are many other great courses available as well, these are just a few examples. More on reddit.com

You expect too much from a single course, esp. point 5 is really outside the scope. More on reddit.com

I have been doing self-study for AI/ML over the last 2 years. I learned supervised/unsupervised algorithms to working with tools like TensorFlow and PyTorch. I followed FastAI for a solid theoretical base and did all assignments in Python. One thing I realized during interviews (I've done around 10 for ML/AI roles) is that project experience matters more than just theory. Most questions asked in interviews were around the projects. So i need to work on projects from scratch I worked on two end-to-end projects during classes with LogicMojo ML live online program (we used Scikit-learn, Pandas, Google Colab, etc.). It helped me bridge the theory practice gap and gave me some deployable model experience. That hands-on work is what I talk about the most during interviews .It really shifted the conversation. Now working as a GenAI Architect and still learning every day, but definitely felt that moving from theory to practice helped unlock opportunities. More on reddit.com