Removed for off-topic replies.
Learning AI in a structured way is definitely the right approach because there are many areas to cover and it can become overwhelming without a clear roadmap. I would suggest starting with strong foundations in mathematics, Python programming, and basic machine learning concepts before moving into deep learning, neural networks, transformers, and LLMs. After understanding the theory, focus on hands-on projects, model training, fine-tuning, and deployment to gain practical experience. Courses are helpful, but building real projects and experimenting with different models is what truly improves your skills. Consistency and curiosity matter more than trying to learn everything at once.
A structured way to learn AI is to start with the fundamentals, then gradually move toward practical projects. It helps to learn concepts such as machine learning, neural networks, NLP, and generative AI while actually building small projects along the way. Platforms such as Secrets.ai can also be interesting as a real-world example of how AI is applied to personalized conversational experiences. Looking at how these systems handle conversations, personalization, and AI-generated responses can make theoretical concepts easier to connect with practical applications. Consistent practice, documentation, and project-based learning are probably the best ways to build stronger AI skills over time.
ok best way to learn is together ask ai you will make a real world production ready project and ai will teach you while you are making it what it did why it did that andhow it did that. its the best way