What’s a practical roadmap for learning generative AI from scratch before fresher interviews?

For an entry-level role, aim for a combination of fundamentals and practical work. Learn Python, data structures, basic algorithms, statistics, machine learning, and neural networks. Then create a few projects that demonstrate useful skills, such as building a document-question-answering system, evaluating different prompts or models, or fine-tuning a small open model. Be prepared to explain your design choices, limitations, and results during interviews.

Build a strong foundation in linear algebra, probability, and statistics first. You don’t need to memorize every proof, but you should be comfortable enough with the math to understand how models work and read the technical sections of machine-learning papers. After that, study core machine learning and deep learning, then move into transformers, large language models, embeddings, retrieval-augmented generation, and model evaluation. Courses can help, but keep in mind that specific tools and frameworks change quickly, so focus on concepts rather than following a roadmap blindly.

A computer science or machine-learning degree can provide a structured path, especially if you want to work on research or model development. Self-study is still possible, but it usually requires more discipline and may take longer to reach an interview-ready level. Either way, prioritize understanding papers and the underlying methods instead of only learning how to call an AI API.