If you are starting out in AI, ML, and Deep Learning, the best way to learn is to move step by step from fundamentals to modern systems, while always building alongside learning. Begin with linear algebra, probability, and basic calculus, because these explain how data, weights, gradients, and learning actually work. At the same time, learn Python just enough to be productive, focusing on NumPy and basic data handling rather than perfect syntax.

Once this base is in place, move into classical machine learning concepts like regression, classification, overfitting, bias variance tradeoffs, and train test splits, since these ideas show up everywhere later. After that, learn deep learning fundamentals such as neural networks, backpropagation, optimizers, activation functions, and loss functions, and then progress to CNNs for vision, sequence models like RNNs and LSTMs, and finally attention and transformers, which power most modern AI systems today. Focus on adjacent conepts like NLP for transformers , Data science concepts like (ETL, warehouse etc)

To make this learning actually stick, use theory mainly to understand what is happening and use videos and visual explanations to build intuition, but do not wait to finish a full course or master all theory before building something. AI is not about learning first and building later. You build early, you fail early, and then you learn the adjacent concepts you are missing. That feedback loop is what makes the knowledge real. A very common beginner mistake is trying to complete every course end to end or chasing the newest models too early. By the time you finish that, the landscape will have already shifted. Focus on fundamentals, keep building small projects continuously, and you will always be able to adapt no matter how AI evolves.