How to Learn AI from Scratch?

To learn Artificial Intelligence from scratch, FreeCodingTour offers three courses that should be taken in the following order:

1. Programming
2. Data Science
3. Artificial Intelligence

The AI Learning Methodology at FreeCodingTour is based on the following principles:

• Step 1: Learn programming as a process of Input -> Process -> Output where the Process can be seen as a black box. As masterfully described by Richard Feynman.
• Step 2: Learn to test if our programs work well through the methodology of Test Cases. This is very important and useful as it introduces us to a practice widely used in the software industry and also opens the door if we later want to delve into the field of algorithms since all interviews and competitions are based on this method.
• Step 3: Learn Data Science with the Machine Learning Methodology. That is, admitting that nature is very complex (as Vladimir Vapnik says), setting aside (at least at the beginning) the frequentist and Bayesian methodologies, and accepting that we definitely need machines that can perform millions of computations per second. Once accepted, we can view Machine Learning as a method for a machine to automatically learn a process that we cannot describe but can specify what the inputs and outputs of that process should be. In other words, understand Machine Learning as Automatic Programming guided by Test Cases.
• Step 4: Learn Deep Learning as a way for a machine to encode (convert into numbers) images, audio, and text to use them in combination with Machine Learning methods to make predictions and interact with humans. This learning method is useful for understanding concepts like Embeddings, RAG, Latent Space, Encoding, and others that are gaining more relevance day by day. In summary: Understand Deep Learning as Input in Human Language -> Process -> Output in Desired Format where Human Language can be Text, Images, or Audio.