Exploring machine learning paradigms and the core idea behind it which is generalization. Also, neural networks and how deep learning is different from traditional machine learning as well as how to use activations functions and optimizers. At the end, I added a small overview of model evaluation followed by some of the common types of neural networks architectures nowadays like CNNs, RNNs, and Transformers.