Machine Learning is a set of powerful algorithms that analyze data, learn from it, and apply what they've learned to find interesting patterns. A Neural Network, on the other hand, is a collection of methods used in Machine Learning to model data using graphs of neurons. A Neural Network arranges algorithms in such a way that it can make reliable decisions on its own, whereas a ML Model makes decisions based on what it has learnt from the data. As a result, while Machine Learning models may learn from data, they may need some human interaction in the early stages. There are two types of machine learning models: unsupervised and supervised learning models. Feed-forward, convolutional, recurrent and modular Neural Networks are the four types of Neural Networks.