Algorithms are a basic element in the world of Machine Learning. The objective of a Machine Learning algorithm is to define the steps necessary to learn from the data and solve a problem autonomously.
Deep Learning is a set of algorithms that seek to reproduce the same results as the human brain.
The algorithms follow a logic of layered processes that simulate the basic functioning of the brain through neurons.
Algorithms seek this imitation by learning to recognize repetition patterns, specific words, frequent behaviors so that they can automatically respond to input data, just as our brain responds to any input.
Neural networks are a class of machine learning algorithms used to model complex patterns in data sets using multiple hidden layers and non-linear activation functions.
The practical applications of NLP are many and have experienced spectacular growth thanks to the new techniques of Machine Learning.
Regression problems seek to model the behavior of a quantitative variable (target variable) based on other predictor variables (components or features) that can be quantitative or qualitative with the usual objective of making predictions or estimates.
Today it is one of the most widespread tools in the world of Machine Learning, particularly for the construction of networks of neurons.