Machine Learning

Front 1

Which type of machine learning exists

Back 1

Unsupervised learning, supervised learning and reinforcemeny learning

Front 2

The main goal in supervised learning

Back 2

is to learn a model from labeled training data that allows us to make predictions about unseen or future data.

Front 3

the term supervised refers to

Back 3

a set of samples where the desired output signals (labels) are already known.

Front 4

Which categories of supervised learning there are

Back 4

Classification and regression

Front 5

What is the goal of the classification

Back 5

To predict the categorical class labels of new instances based on past observations. Those class labels are discrete, unordered values that can be understood as the group memberships of the instances.

Front 6

How works the regression analisys

Back 6

we are given a number of predictor (explanatory) variables and a continuous response variable (outcome), and we try to find a relationship between those variables that allows us to predict an outcome.

Front 7

What is the goal of the reinforcement learning

Back 7

the goal is to develop a system (agent) that improves its performance based on interactions with the environment.

Front 8

How unsupervised learning works

Back 8

Using unsupervised learning techniques, we are able to explore the structure of our data to extract meaningful information without the guidance of a known outcome variable or reward function

Front 9

What is clustering

Back 9

Clustering is an exploratory data analysis technique that allows us to organize a pile of information into meaningful subgroups (clusters) without having any prior knowledge of their group memberships.

Front 10

What is a cluster

Back 10

Is a sub-group, each cluster that may arise during the analysis defines a group of objects that share a certain degree of similarity but are more dissimilar to objects in other clusters

Front 11

What is some sub-categories of unsupervised learning

Back 11

Clustering and Dimensionality reduction

Front 12

How Dimensionality reduction works

Back 12

to remove noise from data, which can also degrade the predictive performance of certain algorithms, and compress the data onto a smaller dimensional subspace while retaining most of the relevant information.