An Introduction to Machine Learning Applications: Glossary

Machine Learning is the science of getting computers to learn, without being explicitly programmed.

Machine Learning main objective is to find new useful representation that help understand hidden patterns in data.

There are several ways to classify machine learning algorithms based on the problem they are trying to solve, how is data processed and how much human supervision is required.

Python is a very useful programming language to develop machine learning applications

Scikit Learn together with Numpy, Pandas and Matplotlib form a popular machine learning environment.

Linear regression is one of the most simple and useful machine learning algorithms in which the model makes a prediction by simply computing a weighted sum of the input features, plus a constant called the intercept term.

k-Nearest Neighbors is a simple classification algorithm in which predictions a new data point to the closest data points in the training dataset.

It is not necessary to use all the features in our training dataset. We can use different combinations to try to achive better results.

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Moving to an HPC system can be challenging but there are sources of help that can make the transition easier.

As you progress in the development of machine and deep learning applications new challenges are likely to appear. Requesting help in those case can save you a lot of time.