Machine Learning is a term used for a number of different techniques where hyper-dimensional datasets can be used without prior conceptualization and definition of the relationships between variables. The “machine” will “learn” from the data. As a result, a ML-model can be very complex and thus treated as a “black box”, i.e. its purpose it not to show how something works but just to be great at classifying, predicting etc.

The programming language Python is perhaps the most used framework in advanced analytics and machine learning. A good way to get started with ML in Python is to use Google Colab at https://colab.research.google.com/. It means you don’t have to install Python and libraries. It also means you can use their GPU and TPU resources to train and run models much faster than on a standard CPU.

R can also be used for machine learning, see for example https://www.datacamp.com/community/tutorials/machine-learning-in-r

To familiarize yourself with ML, you can also use Weka (https://www.cs.waikato.ac.nz/ml/weka/) which is a tool that makes ML algorithms available without coding.

Choose the platform you would like to work on. If you have Python or R installed, you might want to use that. If you are not into coding, try Weka.

Go through a basic tutorial for machine learning using your platform!

Also, try doing the Classification challenges with your new-found ML-skills!