Applied Machine Learning without coding using Orange 3

This is a course about data science and machine learning without using any programming!

Orange 3 is used as the platform for the course. It is public domain and very powerful tool with a lot of flexibility. It is a visual environment making it very easy to demonstrate your work to others. You do not need to have any background in machine learning in order to follow this course. I have tried to keep the concepts very simple and provide enough theory to get you started. I provide resources for further reading if you want to advance your knowledge in machine learning and data science.

Although Orange 3 is a simple tool, it can accomplish advanced tasks. I did not cover all of Orange 3’s capabilities, however at the end of the course you will be able to experiment with all of its features.

I will cover a variety of topics:

Data pre–processing

Imputation

Outlier detection

Variable transformation

Handling of imbalanced data sets

Modelling for both classification and regression problems

Linear regression

Support vector machines

Neural Networks

kNN

k–Means

Random forest

Time series forecasting. This is normally a subject of advanced courses but I thought to include it and give you an idea of how it is performed, as so many real life problems are time series