Dimensionality Reduction (Heterogenous Data)
Look into feature selection algorithms, there are a ton of articles and public libraries that have implementations of these.  Support Vector Machine's (SVM) is one that is commonly used. Take a look at sklearn/tensorflow/etc. docs to see implementation details and pick which one is best for your problem.
I have few notions of machine learning and data science and I need to make a dimensional reduction of a data set, corresponding to the TV consumption of users. I have approximately 20 columns (features) and hundreds of thousands of samples.
The problem is that the features are of different kinds. For example, the region, the date, the type of device, the duration of consumption, etc.
What algorithms could I implement in this particular case to reduce the number of features?