Using ML techniques

In the previous section, we explored the various types of ML paradigms in detail. So, we have understood the basic principles that underlie the different approaches. At this point, it is necessary to understand what the elements that allow us to discriminate between the different approaches are; in other words, in this section, we will understand how to adequately choose the learning approach necessary to obtain our results.

Selecting the ML paradigm

Selecting the appropriate ML algorithm can feel overwhelming given the numerous options available, including both supervised and unsupervised approaches, each employing different learning strategies.

There is no universally superior method, nor one that fits all situations. In large part, the search for the right algorithm involves trial and error; even seasoned data scientists cannot determine whether an algorithm will work without testing it. Nonetheless, the algorithm choice is also influenced by factors...