A “Machine Learning” algorithm is defined as an algorithm able to change its structure and functioning according to the data submitted. In other words, a machine learning algorithm is capable to learn from data and be refined after implementation. Nowadays, many structural biology (e.g. psi-pred, jpred), bioinformatics (HMM-based software) and systems biology (network analysis and db comparison) algorithms rely on machine learning methods, and an insight of the basic principles underlying them is very useful to all those that are working on software development. Unfortunately, an extensive study of such an advanced topic may be pretty tough for someone with a biological background.
Surfing on YouTube, I have been really pleased to find the mathematicalmonk’s channel. Actually, I have no clue on who this guy is, but I am pretty sure that he did a good work with his tutorials. Along with other advanced mathematical topics, Machine Learning is explained in a 160 videos playlist, where the author explains the base concept of Machine Learning with simplicity and great clearness. The course goes through all the major topics needed for an introduction to machine learning methods, and it’s a perfect point to start your exploration in the machine learning.
Above this post, you can play the introductory video, to get an idea about the topic and the kind of lessons proposed. The whole playlist can be found following this link.