Alessandro N. Vargas
This page presents some ideas about what is Machine Learning and its applications. The first point is that you must have data and want to extract from them some meaningful information. For instance, suppose that you have a database containing information about tree leaves. An entry in this database represents how green the leaf is. For instance, a leaf with a value of +1 means a full, dark green color, and -1 means a gray, possibly unhealthy leaf. Our database can be made of three leaves, for instance [('L1',0.45),('L2',-0.56),('L3',0.127)]. Suppose that our database has thousands of entries. Is it possible to extract some meaningful information from this database? Is it possible to know whether a leaf is either healthy or unhealthy?
What should I do with the data that I collected in a laboratory? Can I apply a Machine Learning method to them?
Can a machine learning method help us in the task of classifying whether a leaf is healthy or unhealthy? In many cases, the answer is yes. Note that the database could be enriched to become [('L1',0.45,'healthy'),('L2',-0.56,'unhealthy'),('L3',0.127,'healthy')]. In this case, each entry is labelled by an expert (a human).
The task we wish to rely on the machine learning is as follows. Suppose that we have a novel data with value of ('L4',0.277). Is the machine learning wise enough to help us conclude that 'L4' is a healthy leaf? The answer to this question seems 'yes,' but this example, yet a simple illustration, is very limited to illustrate the full potential of machine learning methods.
Python is the main programming language used by researchers to perform machine learning. For instance, a useful library is scikit-learn, a Python library that has many built-in functions to help researchers develop their own machine learning algorithms.
The diagram in Figure 1 illustrates the potential of machine learning. To navigate through it, machine learning researchers should be proficient not only in Python (Computer science), but also in Statistics (i.e., Stochastic Process) and Engineering methodology (i.e., understand what is the problem). It is necessary to become a high-skilled scientist to know how to deploy machine learning in a meaningful way. At Labcontrol you can study machine learning with us. Send us a message if you are interested in this topic.