Machine Learning / Python
"Machine Learning" is where Big Data meets Artificial Intelligence. Whereas
Data Analysts have, in recent years, demonstrated some spectacular successes in the innovative interpretation
of large data volumes, subtle correlations/weak signals often escape the human eye. When quantities
of interest are correlated with a large number of data streams, some of them having contrary effects, it becomes necessary
to apply computing techniques to identify and evaluate these correlations. "Supervised" Machine Learning systems
rely on a large quantity of "examples" to "learn" to recognize such correlations. Modern programming languages such as
have made it possible to create such models with relative ease. The analysis being written in Python, and the
speed-critical code libraries in C/C++, offer the data scientist an optimal combination between speed of coding and
speed of execution.
Using an example many might recognize from school days and/or work, the figure below shows the age and
height (the x-markings/data points) for a set of trees. Trees generally grow with age, making it possible to draw an upwards sloping
straight line closely matching the entire set of data points. This method is known as a linear regression.
As a result, it is now possible to make a good prognosis for a tree's height, given its age. When a machine
determines the height, by first calculating the line of best fit through the input data, it can be seen as a
very simple example of machine learning. By adding further parameters, such as e.g. the fertility of the soil to
more accurately determine the height, we carry out a multivariate
regression. Many machine
learning models use exactly this technique.