Machine learning and AI
The use of Python for data science and analytics is growing in popularity and one reason for this is the excellent supporting libraries (NumPy, SciPy, pandas, Statsmodels (ref), Scikit-Learn, and Matplotlib, to name the most common ones). One obstacle to adoption can be lack of documentation: e.g. Statsmodels documentation is sparse and assumes a fair level of statistical knowledge to make use of it. This article shows how one feature of Statsmodels, namely Generalized Linear Models (GLM), can be used to build useful models for understanding count data.
With Google just about to launch a point of presence in Sydney for its Google Cloud Platform (GCP), we thought it timely to explore how to use Google’s Cloud Natural Language API as a part of the Google Cloud Platform Machine Learning suite. There are many articles out there which outline many different ways to do text analysis, but what Google offers is a kind of black box where you simply call an API and get a predicted value. What this means for the average developer is that we no longer need to be statisticians, and we don’t have to accumulate the vast amount of data required for this kind of analysis. Sure, we forego the ability to fine tune the algorithm, but there’s definitely a market to get productive right away and build important applications, instead of building everything from the ground up.
I find it almost inconceivable to think that it was less than 200 years ago, that Ada Lovelace wrote the first instructions that would lead the way to the first computer program. It’s mind boggling to think how far humanity has come and my mind wonders in hungry awe at where we are heading. Part of me wishes for eternal life so that I can stay and watch, but unless I take part in some Transhumanistic experiment, I fear that I’m just going to have to miss out!