In my last notebook we looked at a classification problem, and we defined many classification metrics. In this notebook, we will go through some regression metrics. Recall that in regression, the response value is continuous (and not categorical), as such different kind of prediction assessment will come into play.
Now, say, you have built a machine learning model; the question you ask is: ‘how well does this thing works anyways?’. To answer this question, we will need to define the performance metrics. As you might have imagined, the metrics will depend on the kind of machine learning problem in view.
These are the methods involved in sampling during machine learning. In my last notebook-blog, I hinted the idea of an analogy between a 12-year-old girl studying for an exam, and our machine trying to learn…
In our ML blog-syllabus, mathematical foundations of ML should be the next stop, however I have decided to postpone this till later in the blog in order to write something more comprehensive. The reader should note that getting the maths ‘out of the way’ is very essential to deeply understand a lot of the ML algorithm out Read More
Now that I am done with the computational foundations of ML in python, I can not in good conscience proceed to other topics in maths, without touching on some basic statistics. Here is a brief note on statistics.
In this notebook, I demonstrated a few visualization techniques using my reading data from Goodreads.
Pandas is an open source python library that is used for data handling and manipulation. It was developed to work with the NumPy library.
NumPy is an inescapable package for scientific computing in python. You can think of it as a foundation for numerous python packages…
A web-based application in the form of a notebook that can be used for storing (and sharing) codes, notes, mathematical equations, and visualizations.
About 2 months ago, I announced this new blog section. I will be starting with machine learning over the next several months, and here is a peep at the blog outline.