Machine Learning

I'm not an expert on ML by far, but I know enough basics to hold an intelligent conversation on the subject when I have to.

How It All Started

I admit that I started looking into Machine Learning only because it sounded cool. I wanted to understand what the hype was about. I also probably didn't want to be left out of all the ML lingo and talk my friends kept having every now and then.

Having picked up on the basics of Machine Learning in the past few months, I've realised how ML can be used to solve problems in various domains. And that in itself is reason enough to fully commit to it. I continue to pursue ML only as a technical achievement for now. But I'm excited at the prospect of learning something new in the process.

How It's Going Right Now

I'm focusing on the fundamentals that will help me in the long run. With an eye out for what is most widely used in the industry right now, I have a good understanding of the following topics.


Library that helps in working with lists, arrays (multi dimensional) in Python.


Data analysis library that extracts tabular data in dataframes to allow transformations and aggregations.

Scikit Learn

To create models, data mining and data analysis by using prebuilt algorithms for classification, regression, etc.


Python charting tool that allows us to visualise data.

Jupyter Notebooks

Useful as it can step through each data analysis stage, and gives visualizations. Can be shared easily with others.


Helps to work with many data science libraries which are written in Python.