Machine Learning (Supervised Learning) — In my previous blog — Shades of Machine Learning — we discussed what are the two main types of machine learning algorithms. Just to brush up, we have Supervised Learning (where the target is known/ the data is labeled and the model works under this supervision) and Unsupervised Learning (where…

Supervised vs Unsupervised Learning — In the last few blogs, we discussed various methods of cleaning and transforming data at different scales before applying any ML algorithm to it (you can find the links at the bottom of this article). This preprocessing of the data is required because dirty/messy data would not make any sense…

Performing feature engineering on the dataset — The next step after exploring the patterns in data is feature engineering. Any operation performed on the features/columns which could help us in making a prediction from the data could be termed as Feature Engineering. This would include the following at high-level: adding new features eliminating some of the features…

The next step after importing the data — In my previous blog, I explained how to clean the data, perform EDA (exploratory data analysis) and what is a basic feature engineering in brief. So let’s say, you did a “read_csv” and imported the data. Now, what next? The next important thing to talk about is how can we…

Preprocessing, EDA & Feature Engineering — Believe it or not, this part covers about 60–70% of the entire ML work! I will not talk about the codes here, because I believe that if you know the logic, finding a code isn’t super difficult, only getting there would vary from person to person. …

A practical starter kit to data science for non-programmers — So I come across a lot of people from different backgrounds (Electronics, Mechanical, Undergraduates (1st/2nd year), Sales, Finance, etc. who want to explore the Data Science field, and some of the questions they have are these, which even I was wondering about when I started. — Can I enter in…

Understanding how to make the business and the data science teams work together — Data Science is used to provide a “data-driven solution”. But before we can solve a problem, we need a problem! And problems come from the pain points of the different business teams. …

10 steps to help you become a data scientist in the real world! — I often hear people without a data background having doubts about Machine learning. And honestly, I had similar doubts a few years back! So, this is my attempt to make you all understand Machine Learning one step at a time. I am sure you all know that Machine learning is…