In today’s world people are too busy to even think and react to a random thought. Social media and virtual games have hacked many of our brains (Not all). Of course, we do react to some of the thoughts that are generated in brains but they are mostly again controlled…

In today’s digital era, data has become the lifeblood of organizations across various industries. The insights derived from data drive decision-making, innovation, and competitiveness. However, as the volume and complexity of data continue to surge, ensuring its accuracy, security, and ethical use has become paramount. This is where data governance…

About Data The first thing every data science aspirant thinks “We will be building models and automating the process using the data”, but the real question is “How will we get the data ?”. Before answering this question let us understand what is data. Data is information, “Is everything available…

Every machine learning engineer is interested in working with images, as they give a wider perspective of understanding new concepts in machine automation and indeed a very interesting field to explore. Unlike numeric and categorical data, image data is higher dimensional (2d and 3d), so we cannot work with them…

We start building a neural network because we have a problem that needs to be solved, but we have many machine learning algorithms which can be used for the same purpose, so what is the need in building a neural network? One of the main reasons we use NN’s is…

In part 1 we have understood how the Linear regression algorithm works and how it can predict the target values by understanding the current and previous data. …

Machine Learning is a very vast field, often when we want to start learning ML algorithms, the first ML algorithm that we start with is Linear Regression, many online data science courses and even graduate courses start with Linear Regression. The main reason behind this might be LR is one…

While building any machine learning models, data scientists or ML engineers use certain libraries depending upon the framework they are working on, these libraries make the work easier in building an ML model. But, to understand what is happening behind each algorithm we need to have sufficient statistical knowledge because…

In part 1, we have introduced the basic maths that is required to start a career in data science, but honestly, we all have studied and worked with those concepts in our 9th or 10th grade. …