The Machine Learning Vanguard

Most commonly used metrics for Linear Regression Model Imagine a Class with Test Score, s uppose you are in a class and your teacher gives you and your friends a test. Following the test, your teacher wants to know how good the class actually did. To do so, she tries to predict how well everybody would perform based on the study time. Then, following the test, the teacher compares the predicted scores to the actual scores that you all attained. The teacher wants to know how far off the predictions were from the real scores. This is where MSE, RMSE, and MAE come in, they are ways of measuring the errors or mistakes between predicted scores and real scores. 1. MSE (Mean Squared Error) Suppose your teacher predicted that you would score 90 on the test, but you actually scored 80. The error here is: 90 - 80 = 10 Now, we square it instead of using the error itself. That means, multiply the error by itself. So: 10 * 10 = 100 Why square it? The reason is because squaring makes s...

Well, in this data-driven world, terms like Data Science and Data Analysis come on the radar again, of course, talking about potential careers or high-end education in the fields of technology, business, and academia. But if you're just starting to learn these disciplines, it might get a bit confusing. While both data scientists and data analysts work with data, the roles, tasks, and skill sets are quite different. Let's break it down in a simple easy way. By the end of this article, you shall get a clearer idea of what each role involves and how they affect businesses, industries, and even day-to-day life in India. What is Data Science? Imagine you are predicting the future. You gaze into some historical trends, look for patterns, make educated guesses via some pretty complex algorithms of what might happen next. That's basically what a data scientist does, but with data. Combining knowledge of statistics, programming, and machine learning, he or she can discover mea...

In recent years, Python has become the go-to language for data science and data analysis. It’s the first language that comes to mind for most aspiring data scientists, and seasoned analysts often favor it too. So, why has Python, a language initially designed for general-purpose programming, become so popular in data science? Let’s dive into the reasons behind this trend and understand why Python shines brighter than other programming languages in the world of data. 1. Ease of Learning and Readability One of the most appealing aspects of Python is its readability and simplicity. Python’s syntax is close to English, making it relatively easy to learn for beginners. Unlike languages like C++ or Java, Python doesn’t overwhelm newcomers with complex structures or verbose code. This simplicity is a significant plus in data science, where people from diverse fields like statistics, biology, business, and engineering are venturing into programming. For someone transitioning into data science,...