Amit Yadav

Imagine trying to teach a machine to understand the beauty of different colors. Red, blue, green—they mean something to you, but to a machine, they’re just arbitrary labels. This is where the magic of feature encoding comes into play, and trust me, it’s more powerful than it seems. If you’re working with machine learning models, […]

1. Introduction Brief Overview of One Hot Encoding Think about this: In a world driven by data, where we’re constantly crunching numbers, how do you handle text-based information, like categories, in a machine learning model? That’s where One Hot Encoding comes to the rescue. In simple terms, it’s a technique that transforms categorical data into

1. Introduction Definition of Data Transformation Think of data transformation as the process of taking raw data and reshaping it to be usable for analysis. It’s not just about changing numbers but altering how data is structured, formatted, and interpreted. You’re turning raw ingredients into a gourmet meal—an essential step in any data pipeline. Whether

1. Introduction to Data Transformation in R Before we dive into the practical aspects, let’s start with something basic but essential: What exactly is data transformation, and why should you care? Here’s the deal: Data transformation is the process of changing the format, structure, or values of your data so it’s ready for analysis. It

1. Introduction to Bag of Words (BoW) “You can’t manage what you can’t measure.” This famous quote applies to many fields, including Natural Language Processing (NLP). When you’re dealing with text, one of the most crucial tasks is to measure words in a meaningful way, and that’s where the Bag of Words (BoW) model comes

Think of data as the building blocks of every insight, model, and decision we make. But, here’s the thing: not all data is created equal. Sometimes, it’s dense and abundant, like a tightly packed suitcase full of essential items. Other times, it’s sparse, with gaps and missing pieces—more like a suitcase where half the items

What are RBMs? Let’s start with a simple idea: imagine a neural network that learns to represent the complex patterns in your data—without needing explicit supervision. That’s essentially what a Restricted Boltzmann Machine (RBM) does. Now, here’s the kicker: RBMs are stochastic neural networks, which means they rely on probabilistic principles to model data. In

What is Deep Learning? Imagine teaching a child to recognize faces. You don’t just tell them “this is a nose” or “this is an eye” every time—they gradually learn through examples. This is the essence of deep learning (DL). It’s a type of artificial intelligence (AI) that mimics the human brain in recognizing patterns. The

Have you ever looked at a giant spreadsheet filled with mostly empty cells and thought, “There’s got to be a more efficient way to store this”? Well, that’s where sparse datasets come into play. A sparse dataset is essentially a dataset where most of the values are either zero or empty. Think of it like

Have you ever wondered how to uncover the hidden relationships between two sets of variables? Maybe you’re interested in how psychological traits relate to academic performance, or how economic indicators influence environmental factors. Canonical Correlation Analysis (CCA) is the tool you need to explore these complex interrelationships. Overview of Canonical Correlation Analysis (CCA) Definition and