ML Mastery

Understanding Morphology in NLP: The Key to Word-Level Language Intelligence Understanding Morphology in NLP A Deep Dive into Telugu vs English Morphological Patterns TL;DR This post introduces Morphology in NLP using real language examples (Telugu vs English). Learn how word forms change and how machines understand them. Welcome to the fascinating world of morphology in Natural Language Processing! Today, we're diving deep into how different languages structure their words, and why this matters immensely for building intelligent language systems. What is Morphology? Morphology is the study of word structure - how words are formed and how they change their forms to express different meanings. In NLP, understanding morphology is crucial because it helps machines recognize relationships between different word forms and extract meaningful information from text. Think of morpheme...

Bag of Words Explained: Mastering the Fundamentals of NLP Welcome to Part 2 of the ' NLP Engineering ' series where I’ll guide you through essential NLP concepts—from theory to practice—with clear explanations and hands-on code examples. Perfect for beginners and seasoned engineers alike!" Natural Language Processing (NLP) requires converting text into numerical formats that machines can understand. One of the fundamental techniques for this transformation is the Bag of Words (BoW) model. In this blog post, I'll explain how BoW works, why it's useful, and demonstrate it with Python code. What is Bag of Words? Bag of Words is a method of representing text data as numerical features. At its core, BoW involves creating a meaningful vocabulary from a corpus of text, which can then be used for various NLP tasks like sentiment analysis, text classification, or document clustering. The name "Bag of...

  Have you ever played a game with your friends and noticed that your performance can vary depending on the level or challenge? Machine learning models can also have a similar issue with performance variability, known as bias and variance. Bias: Bias is the overall difference between what the model predicts and what the correct answer should be, averaged across all examples. A model with high bias consistently makes the same types of errors, regardless of the specific input features or examples. To illustrate this concept, let's consider a regression model that predicts the price of a house based on its square footage, number of bedrooms, and location. When evaluating this model on a test dataset, we calculate the mean squared error (MSE) between the predicted prices and the actual prices across all examples in the test dataset. If the MSE is high, this suggests that the model is biased and consistently predicts prices that are too high or too low compared to the true values. To ca...

 What is data analysis ? Data analytics is the process of collecting, processing, and analyzing raw data to extract meaningful insights and patterns. It involves using various tools and techniques to transform data into actionable information that can be used to make data-driven decisions. By analyzing data, businesses can identify trends, patterns, and relationships that can help them optimize their operations, improve their products or services, and better understand their customers. Data analytics is a critical component of modern business strategy, as it enables organizations to make informed decisions based on empirical evidence rather than guesswork or intuition. DESCRIPTIVE ANALYSIS: Descriptive analysis is a type of data analysis that examines raw data to identify patterns and summarize what has happened in the past. For instance, descriptive analysis can help businesses to understand their sales figures, customer behavior, and other metrics to make data-driven decisions th...

  what is Linear Regression? Linear regression is a supervised machine learning algorithm used for predicting a continuous outcome variable (also known as a dependent variable) based on one or more predictor variables (also known as independent variables or features). The goal of linear regression is to find the line of best fit that minimizes the sum of the squared differences between the predicted values and the actual values. Linear regression assumes that there is a linear relationship between the predictor variables and the outcome variable. In other words, it assumes that changes in the predictor variables are directly proportional to changes in the outcome variable. There are two main types of linear regression: simple linear regression and multiple linear regression. Simple linear regression is used when there is only one predictor variable. The equation for a simple linear regression model is: Y = b0 + b1*X Where Y is the outcome variable, X is the predictor variable, b0 i...