Top 6 Open Source Pretrained Models for Text Classification you should use
Introduction We are standing at the intersection of language and machines. I’m fascinated by this topic. Can a machine write as well as Shakespeare? What if a machine could improve my own writing skills? Could a robot interpret a sarcastic remark? I’m sure you’ve asked these questions before. Natural Language Processing (NLP) also aims to answer these questions, and I must say, there has been grou
A Beginner's Guide to Random Forest Hyperparameter Tuning
Introduction to Random Forest What’s the first image that comes to your mind when you think about Random Forest? It conjures up images of trees and a mystical and magical land. And that’s what the Random Forest algorithm does! It is an ensemble algorithm that combines multiple decision trees and navigates complex problems to give us the final result. I’ve lost count of the number of times I’ve rel
Enhance Model Performance through Cross Validation: A Guide in Python and R
Introduction One of the most interesting and challenging things about data science hackathons is getting a high score on both public and private leaderboards. I have closely monitored the series of data science hackathons and found an interesting trend. cross validation using python and R trend is based on participant rankings on the public and private leaderboards. One thing that stood out was th
Beginner's guide to Web Scraping in Python using BeautifulSoup
Overview Learn web scraping in Python using the BeautifulSoup library Web Scraping is a useful technique to convert unstructured data on the web to structured data BeautifulSoup is an efficient library available in Python to perform web scraping other than urllib A basic knowledge of HTML and HTML tags is necessary to do web scraping in Python Introduction The need and importance of extracting dat
A comprehensive beginners guide to Linear Algebra for Data Scientists
Introduction One of the most common questions we get on Analytics Vidhya is, How much maths do I need to learn to be a data scientist? Even though the question sounds simple, there is no simple answer to the the question. Usually, we say that you need to know basic descriptive and inferential statistics to start. That is good to start. But, once you have covered the basic concepts in machine learn
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