This article will showcase a skin cancer detection web app developed using Flask, Python, and a MySQL database. This web app is a beacon of hope for patients and a precision tool for healthcare professionals.
In the following sections, we will guide you through the process of machine translation by custom training a M2M 100 model for Sanskrit to English, using Hugging Face Trainer.
Online tools are replacing the old ways of performing recurring tasks in content writing. With the help of AI based paraphrasing tools, marketers can enhance their workflow
In this article, we will explain in detail what is named entity recognition, the types of named entities, how named entity recognition works, IOB labeling in NER, types of NER techniques, applications of NER, and what are the open-source industrial tools available for named entity recognition.
In this article, we will discuss the Hidden Markov model in detail which is one of the probabilistic (stochastic) POS tagging methods. Further, we will also discuss Markovian assumptions on which it is based, its applications, advantages, and limitations along with its complete implementation in Python.
In this article, we will explore the process of implementing both lexicon-based and rule based POS tagging on the Treebank corpus of NLTK. We will begin by exploring the corpus to gain a better understanding of its structure and the data it contains.
In this article, we will discuss what part of speech tagging is, how it works, different types of POS tagging techniques, and how to perform POS tagging in Python.
In this article, we have discussed 8 real-time machine learning for healthcare analytics projects.
In this article, we have demonstrated step-by-step medical insurance premium prediction using machine learning in Python using the open-source dataset available on Kaggle.
In this article, we have demonstrated how we can perform breast cancer prediction using a machine learning algorithm support vector machine for predicting if the breast cancer diagnosis is benign or malignant.