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Sentiment Analysis Made Easy: Building, Deploying, and Tracking with Logistic Regression, Flask, and MLflow

In today's digital age, the explosion of text data on social media, product reviews, and online discussions has made sentiment analysis an essential tool for businesses and researchers alike. Sentiment analysis involves the use of Natural Language Processing (NLP) techniques to determine the underlying sentiment expressed in a

Demystifying Word2Vec: Understanding Word Embeddings for Natural Language Processing

Word embeddings are a popular technique in natural language processing (NLP) that represents words or phrases as dense vectors in a continuous vector space. They capture the semantic and syntactic relationships between words, allowing machines to understand and process human language more effectively. Traditionally, NLP models represented words as one-hot

Revolutionizing Education:The Impact of ChatGPT in Transforming Learning Experiences

Artificial intelligence (AI) has witnessed remarkable progress in recent years, transforming various industries and significantly impacting our daily lives. One particular area where AI is making a significant difference is in education. With the emergence of ChatGPT, an advanced language model developed by OpenAI, the possibilities for enhancing the learning

End-to-End Engine Horsepower Prediction with Deep Learning Regression Part-IV

End-to-End Engine Horsepower Prediction with Deep Learning Regression Part-IV

Introduction In the previous articles we explored data pre-processing ,model building and model tracking, in this blog we will explore Model Deployment using Flask. Flask is a lightweight and popular open-source web application framework written in Python. It is designed to be minimalistic and simple, allowing developers to build web

Regression Analysis

Regression Analysis

INTRODUCTION: Regression analysis is a statistical tool used to examine the connections between variables. Its main purpose is to investigate the cause-and-effect relationship between two variables, such as the impact of a price increase on demand or changes in the money supply on the inflation rate. To study these issues,

End-to-End Engine Horsepower Prediction with Deep Learning Regression Part-II

End-to-End Engine Horsepower Prediction with Deep Learning Regression Part-II

Introduction: After completion of data preprocessing, we need to do the following steps to build the regression model. 1. Divide the Data into the Training and Test Sets. 2. Creating and Training a Neural Network with TensorFlow 2.0 3. Evaluating Neural Networks Performance * Divide the Data into the Training

End-to-End Engine Horsepower Prediction with Deep Learning Regression Part-I

End-to-End Engine Horsepower Prediction with Deep Learning Regression Part-I

Introduction This is a series of 4 articles where we will explore how to build a horse power prediction model using Deep Learning Regression from scratch. We will cover data pre-processing, model building, model tracking and model deployment in this series of blogs. In this blog we will cover data