There are 4 repositories under ineuron topic.
This repository consists content, assignments, assignments solution and study material provided by ineoron ML masters course
Data Science interview question by iNeuron
This is a part of the ineuron intership. This project will deal with the analysis of Amazon Sales Data
Developed project with intention of understanding of web scrapping, api integration, talking to 3rd party libraries
iNeuron Webscraper Python Project using beautifulsoup and flask
π This repository is related to the assignments based upon π§ Artificial Intelligence, π€ Machine Learning and π» Data Science given by PWSKILLS for the course "DATA SCIENCE MASTERS - IMPACT BATCH 1" π€π¨βπ
iNeuron Assignments
Extract the text data from the prescriptions and convert them into speech.
This repository consists of all the study material for the course of iNeuron. The course is "Deep learning with Advance Computer Vision and NLP Masters".
π This repository is related to the projects related to π€π§ π» Artificial Intelligence, Machine Learning and Data Science given by PWSKILLS for the course "DATA SCIENCE MASTERS - IMPACT BATCH 1" π
Color changer using JavaScript.
Flask based YouTube scraper application. Downloads latest videos from the video link provided and stores it in AWS S3 bucket and save meta data to MySQL & mongoDB.
This is Rode website clone project using TailwindCss.
Javascript Bootcamp Projects and Assignments - Hitesh Choudhury Fullstack javascript bootcamp
Developer Homepage Using HTML & CSS
This is a simple random jokes appthat displays a random joke everytime user clicks on fetch a new joke button.
Assignment for Full Stack Data Analytics Tech Neuron
This is a simple rock-paper-scrissor game using React js and CSS
This my repository for my ineuron fsda 2.0 course. Here i have assignment and certificate from ineuron and udemy
Built this Responsive Homepage without using flexbox or grid only by using HTML and CSS.
designed and developed a completely responsive Landing page
An Online Compiler with Multiple Language Support.
Classify mushrooms as π edible or poisonous π« using machine learning π€. This project includes data preprocessing, training various models (Logistic Regression, Decision Tree, SVM, etc.) ποΈ, and evaluating performance with metrics and visualizations π. Perfect for learning classification and model comparison.