Name: VIPIN K
Type: User
Bio: Having Hands-on experience in Computer Vision, Machine Learning, Deep Learning and Natural Language Processing.
Twitter: vipinkvpk
Location: Bengaluru, Karnataka, India
Blog: vipinkvpk.github.io
VIPIN K's Projects
100 Days of ML Coding
500 AI Machine learning Deep learning Computer vision NLP Projects with code
Roadmap to becoming an Artificial Intelligence Expert in 2021
In this project, we will predict customer sentiment using natural language processing techniques. In this project, we will build a machine learning model to analyze thousands of amazon echo reviews to predict customers sentiment. Artificial Intelligence and Machine Learning (AI/ML)-based sentiment analysis is crucial for companies to automatically predict whether their customers are happy or not. This project is practical and directly applicable to any company with that has online presence. The algorithm could be used automatically detect customers sentiment.
final peer graded assignment in DATA VISUALIZATION FOR PYTHON by IBM coursera course
Text Dataset, Numerical Dataset
This repo has all the code files which were created as part of the assignments to complete the Applied AI Course. The credit for code source structure and data goes to the Applied AI team.
This repo contains the Applied Ai Course Assignments and Notes.
š Awesome lists about all kinds of interesting topics
:memo: An awesome Data Science repository to learn and apply for real world problems.
A curated list of awesome Machine Learning frameworks, libraries and software.
A curated list of references for MLOps
Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas
A topic-centric list of HQ open datasets.
A curated list of awesome Python frameworks, libraries, software and resources
Source Code for 'Building Computer Vision Applications Using Artificial Neural Networks' by Shamshad Ansari
Design, test and validate complex systems through simulation in Python
In this guided project, we will build, train, and test a deep neural network model to classify low-resolution images containing airplanes, cars, birds, cats, ships, and trucks in Keras and Tensorflow 2.0. We will use Cifar-10 which is a benchmark dataset that stands for the Canadian Institute For Advanced Research (CIFAR) and contains 60,000 32x32 color images. This project is practical and directly applicable to many industries.