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Advanced NLP Workshop: word-sense disambiguation with RoBERTa and text summarization with BART (Machine Learning Milan)
Visualizing Gradient Descent with Momentum in Python
SPPU BE COMP LP3 Codes - Machine Learning (ML) and Information and Cyber Security (ICS)
Being the most common and rapidly growing disease, Diabetes affecting a huge number of people from all span of ages each year that reduces the lifespan. Having a high affecting rate, it increases the significance of initial diagnosis. Diabetes brings other complicated complications like cardiovascular disease, kidney failure, stroke, damaging the vital organs etc. Early diagnosis of diabetes reduces the likelihood of transiting it into a chronic and severe state. The identification and analysis of risk factors of different spinal attributes help to identify the prevalence of diabetes in medical diagnosis. The prevalence measure and identification of diabetes in the early stages reduce the chances of future complications. In this research, the collective NHANES dataset of 1999-2000 to 2015-2016 was used and the purposes of this research were to analyze and ascertain the potential risk factors correlated with diabetes by using Logistic Regression, ANOVA and also to identify the abnormalities by using multiple supervised machine learning algorithms. Class imbalance, outlier problems were handled and experimental results show that age, blood-related diabetes, cholesterol and BMI are the most significant risk factors that associated with diabetes. Along with this, the highest accuracy score .90 was achieved with the random forest classification method.
Lets Grow More Virtual Internship Program(LGMVIP) Beginner level tasks as a Data Science Intern March-22
Repository for The projects that i completed that was assigned in the nanodegree course.
The global fashion industry is valued at three trillion dollars and accounts for 2 percent of the world's. GDP the fashion industry is undergoing a dramatic transformation by adopting new computer vision and Machine learning and deep learning techniques. In this case study we'll look at a hypothetical situation. We assume that if a retailer hired you to build a virtual stylist assistant that looks at customer Instagram and Facebook images and classifies what fashion category they are wearing either bags dresses and pants. The virtual assistant can help the retailer detect and forecast fashion trends and launch targeted marketing campaigns. In this story we're going to use the fashionmnist data. It's a data set that contains images of bags shoes and dresses. And we're asking the deep network to classify the images into 10 classes. So we wanted to build kind of an app per se or a model. They can look at images and can tell us exactly what category in this image. Is it like a short. Is it a bag. Is it like a hat. And so on. That's the whole objective. The data again they are divided into 28 by 28 greyscale images and the target class is actually No. 1 out of 10 which is kind of a target label which can be categorized as you can see into either like maybe a shoe maybe like like pants. Basically these are the target classes. We have the t shirts trousers pullovers ankle boots sneakers and so on so forth.
写过的深度学习文章以及翻译的文章等
Lead classification using python and logistic regression.
Machine Learning Model to classify whether a Mushroom is Edible or Poisonous by its features
This repository includes Machine Learning Algorithms
Machine learning has been one of the standard and improving techniques with strong methods for classification and reorganization based on recursive learning. Machine learning allows to train and test classification system, with Artificial Intelligence. Machine learning has provided greatest support for predicting disease with correct case of training and testing. Diabetes needs greatest support of machine learning to detect diabetes disease in early stage, since it cannot be cured and also brings great complication to our health system. One of the promising techniques in machine learning is Random, it is a classification and regression algorithm consisting of many decisions trees. It uses bagging and feature randomness when building each individual tree to try to create an uncorrelated forest of trees whose prediction by committee is more accurate than that of any individual tree.
This repository contains CryMLClassifier, a machine learning model that classifies baby cries into five categories. It utilizes 193 features extracted from the cry audio data, achieving high accuracy with Random Forest and XGBoost algorithms. The repository includes a "features_extraction" folder for feature extraction code samples.
Deploy a Machine Learning model into production using Docker
performed Exploratory Data Analysis and predictive analysis on given dataset from ANZ virtual intenship
"A lightweight, foundational machine learning framework implemented in Python. Features a modular design with customizable layers and support for various activation functions, as well as popular machine learning algorithms. Ideal for educational purposes and rapid prototyping." - ChatGPT Definition
A face recognition based security system that allow you to open certain apps on your computer through face unlock. If someone tries to breach the security it send alert mail to the concerned person.
The code detects text by creating a Convolution Neural Network to Classify digits from 0 to 9. The Training code is written from scratch and it trains about 10000 images of 10 different classes . Testing script can deduce the trained model data to use along with a webcam to detect Digits from 0 to 9.
watch a neural network create its own solutions on how to play simple games
An end-to-end project which includes statistical analysis and price prediction of the laptops available in June 2022.
Google BigQuery Tutorial
Customer Churn Rate Predicticted by Machine learning models
Perform customer return rate analysis and customer segmentation according to RFM criteria using the Kmean algorithm
Data Science & Machine Learning related projects
Solutions for various Kaggle competitions.
Machine Learning algorithm used to distinguish between spam and ham texts.
A machine learning repository for a variety of algorithms (KNN, Naive Bayes etc)
MetroPT-3 Anomaly Detection using Machine Learning and Deep Learning
A YOLOv5-based packing verification model utilizes real-time object detection to ensure accurate and efficient product packing