241 skills found · Page 5 of 9
TorchlightLegal / Database Build 1.0This repo provides database architecture that provides case law for Legal Research & Machine Learning Model Study
weasteam / Coursera Machine LearningAbout this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
muhammadsohaib60 / Urdu OCROur project is based on one of the most important application of machine learning i.e. pattern recognition. Optical character recognition or optical character reader is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo or from subtitle text superimposed on an image. We are working on developing an OCR for URDU. We studied a couple of research papers related to our project. So far, we have found that Both Arabic and Urdu are written in Perso-Arabic script; at the written level, therefore, they share similarities. The styles of Arabic and Persian writing have a heavy influence on the Urdu script. There are 6 major styles for writing Arabic, Persian and Pashto as well. Urdu is written in Naskh writing style which is most famous of all. Optical character recognition (OCR) is the process of converting an image of text, such as a scanned paper document or electronic fax file, into computer-editable text [1]. The text in an image is not editable: the letters are made of tiny dots (pixels) that together form a picture of text. During OCR, the software analyzes an image and converts the pictures of the characters to editable text based on the patterns of the pixels in the image. After OCR, the converted text can be exported and used with a variety of word-processing, page layout and spreadsheet applications [2]. One of the main aims of OCR is to emulate the human ability to read at a much faster rate by associating symbolic identities with images of characters. Its potential applications include Screen Readers, Refreshable Braille Displays [3], reading customer filled forms, reading postal address off envelops, archiving and retrieving text etc. OCR’s ultimate goal is to develop a communication interface between the computer and its potential users. Urdu is the national language of Pakistan. It is a language that is understood by over 300 million people belonging to Pakistan, India and Bangladesh. Due to its historical database of literature, there is definitely a need to devise automatic systems for conversion of this literature into electronic form that may be accessible on the worldwide web. Although much work has been done in the field of OCR, Urdu and other languages using the Arabic script like Farsi, Urdu and Arabic, have received least attention. This is due in part to a lack of interest in the field and in part to the intricacies of the Arabic script. Owing to this state of indifference, there remains a huge amount of Urdu and Arabic literature unattended and rotting away on some old shelves. The proposed research aims to develop workable solutions to many of the problems faced in realization of an OCR designed specifically for Urdu Noori Nastaleeq Script, which is widely used in Urdu newspapers, governmental documents and books. The underlying processes first isolate and classify ligatures based on certain carefully chosen special, contour and statistical features and eventually recognize them with the aid of Feed-Forward Back Propagation Neural Networks. The input to the system is a monochrome bitmap image file of Urdu text written in Noori Nastaleeq and the output is the equivalent text converted to an editable text file.
AdityaDutt / MultiColor Shapes DatabaseA small database to test different machine learning tasks. It contains simple shapes of different colors.
datasciencelearnofficial / Diabetes Prediction Machine Learning Project🚀 Diabetes Prediction Machine Learning project using the Pima Indians Diabetes Database. 🩺
cmcmicrosystems / FPGA GPU ClusterThe FPGA/GPU cluster is a cloud-based, remotely accessible compute infrastructure specifically designed to accelerate compute intensive applications, such as machine learning training and inference, video processing, financial computing, database analytics networking and bioinformatics. Latest state of the art acceleration technologies including the Alveo FPGAs, and Tesla V100 GPUs, closely coupled with server processors constitute the backbone of this cluster. The software stack consists of a complete ecosystem of machine learning frameworks, libraries and runtime targeting heterogeneous computing accelerators.
ritabratamaiti / BlooddonorpredictionThanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage of classifiers. In this study, we look at a Blood Transfusion Service Center Data Set (Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python. From Support Vector Machines(SVM), we use Support Vector Classification(SVC), from the linear model we import Perceptron. We also used the K.neighborsclassifier and the decision tree classifiers. We segmented the database into the 2 parts. Using the first, we trained the classifiers and the next part was used to verify if the classifier prediction matched that of the actual values.
MD-Rifat1709 / Retinal Blood Vessel SegmentationEvaluation of the segmented vascular structures of the retina of our eye obtained through fundus photography using Machine learning techniques. Two open-source databases of the retinal images (DRIVE and STARE) are used. K - Means Clustering Algorithm is used for the segmentation of the retinal images. MATLAB r2020b environment was employed for feature extraction and image segmentation. The accuracy of the segmentation is evaluated for both the database as well as the algorithms. A simple GUI was developed for convinience of evaluation. The future work in this project deals with improving the accuracy of the segmented vessels and using them for classification of vessels into arteries and veins, and also for identification of various diseases like Diabetic Retinopathy, Stroke, Glaucoma etc. A more interactive, highlyautomated graphical user interface (GUI) may also be developed for user convenience and the software may be made compatible for various devices in the future.
niladri-1 / Spam Mail ClassificationThe Spam Mail Classification project is a web-based application that uses machine learning to classify emails as spam or ham. It features a Flask backend, a frontend created with HTML, CSS, and JavaScript, and a MySQL database for storing user data and email classifications.
crivello-jc / Sigma Phase PredictionThis code contributes to predict any properties (heat of formation and crystal data) from a DFT learning database by a supervised machine learning
melodimag7700 / Churn Cust PredictThe main goal of this project is to predict the probability of customer churn using the provided bank database, by applying machine learning models and comparing their performances.
terchris / Shadow Brregshadow-brreg is a system that creates a shadow database copy of all companies in Norway. Enables you to play with machine learning, data science, data analysis, data visualization on your local machine with relevant data. Updated every minute with all changes from Norwegian public records www.brreg.no Runs on any machine - install with one command
yodigi7 / Runescape Grand Exchange Data AnalyticsA project to automatically scrape the web for information on the grand exchange and update prices. It keeps all of this data in the sqlite3 database. It also has machine deep-learning functionality to try to predict future prices and trends.
ankitsingh1240 / CUSTOMER CHURN PREDICTIONINSAIDINSTRUCTIONS:You are required to come up with the solution of the given business case.Business Context:This case requires trainees to develop a model for predicting customer churn at a fictitious wireless telecom company and use insights from the model to develop an incentive plan for enticing would-be churners to remain with company.Data for the case are available in csv format. The data are a scaled down version of the full database generously donated by an anonymous wireless telephone company. There are still 7043 customers in the database, and 20 potential predictors. Candidates can use whatever method they wish to develop their machine learning model. The data are available inone data file with 7043 rows that combines the calibration and validation customers. “calibration” database consisting of 4000customers and a “validation” database consisting of 3043customers. Each database contained (1) a “churn” variable signifying whether the customer had left the company two months after observation, and (2) a set of 20 potential predictor variables that could be used in a predictive churn model. Following usual model development procedures, the model would be estimated on the calibration data and tested on the validation data.
DHEEPAK29 / Project ML IoTObjective : To help Doctors (or) Supervisors remotely monitor the Condition of the ambiance, Health condition, and the proximity from the set location of a Patient (or) Person under observation. This project imbibes the concept of the Internet of Things (IoT) and so the data is accessible seamlessly even if the supervisor is remote. Using range detection techniques and health parameters, Manipulations are done in the backend such that the alerts are notified based on set conditions to the respective person in case of emergency, we can conclusively predict the condition of a Patient (or) Person under observation remotely and accurately. Further, the data received from a patient is integrated into the database for analytics in Machine Learning (ML) to predict the reaction of another patient who suffers from the same disease or condition in the future. In addition, the product is feasible to be designed as a Handy and User-Friendly prototype, Cost-Efficient model, Less power-consuming mechanism, and Alterable Design.
shalikaprasad / Cloud Provider Selection Recommendation Using Machine LearningCloud computing (CC) has recently been receiving tremendous attention from the IT trade and educational researchers. CC leverages its distinctive services to cloud customers in a very pay-as-you-go, anytime and anyplace manner. As well as Cloud services offer dynamically scalable services on demand. Therefore, service supplying plays a key role in CC. Then, it is good opportunity for customers to find suitable and lowly cost service for their project. Specially, Customer must be able to select appropriate cloud service according to their needs and money. It is time-consuming task for consumers to collect the necessary information and analyze from all cloud service providers to make right decision. As well as it is also a highly demanding task from a computational perspective because multiple consumers who have similar requirements conduct same computations repeatedly. They provide all products you might need for moving your business to the cloud. But these product offerings differ in pricing as well as the naming of their services. Some Businessmen already may use on-premises infrastructure or think which infrastructure will use for my project. They may have more complex problems like how to choose a cloud service, which services want use and specially how many costs want to pay for monthly or yearly. Sometimes, someone already use a cloud services, they have lot of problems like more expensive, less flexibility, hard to use, overwhelming options of services, poor management of GUI and tool, complex price schema and other issues. However, they must spend more price and time as useless. Because they could not select best cloud service provider early to their business. For solving the cloud service selection problem, many researchers have proposed some approaches including multicriteria decision analysis (MCDA) and Brokerage-Based Approach. But we cannot see any machine learning prediction system for solving this issue. This system enables the user to choose from among a number of available choices. In this paper, we make a neural network with TensorFlow to service selection in CC. This system focuses on three main players in CC. There are Amazon Web Services, Microsoft Azure and Google Cloud Platform in the race for cloud services providers. I identify and synthesize several products relevant for web services in Cloud providers. There are Featured, Compute, Storage, Database, Networking, Operation, Identity & Access and Cost. As well as I focus on Small and medium-sized businesses (SMBs). Because these are most aggressive segment in cloud service. It is less-complex IT needs, fewer legacy applications and less IT support than larger enterprises. We use Support Vector Machine (SVM), Multiple linear regression (MLR) and Multiple-criteria decision analysis (MCDA). We develop efficient and flexible recommendation system for ranking cloud service providers. I prove accuracy and effectiveness of our approach through an experimental study with the real and synthetic Cloud data.
mldbai / PymldbPython interface to the Machine Learning Database (MLDB).
OracleDataManagementSpain / ConvergedDatabaseOracle Converged Database 19c workshop series, including: Multitenant, Multimodel, In-Memory, Spatial & Graph, Machine Learning with Phyton and R, Multicloud and Autonomous Database
takahashi-akira-36m / Oxi Diel DbDatabase and machine learning prediction models of dielectric constants of oxides obtained by first principles calculations.
TmaxTiberoOSP / In Database MLPython Server for In-Database Machine Learning
