Name: Samuel Kolawole
Type: User
Company: Loopify
Bio: Data Scientist || Mech Engr. Steam & Power Generation || Python, SQL, R, Spark || Data Engineering & Analytics – Machine Learning – Big Data – Image processin
Location: London Uk
developing-machine-learning-models-for-back-pain-prediction-utilizing-video-recordings-of-gaits
Developing machine learning models to classify high, medium and low back pain risk based on vide recording of gaits: Utilising OpenPose a computer vision technique for features extraction.
distributed-linear-regression-analysis
The housing dataset is used to develop a pipeline for running a simple linear regression in a pseudo-distributed manner (single node setup) using Spark ML. The following was implemented: • Created a Spark session, loaded the data, parse and display them using the apache spark ecosystem (pyspark). View "read me" to see full desciption.
fall-risk-prediction-computer-vision-machine-learning
Developing machine learning models to classify high and low fall risk based on vide recording of gaits: Utilising OpenPose a computer vision technique for features extraction.
image-classification
Data collection, statistical analysis, interpretation and image classification of the obtained diseases images are the focus of this project, as well as the exploration of quantitative methods for analysing this data using machine learning (support vector machine, fully connected neural network, convolutional neural network, K-nearest neighbour etc
text-processing-information-retrieval-and-information-extraction-text-to-speech
The author implemented simple rule base solution and machine learning approach for information retrieval and information extraction after which the result were analyzed.
text-processing-sentiments-analysis
The author implemented support vector machine for sentiments analysis and applied two feature extractions, Bag-of-Words (CountVectorizer) and TF-IDF (TfidfVectorizer), after which the results for both methods were analysed. The accuracy obtained for both methods were (BoW = 87%) and (TF-IDF = 86%).
text-processing-topic-labelling
The author implemented logistic regression and support vector machine for topic labelling and applied two feature extractions, Bag-of-Words (CountVectorizer) and TF-IDF (TfidfVectorizer), after which the results for both methods were analyzed.
weed-classification
Created image classifiers as well as an access model by using Python to create a process for the processing of image data. This pipeline include: • Pre-processing, feature extraction, train classifiers with extracted features and labels from train, test, and val set. • Evaluate models with extracted features from test and val set with Visualisation
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
Django
The Web framework for perfectionists with deadlines.
Laravel
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft
Open source projects and samples from Microsoft.
Google
Google ❤️ Open Source for everyone.
Alibaba
Alibaba Open Source for everyone
D3
Data-Driven Documents codes.
Tencent
China tencent open source team.
