551 skills found · Page 5 of 19
Abdallah-M-Ali / Mineral Prospectivity Mapping MLApplication of Machine Learning to map mineral prospectivity using remote sensing and geological data. This research project select four supervised learning methods (RF, SVM, ANN, and CNN) to predict gold prospection in Hamassana area north east Sudan
anishsingh20 / Statistical Learning Using RThis is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.
abcxyzi / RadCharSSLRadar datasets for self-supervised radar signal recognition. This work is published at the 35th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2025).
zeglam / Countries GDP PredictionDeveloped a supervised machine learning system that can estimate a country's GDP per capita using regression algorithms.
francescopisu / UsedCarPricePrediction🚗 Solving the problem of predicting the price of a used car using Sklearn's supervised machine learning techniques.
girisagar46 / FYPFruitClassifierAutomatic Fruit Classifier Using Supervised AdaBoost Machine Learning Algorithm
scrapfishies / Twitter Bot DetectionTwitter Bot or Not: Twitter bot detection with supervised machine learning models
vshantam / Age PredictionThis Project is an applicaton based on Computer vision and Machine learning implementation using regression supervised classification.
jojo62000 / Learn Keras For Deep Neural NetworksLearn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.
msultan / SML CVUsing supervised machine learning to build collective variables for accelerated sampling
emirhanai / Emotion Prediction With Quantum5 Neural Network AI Machine LearningEmotion Prediction with Semi Supervised Learning of Machine Learning Software with RC Algorithm - By Emirhan BULUT Emotion-Prediction-with-Quantum5-Neural-Network-AI-Machine-Learning
max-fitzpatrick / Machine Learning Trading AlgorithmMaster's degree project: Development of a trading algorithm which uses supervised machine learning classification techniques to generate buy/sell signals
KaziAmitHasan / Prediction Of Clinical Risk Factors Of Diabetes Using ML Resolving Class ImbalanceBeing 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.
sechidis / 2018 MLJ Semi Supervised Feature SelectionMatlab code of the paper "Simple Strategies for Semi-Supervised Feature Selection", published in Machine Learning Journal
serkor1 / SLmetricsA high-performance R :package: for supervised and unsupervised machine learning evaluation metrics witten in 'C++'.
tonyleidong / OptimalFlowOptimalFlow is an omni-ensemble and scalable automated machine learning Python toolkit, which uses Pipeline Cluster Traversal Experiments(PCTE) and Selection-based Feature Preprocessor with Ensemble Encoding(SPEE), to help data scientists build optimal models, and automate supervised learning workflow with simpler coding.
MauroCE / PythonBRMLtoolboxPython 3.7 version of David Barber's MATLAB BRMLtoolbox
DataSystemsGroupUT / SmartMLSmartML: Supervised Machine Learning Automation in R
dtkirsch / HmmThis project is a Ruby gem ('hmm') for machine learning that natively implements a (somewhat) generalized Hidden Markov Model classifier. At present, it is capable of supervised learning (using labeled training data) and Viterbi decoding. Unsupervised learning is on the way.
AdArya125 / Primer To Machine Learning'Primer to Machine Learning' is a comprehensive guide covering essential topics in machine learning, including statistics, data preprocessing, supervised and unsupervised learning, neural networks, deep learning, NLP, time series analysis, and reinforcement learning. Perfect for beginners and intermediates.
