GGYIMAH1031
Data Scientist / Deep Learning Engineer
Missouri University of Science & TechnologyMissouri, USA
Pinned Repositories
2016_POTUS_Election-Sentiment_Analysis_And_Topic_Modeling
Building_A_Spam_Filter
Building A Spam Filter Using the Naive Bayes Classifier
Conjoint_Analysis_Mobile_Service_Preferences
Image_Data_for_Formation_Recognition_-_Obstacle_Detection
This repository contains train and test data sets for building a convolutional neural network model, which is able to recognize different soil/rock formations as well as detect obstacles in a mining / construction environment. This effort is towards the development of smart, autonomous excavators which are able to recognize different excavating environments and adjust the digging strategy accordingly.
jupyter
Jupyter metapackage for installation, docs and chat
Market_Analysis-Predicting_Customer_Churn
MarketBasketAnalysis
Predicting-Tennis-Matches-Live-Betting-
Predictive_Maintenance_Analytics
A model for predicting imminent machine failure
Smart_Surveillance
An intelligent video surveillance system that flags suspicious activities.
GGYIMAH1031's Repositories
GGYIMAH1031/stochastic_approximation
Python implementation of various stochastic approximation algorithms
GGYIMAH1031/GeoSearch-Tweepy
Python code, using the Tweepy and MySQLdb modules, to stream the Twitter API
GGYIMAH1031/opencv_python_tutorials
A place to play around with the OpenCV-Python Tutorials.
GGYIMAH1031/pyensemble
An implementation of Caruana et al's Ensemble Selection algorithm in Python, based on scikit-learn
GGYIMAH1031/quadprog
Quadratic Programming Solver
GGYIMAH1031/scipy_2015_sklearn_tutorial
Scikit-Learn tutorial material for Scipy 2015
GGYIMAH1031/spsa
Simultaneous perturbation stochastic approximation Python code
GGYIMAH1031/Stanford-Machine-Learning-Course
machine learning course programming exercise
GGYIMAH1031/statistical-analysis-python-tutorial
Statistical Data Analysis in Python
GGYIMAH1031/Support-Vector-Machine
MATLAB implementation of the Support Vector Machine (Dual) Algorithm
GGYIMAH1031/SVM-Kernels
Goal of this project is to implement perceptron,Dual perceptron,Linear Kernel and RBF kernel without using any Machine Learning Libraries