I have a Master of Engineer in AI and Data Science from the University of Ottawa. Here are some of the projects done during academics and after.
-
- Apply Clustering models (Kmeans, SOM, T-NSE and DBSCAN).
-
- Apply Naïve Bayes Classification.
- Study how the mean and the variance values affect posterior probabilities.
-
- Implement One-versus-Rest (OvR) multiclass classification.
-
- Apply Multi Layer Perceptron (MLP)
- Test different activation functions.
-
Decision Tree, Bagging and Boosting:
- Train Decision tree, bagging and boosting.
- Plot decision boundaries.
-
Comparing Different Algorithms:
- Train Decision Tree, SVM and Gradiant Boost.
- Apply feature selection, finetuing and compare the models with stacking algorithm.
-
- Apply different preprocessing techniques to improve classifier performance.
- Achieved F1 score: 0.96187 by Decision tree classifier.
-
- Dos-DDos Detector using ensemble Deep learning models
- Achived 0.9991 F1 score.
-
- Train adaptive model using fixed sliding window technique.
- Retrieve data from Kafka server.
-
Handwritten Digit Recognition:
- Tain a CNN model on MNIST dataset.
-
- Train Yolov4 model to detect Tennis player, racket and ball.
-
COVID Protective Measures Tracking : Graduation project
- Monitor how well people apply COVID protective measures by
- Count people in a palce
- Detect if they are wearing face masks or not
- Measure the destances between them.
- Monitor how well people apply COVID protective measures by
-
People Counter : Count people inside some given ROIs.
-
- Classify between 5 books with the same Genre and different outhers.
-
- Produce similar clusters and compare them.
- Analyze the pros and cons of algorithms, generate and communicate the insights.
-
- Answer speech questions on a specified document.
-
E-Commerce Chatbot using Rasa.