Hi, I'm Hoque
From Toronto, Canada

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Mohammed Hoque


Building Intelligent Solutions in AI, Machine Learning, Deep Learning & Computer Vision

With a strong foundation in AI research and hands-on experience in deep learning, I specialize in creating real-world applications—from emotion recognition and medical imaging to autonomous systems and digital product innovation.

Passionate about solving complex problems using data-driven methods, collaborating with diverse teams, and continuously learning to push the boundaries of what's possible with AI.

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Excellence in AI

Brain Tumor Detection Using YOLOv12:

Medical AI + Computer Vision + Custom YOLO.

Transforming MRI scans into actionable clinical insights with AI.

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Forest Fire Detection Using YOLOv12:

Real-world object detection, custom training.

AI-driven monitoring to identify forest fires and help protect wildlife and communities.

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Automatic License Plate Recognition (ALPR):

Computer Vision application.

AI-powered real-time vehicle identification for smarter traffic management and secure access control.

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Autonomous Driving using NVIDIA CNN:

Strong Deep Learning & CNN project.

Transforming camera imagery into real-time driving decisions.

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Facial Emotion Detection:

Image classification + Computer Vision.

Real-time human facial emotion recognition for smarter, empathetic systems.

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YOLOv12 Custom Training:

Shows understanding of model training.

Building task-specific vision models for real-world applications.

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Dataset Preparation for YOLO:

Demonstrates complete ML pipeline.

Turning raw images into high-quality training data.

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Image Compression using PCA & K-Means:

Image compressing fundamentals.

Reducing data size while preserving visual quality.

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Image Processing using PCA & K-Means:

Computer vision preprocessing.

Transforming raw pixels into informative representations.

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Deep Neural Networks (Multi-Class Classification):

Neural network fundamentals.

Learning rich feature representations for accurate classification.

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Self-Driving Cars Implementation:

Reinforces autonomous driving knowledge.

Turning visual input into intelligent driving actions.

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Neural Network Visualization:

Feature Learning & Visualization.

Making hidden representations visible and understandable.

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Spam Detection using SVM:

Classic Supervised Machine Learning.

Enhancing inbox security through machine learning.

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SVM Classification:

Advanced SVM Techniques for Machine Learning.

Transforming complex data into confident decisions.

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Anomaly Detection & Recommender Systems:

Useful Machine Learning concepts.

Uncovering hidden patterns and delivering personalized insights.

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Logistic Regression:

Fundamental ML. Logistic regression & Sigmoid function.

Transforming features into confident binary decisions.

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House Price Prediction:

Predicting House Prices with Gradient Descent. A Regression example.

Interactive House Price Prediction & Cost Analysis by Machine Learning.

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Feature Normalization & Gradient Descent:

ML fundamentals. Feature Normalization & Cost Optimization.

Demonstrate how linear regression models learn through cost minimization.

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Bias-Variance Analysis:

Educational project. Controlling Overfitting & Underfitting in Machine Learning

Understanding Bias-Variance Trade-off in Regularized Polynomial Regression.

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Waymo vs Tesla:

The Reality of Self-Driving Cars.

Comparative analysis of Waymo and Tesla's self-driving technologies.

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Contact Me

sap.hoque@gmail.com

+1 473 607 1949

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