Chapter 01 · Completed
Data Science Foundations
DEPI IBM Data Science Track
Learned Python for Data Science core syntax and foundational programming. Used Jupyter Notebooks, GitHub, and cloud environments. Extracted and queried data using SQL and APIs. Analyzed and visualized data with Pandas, NumPy, Matplotlib, and Seaborn. Built regression, classification, clustering, and recommender systems using Scikit-Learn. Completed a hands-on capstone project applying the full data science pipeline to a real-world dataset.
Chapter 02 · Completed
Machine Learning Specialization
Andrew Ng — Stanford / DeepLearning.AI
Studied supervised learning (linear/logistic regression, neural networks, decision trees), advanced algorithms (ensembles, clustering, anomaly detection, recommender systems), and practical ML engineering (bias/variance, error analysis, CV pipelines, sklearn workflows). Built a strong theoretical and applied foundation in modern ML.
Chapter 03 · Completed
Deep Learning for Computer Vision
Coursat.ai — DL for CV Certificate
Studied deep learning fundamentals applied to computer vision — CNNs, backpropagation, activation functions, and training pipelines for image data. Built custom CNN architectures from scratch.
Chapter 04 · Completed
Computer Vision Applications
Coursat.ai — CV Applications Certificate
Applied CV techniques to real-world problems: image classification with AlexNet and ResNet, facial emotion recognition (FER-2013), and transfer learning using pre-trained models like ResNet50V2.
Chapter 05 · Paused
Attention & Transformers
Self-study — 'Attention Is All You Need' paper
Studied the Attention mechanism and the Transformer architecture. Worked through the seminal paper, understanding multi-head attention, positional encodings, and how transformers revolutionized sequence modeling and vision tasks.
Chapter 06 · Paused
Object Detection & Mobile CV
Ala Mahlak — Driver Monitoring System
Built an on-device real-time Driver Monitoring System fusing Google ML Kit Face Mesh, YOLOv8n (ONNX), head pose estimation via pure Dart solvePnP, and multi-signal fusion. Implemented EAR (eye closure), MAR (yawning), gaze estimation, PERCLOS analysis, and a temporal state machine — entirely offline at ~15 FPS on mid-range Android.