Certifications
Every entry links to its verification page.
IBM AI Engineering Professional Certificate
A six-module track covering machine learning and deep learning end to end — the major Python libraries, neural network architectures, and hands-on lab implementations of each. The through-line was less about any single model than about how to implement them properly and transfer that to new problems.
VerifyIBM AI Engineering Professional CertificateDeep Neural Networks with PyTorch
Part of the IBM AI Engineering track. Practical work in PyTorch alongside Pandas and NumPy, building foundational through intermediate understanding of deep neural networks.
VerifyDeep Neural Networks with PyTorchBuilding Deep Learning Models with TensorFlow
Supervised architectures — convolutional and recurrent networks — followed by unsupervised ones, including restricted Boltzmann machines for a recommendation system and autoencoders for image reconstruction.
VerifyBuilding Deep Learning Models with TensorFlowIntroduction to Deep Learning & Neural Networks with Keras
Fundamentals of deep learning and neural network design, built out across the TensorFlow and Keras library surface.
VerifyIntroduction to Deep Learning & Neural Networks with KerasIntroduction to Computer Vision and Image Processing
Image classification with computer vision tooling and Pillow, closing on a final project training a model to distinguish between traffic signs from a labelled image set.
VerifyIntroduction to Computer Vision and Image ProcessingMachine Learning with Python
A broad introduction to machine learning concepts and their application in Python, built through lectures and labs and assessed with a final exam and project.
VerifyMachine Learning with PythonPredicting House Prices with Regression using TensorFlow
A guided regression project over a housing dataset — date, age, transit distance, nearby amenities, coordinates and sale price — handled through Pandas, NumPy, Scikit-Learn, Matplotlib and Keras.
VerifyPredicting House Prices with Regression using TensorFlowBuild a Data Science Web App with Streamlit and Python
An interactive map of New York City motor vehicle collision statistics, built in Python and Streamlit, with an emphasis on structuring a data app so the code survives a change of dataset.
VerifyBuild a Data Science Web App with Streamlit and Python