Experienced in app development and passionate about data-driven solutions, I enjoy aligning technology strategies with business objectives. I’m seeking new challenges in innovative organizations. I’m most interested in machine learning and data driven decision making, low-latency systems, as well as augmented reality applications.
Ongoing, Start: Trimester 1, 2024
Applied Machine Learning, Software Optimization, Distributed Systems
Software Engineering, Applied Logic, Business Strategy
Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements. Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application. Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools. Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.
A deep understanding of the math that makes machine learning algorithms work. Fundamental skills that employers desire, helping you ace machine learning interview questions and land your dream job. Statistical techniques that empower you to get more out of your data analysis.
Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications. Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data. Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow. Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering.
Best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for a computer vision applications. Build natural language processing systems using TensorFlow. Handle real-world image data and explore strategies to prevent overfitting, including augmentation and dropout. Apply RNNs, GRUs, and LSTMs as you train them using text repositories.
Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression). Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection. Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods. Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model.