Daryl Roberts Email & Phone Number

With a solid background as an Operations Manager, I've successfully led teams, managed complex data, and crafted innovative solutions to challenges. My time as a Sergeant in the Marine Corps further honed my problem-solving abilities, communication skills, and adaptive thinking.Driven by a profound belief in AI's capacity to revolutionize and enhance humanity, I've embarked on a transformative journey into the world of Machine Learning Engineering. Recognizing the rapid evolution of AI technologies, I made a deliberate choice to transition my career towards this groundbreaking field.Recently graduated from Fullstack's AI/ML bootcamp and supplemented my learning with specialized courses in AI/ML, Google's Tensorflow certification, Computer Vision, Langchain, PyTorch, Git, and Environment-based Reinforcement Learning . This journey underscores my unwavering commitment to continuous growth and development, positioning me as a dynamic force in AI development.Skills:Data Acquisition: Proficient in sourcing and collecting diverse datasets crucial for model training.Skills: SQL query, API utilization, Web scraping, Stuctured and Unstructured data (csv, txt, pdf, etc), Build Langchain based AI assistant for additional data exploration.Data Engineering: Skilled in cleaning, transforming, and preparing data for analysis and modeling. Additionally I have a keen eye for extracting meaningful features from existing data to feature engineer additional data insights to enhance model performance.Skills: Pandas, Numpy, Tensorflow, Seaborn, MatplotlibModel Building: Capable of developing robust machine learning models tailored to specific tasks and objectives.Skills: Tensorflow, Pytorch, Sci-kit Learn, Transfer Learning, Huggingface Hub, Implementation from research papersModel Testing and Fine Tuning: Thorough in evaluating model performance and iteratively refining for optimal results. Adept at optimizing model parameters for enhanced accuracy and efficiency.Skills: Gridsearch/random search parameter tuning, Data Augmentation, Upsampling, Learning rate optimization, evaluation of training metrics loss/accuracy using graphs to identify bias.