Machine Learning Engineer Nanodegree
Period: 28 August 2019 - 28 November 2019 (Ongoing)
Objectives: Build predictive models using a variety of unsupervised and supervised machine learning techniques. Understand cloud deployment terminology and best practices. Use Amazon SageMaker to deploy machine learning models to production environments, such as a web application or piece of hardware. A/B test two different deployed models and evaluate their performance. Utilize an API to deploy a model to a website such that it responds to user input, dynamically. Update a deployed model, in response to changes in the underlying data source.
More information, visit the Udacity Machine Learning Nanodegree webpage.
Machine Learning in Production. Deploy a Sentiment Analysis Model.
Using SageMaker, deploy a PyTorch sentiment analysis model, which is trained to recognize the sentiment of movie reviews (positive or negative).
Machine Learning Case Studies. Plagiarism Detector.
Engineer features that can help identify cases of plagiarism in text and deploy a trained plagiarism detection model using Amazon SageMaker.
Machine Learning Capstone Project
A final project that involves data exploration and machine learning.
Last update: 4 July 2021