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.

Projects:

• 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).

  • Project and review from Udacity

• 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.

  • Project and review from Udacity

• Machine Learning Capstone Project

  A final project that involves data exploration and machine learning.

  • About
  • Project Proposal
  • Project Report

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Last update: 4 July 2021