1. Test Python code and build a Python package of their own.
2. Build predictive models using a variety of unsupervised and supervised machine learning techniques.
3. Use Amazon SageMaker to deploy machine learning models to production environments, such as a web application or piece of hardware.
4. To conduct A/B test two different deployed models and evaluate their performance.
5. To utilize an API to deploy a model to a website such that it responds to user input, dynamically.
6. To update a deployed model, in response to changes in the underlying data source.
Click here for course syllabus
Learners shall have to prove their skills by completing the following projects:
- Build a Python Package: Write a Python package on your own using software engineering best practices for writing production level code. This project is optional and will not be graded.
- Deploy a Sentiment Analysis Model: Using SageMaker, deploy your own PyTorch sentiment analysis model, which is trained to recognize the sentiment of movie reviews (positive or negative).
- Plagiarism Detector: Engineer features that can help identify cases of plagiarism in text and deploy a trained plagiarism detection model using Amazon SageMaker.
- Capstone Project & Proposal: Complete a final project—choosing from a few, provided options or a project of your own design—that involves data exploration and machine learning.
- Learners will be awarded Certificate of Completion co-issued by TP and Udacity in Artificial Intelligence Programming with Python upon completion of at least ONE project.
- Learners will be awarded the Nanodegree conferred by Udacity in Artificial Intelligence Programming with Python upon completion of ALL projects.