Course Report
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Course Features
Duration
5 months
Delivery Method
Online
Available on
Limited Access
Accessibility
Desktop, Laptop
Language
English
Subtitles
English
Level
Advanced
Effort
10 hours per week
Teaching Type
Self Paced
Course Description
Course Overview
Job Assistance
Personlized Teaching
International Faculty
Case Based Learning
Post Course Interactions
Case Studies,Hands-On Training,Instructor-Moderated Discussions
Case Studies, Captstone Projects
Skills You Will Gain
Prerequisites/Requirements
To optimize your chances of success in this program, we recommend intermediate Python programming knowledge and intermediate knowledge of machine learning algorithms
What You Will Learn
Test Python code and build a Python package of their own
Build predictive models using a variety of unsupervised and supervised machine learning techniques
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
Target Students
Anyone who meets the eligibility criteria can join this course
Course Instructors
Matt Maybeno
Principal Software Engineer
Joseph Nicolls
Senior Machine Learning Engineer
Charles Landau
Technical Lead
Soham Chatterjee
Graduate
Corporate Sponsors
Course Reviews
Average Rating Based on 6 reviews
100%