Information Technology
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AWS Machine Learning Engineer

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Course Report - AWS Machine Learning Engineer

Course Report

Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.

Course Features

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Duration

5 months

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Advanced

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Effort

10 hours per week

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Teaching Type

Self Paced

Course Description

Advanced machine learning algorithms and techniques are taught. You will also learn how to package your models and deploy them to a production environment. Amazon SageMaker is a tool that allows you to test and deploy your model to a web app. Learn how to update models as you collect more data. This is an important skill in the industry.

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Highlights

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Pedagogy

Top 5 Percentile

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Course Credibility

Top 1 Percentile

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Rating & Reviews

Top 30 Percentile

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Career Impact

Top 1 Percentile

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Hands on training

Top 10 Percentile

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Parameters

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Course Credibility

Delivered through Udacity a renowned institution in the field, this course offers a comprehensive learning experience.

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Pedagogy

This comprehensive course equips you with all major AWS skills applicable to your daily life. Personalized teaching ensures one-on-one doubt resolution with faculty, maximizing skill acquisition. These practical skills empower you to confidently apply your knowledge and thrive in various real-life situations. An exceptional course in AWS, this stands out for its Self Paced learning approach. Learners have the flexibility to progress at their own speed, tailoring the experience to their individual needs.

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Hands on training

This course stands out as one of the top 10 percentile options in AWS, offering unparalleled hands-on training. Learners gain practical experience and skills through immersive learning, preparing them for real-world challenges. It ensures a well-rounded skill set, catering to a range of learning preferences. With a focus on Hands on training and Capstone Projects / Industry-Simulation as well as essential Case Based Learning, this course is tailored to meet diverse educational needs.

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Career Impact

This course is exceptional, ranking among the top 1 percentile in AWS for its significant career impact and excellent job assistance. Learners benefit from valuable career opportunities and support, enabling them to secure relevant positions and excel in the industry. The course's dual focus on career impact and job assistance enhances its value, making it an ideal choice for individuals seeking to advance their careers and succeed in the AWS field.

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Rating & Reviews

This highly acclaimed course is among the top-rated in AWS, boasting a rating greater than 4 and an overall rating of 5.0. Its exceptional quality sets it apart, making it an excellent choice for individuals seeking top-notch learning experience in AWS.

Course Overview

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Job Assistance

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Personlized Teaching

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International Faculty

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Case Based Learning

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Post Course Interactions

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Case Studies,Hands-On Training,Instructor-Moderated Discussions

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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

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Matt Maybeno

Principal Software Engineer

Matt Maybeno is a Principal Software Engineer at SOCi. With a masters in Bioinformatics from SDSU, he utilizes his cross domain expertise to build solutions in NLP and predictive analytics.
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Joseph Nicolls

Senior Machine Learning Engineer

Joseph Nicolls is a senior machine learning scientist at Blue Hexagon. With a major in Biomedical Computation from Stanford University, he currently utilizes machine learning to build malware-detecting solutions at Blue Hexagon.
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Charles Landau

Technical Lead

Charles holds a MPA from George Washington University, where he focused on econometrics and regulatory policy, and holds a BA from Boston University. At Guidehouse, he supports data scientists and de...
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Soham Chatterjee

Graduate

Soham is an Intel Software Innovator and a former Deep Learning Researcher at Saama Technologies. He is currently a Masters by Research student at NTU, Singapore. His research is on Edge Computing, IoT and Neuromorphic Hardware.

Corporate Sponsors

Course Reviews

Average Rating Based on 6 reviews

5.0

100%

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