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AWS Machine Learning Engineer Nanodegree

Nanodegree Program

The goal of the AWS Machine Learning Engineer (MLE) Nanodegree program is to equip software developers/data scientists with the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker. This program will focus on the latest best practices and capabilities that are enabled by Amazon SageMaker, including new model design/deployment features and case studies in which they can be applied to.

The goal of the AWS Machine Learning Engineer (MLE) Nanodegree program is to equip software developers/data scientists with the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker. This program will focus on the latest best practices and capabilities that are enabled by Amazon SageMaker, including new model design/deployment features and case studies in which they can be applied to.

Built in collaboration with

AWS

Intermediate

5 months

Real-world Projects

Completion Certificate

Last Updated February 26, 2024

Skills you'll learn:
Neural network basics • Deep learning fluency • Sagemaker jumpstart • Machine learning framework fundamentals
Prerequisites:
AWS familiarity • Intermediate Python • API proficiency

Courses In This Program

Course 1 45 minutes

Welcome to AWS Machine Learning Engineer Nanodegree

Course 2 4 weeks

Introduction to Machine Learning

In this course, you'll start learning what machine learning is by being introduced to the high level concepts through AWS SageMaker. You'll begin by using SageMaker Studio to perform exploratory data analysis. Know how and when to apply the basic concepts of machine learning to real world scenarios. Create machine learning workflows, starting with data cleaning and feature engineering, to evaluation and hyperparameter tuning. Finally, you'll build new ML workflows with highly sophisticated models such as XGBoost and AutoGluon.

Course 3 4 weeks

Developing your First ML Workflow

This course discusses how to use AWS services to train a model, deploy a model, and how to use AWS Lambda Functions, Step Functions to compose your model and services into an event-driven application.

Course 4 4 weeks

Deep Learning Topics with Computer Vision and NLP

In this course you will learn how to train, finetune and deploy deep learning models using Amazon SageMaker. You’ll begin by learning what deep learning is, where it is used, and the tools used by deep learning engineers. Next we will learn about artificial neurons and neural networks and how to train them. After that we will learn about advanced neural network architectures like Convolutional Neural Networks and BERT as well as how to finetune them for specific tasks. Finally, you will learn about Amazon SageMaker and you will take everything you learned and do them in SageMaker Studio.

Taught By The Best

Photo of Matt Maybeno

Matt Maybeno

Principal Software Engineer

Matt 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.

Photo of Bradford Tuckfield

Bradford Tuckfield

Data Scientist and Writer

Bradford Tuckfield is a data scientist and writer. He has worked on applications of data science in a variety of industries. He's the author of Dive Into Algorithms, forthcoming with No Starch Press.

Photo of Soham Chatterjee

Soham Chatterjee

GRADUATE STUDENT AT THE NANYANG TECHNOLOGICAL UNIVERSITY

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.

Photo of Charles Landau

Charles Landau

Technical Lead, AI/ML - Guidehouse

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 developers working on internal and client-facing ML platforms.

Photo of Joseph Nicolls

Joseph Nicolls

Senior Machine Learning Engineer - Blue Hexagon

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.

Ratings & Reviews

Average Rating: 4.7 Stars

(41 Reviews)

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About AWS Machine Learning Engineer Nanodegree

Our AWS Machine Learning Engineer Nanodegree program, built in collaboration with AWS, is an intermediate-level machine learning engineering course. It's designed to equip you with the skills needed to build and deploy machine learning models using Amazon SageMaker. The program covers neural network basics, deep learning fluency, and essential machine learning framework fundamentals. You'll learn through practical courses, including developing your first ML workflow and exploring deep learning topics with computer vision and NLP. At Udacity, we provide an unparalleled learning experience, combining expert instruction with real-world projects that ensure you can apply your skills immediately. Under the guidance of industry professionals like Matt Maybeno, you'll gain hands-on experience in AWS machine learning, preparing you to excel as an AWS machine learning engineer.