Udacity is excited to introduce a brand new and updated version of the AWS Machine Learning Engineer Nanodegree program. This redesigned program in the School of AI takes everything great about the previous Machine Learning Nanodegree program and adds in all the latest tools and technology so that students graduate with the newest and most in-demand skills.
What is Machine Learning?
Machine learning (ML) is a subset of artificial intelligence (AI). An ML engineer works to create algorithms that use large training datasets to learn how to make decisions, classify objects, and much more. Machine learning is present in Spotify radio song suggestions, Google Maps driving directions, and your virtual phone assistant, like Siri.
About the AWS Machine Learning Engineer Nanodegree Program
The AWS Machine Learning Engineer Nanodegree program will prepare you to learn how to create ML models, deploy ML models to API endpoints,integrate them into workflows, solve computer vision and natural language processing (NLP) using neural networks, and operationalize ML pipelines.
To get the most out of this course, it’s important to have the following prerequisites:
- At least 40 hours of programming experience
- Knowledge of functions, variables, loops, and classes
- Familiarity with data structures, like dictionaries and lists
- Experience with programming libraries, including NumPy and pandas
- Recommended experience with Python using Jupyter Notebooks
- Knowledge of using and building APIs
- Basic understanding of ML workflows
- Theoretical knowledge of ML algorithms, such as linear regression, logistic regression, and neural networks
- Experience with model training and testing
- Familiarity with metrics used for ML model evaluation, such as accuracy, precision, recall, and mean square error (MSE)
In as little as five months (at 5-10 hours a week), students who enroll in the AWS Machine Learning Engineer Nanodegree program will learn how to work with SageMaker and various AWS tools to solve ML challenges.
Specific Projects Include:
Project 1: Predict Bike Sharing Demand with AutoGluon
Using the AutoGluon framework, students train a baseline model to predict bike sharing demand. After running the initial training, students will modify and improve their model, then submit it to a public Kaggle competition.
Project 2: Build an ML Workflow on SageMaker
Develop a scalable, end-to-end ML Workflow using SageMaker, Lambda, and Step Functions.
Project 3: Image Classification using AWS SageMaker
Fine-tune a pre-trained model for image classification using AWS SageMaker.
Project 4: Operationalizing an AWS ML Project
Deploy a pre-built ML project for computer vision on AWS, adding additional features, including cost minimization, security, and redeployment.
Capstone Project: Inventory Monitoring at Distribution Centers
Build an ML model that counts the number of objects in an automated robotic distribution center. This project will incorporate all levels of learning from this Nanodegree program, including using AWS AgeMaker and other end-to-end ML best practices.
Learning from Top AWS Machine Learning Engineers
To develop this program’s world-class curriculum, we collaborated with professionals from top-rated tech companies like Intel, SOCi, Blue Hexagon, and Guidehouse. Each of these collaborators contributed guidance and feedback to focus the program on the most in-demand skills. Each of the instructors has extensive ML engineering and teaching experience.
Instructors
- Matt Maybeno, Principal Software Engineer in Data Science and ML at SOCi
- Joseph Nicolls, Senior ML Engineer at Blue Hexagon
- Charles Landau, Technical Lead in AI/ML at Guidehouse
- Soham Chatterjee, Software Innovator at Intel
- Bradford Tuckfield, ML Consultant
Enroll in the New and Improved AWS Machine Learning Engineer Nanodegree Program Today
If you’re someone who has programming experience and an interest in learning more about machine learning, this Nanodegree program is for you. With the AWS Machine Learning Engineer Nanodegree program, you’ll learn everything you need to become a top ML engineer, a highly coveted role in the technology field as ML engineers make an average base salary of over $140,000 a year.
Given Udacity’s hands-on project-centric learning, there’s no better way to meet the demand than by registering today for the AWS Machine Learning Engineer Nanodegree program.
