Become a Machine Learning Engineer

424 reviews
Learning paid
Certification free
3 months
About the course
Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in industry.
This program is intended for students who already have knowledge of machine learning algorithms.
Software Engineering Fundamentals
In this lesson, you’ll write production-level code and practice object-oriented programming, which you can integrate into machine learning projects.
Machine Learning in Production
Learn how to deploy machine learning models to a production environment using Amazon SageMaker.
Machine Learning Case Studies
Apply machine learning techniques to solve real-world tasks; explore data and deploy both built-in and custom-made Amazon SageMaker models.
Machine Learning Capstone
In this capstone lesson, you’ll select a machine learning challenge and propose a possible solution.
  • At least 40hrs of programming experience
  • Familiarity with data structures like dictionaries and lists
  • Experience with libraries like NumPy and pandas
  • Supervised learning models, such as linear regression
  • Unsupervised models, such as k-means clustering
  • Deep learning models, such as neural networks
Cezanne Camacho
Curriculum Lead
Cezanne is a machine learning educator with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she’s applied machine learning to medical diagnostic applications.
Mat Leonard
Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.
Luis Serrano
Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.
Dan Romuald Mbanga
Dan leads Amazon AI’s Business Development efforts for Machine Learning Services. Day to day, he works with customers—from startups to enterprises—to ensure they are successful at building and deploying models on Amazon SageMaker.
Jennifer Staab
Jennifer has a PhD in Computer Science and a Masters in Biostatistics; she was a professor at Florida Polytechnic University. She previously worked at RTI International and United Therapeutics as a statistician and computer scientist.
Sean Carrell
Sean Carrell is a former research mathematician specializing in Algebraic Combinatorics. He completed his PhD and Postdoctoral Fellowship at the University of Waterloo, Canada.
Josh Bernhard
Data Scientist at Nerd Wallet
Josh has been sharing his passion for data for nearly a decade at all levels of university, and as Lead Data Science Instructor at Galvanize. He's used data science for work ranging from cancer research to process automation.
Jay Alammar
Jay has a degree in computer science, loves visualizing machine learning concepts, and is the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.
Andrew Paster
Andrew has an engineering degree from Yale, and has used his data science skills to build a jewelry business from the ground up. He has additionally created courses for Udacity’s Self-Driving Car Engineer Nanodegree program.
This platform focuses on technical and business skills. You pay for monthly access to materials for a chosen course. For the majority of classes, trainees apply their knowledge to real cases, are advised by a career coach and get help in the recruitment process.
Become a Machine Learning Engineer