Learn what machine learning is and the steps involved in building and evaluating models. Gain in demand skills needed at businesses working to solve challenges with AI. Learn the fundamentals of advanced machine learning areas such as computer vision, reinforcement learning, and generative AI. Get hands-on with machine learning using AWS AI Devices (i.e. AWS DeepRacer and AWS DeepComposer). Learn how to prepare, build, train, and deploy high-quality machine learning (ML) models quickly with Amazon SageMaker and learn object-oriented programming best practices.
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Machine learning is expected to transform virtually every industry and customer experience we know today. However, there is a shortage of trained and experienced ML developers. Of 23 million developers worldwide, only 1.3% (300,000) have AI/ML expertise, and by 2022 it is predicted there will be 58 million AI/ML jobs, further deepening this talent shortage.
Upon completion of the course, learners will have a strong foundation in object-oriented programming and an introduction to key AWS machine learning technologies, which is a great start on the path towards becoming a Machine Learning Engineer.
What you will learn
Welcome to the AWS Machine Learning Foundations Course
- Meet your instructors
- What you will learn
- Pre-requisites
Introduction to Machine Learning
- Differentiate between supervised and unsupervised learning
- Identify problems that can be solved with machine learning
- Describe commonly used algorithms including linear regression, logistic regression, and k-means
- Describe how model training and testing works
- Evaluate the performance of a machine learning model using metrics
Machine Learning with AWS
- Identify AWS machine learning offerings and understand how different services are used for different applications
- Explain the fundamentals of computer vision and provide examples of popular tasks
- Describe how reinforcement learning works in the context of AWS DeepRacer
- Explain the fundamentals of generative AI and its applications, and describe three famous generative AI models in the context of music and AWS DeepComposer
Software Engineering Practices, Part 1
- Writing clean and modular code
- Writing efficient code
- Code refactoring
- Adding meaningful documentation
- Using version control
Software Engineering Practices, Part 2
- Testing
- Logging
- Code reviews
Introduction to Object-Oriented Programming
- Object-oriented programming syntax
- Using object-oriented programming to make a Python package
Prerequisites and requirements
All learners are welcome to take the foundations course, but familiarity with basic mathematical concepts such as calculation, average, variance, and beginning level programming (preferably Python) is recommended to fully engage in all of the coursework. If you want to brush up on your Python skills, we encourage you to review our free Introduction to Python course.
We encourage you to dive deeper in to machine learning with our Intro to Machine Learning and Intro to Deep Learning with PyTorch courses.
You may also find our Version Control with Git course helpful. It is also offered for free.
