Machine Learning: Data Scientist
Dig deep into the math, science, and statistics behind machine learning
This path is designed for learners skilled in math, statistics, and analysis who want to become machine learning (ML) subject matter experts within their organization. Learn how machine learning frameworks and analysis tools can improve workplace collaboration. Then supplement your skills with optional training.
Learn more about the courses in each learning progression below.
Recommended progressionFollow this recommended sequence of courses and exams to build your AWS Cloud skills in this Learning Path.
The Elements of Data Science
Learn to build and continuously improve machine learning models by covering problem formulation, exploratory data analysis, feature engineering, model training, tuning and debugging, as well as model evaluation and productionizing.
Data Science Capstone: Real World Machine Learning Decisions
Use machine learning to solve a real-life business challenge. Build, train, and test a machine learning model from the ground up.
Digital | 50 minutes
Machine Learning Data Readiness
This course focuses on the concept of data readiness in the context of machine learning (ML). You will learn how to determine data readiness and identify when to employ data readiness as part of your ML process.
Digital | 1 hour
Developing Machine Learning Applications
Explore Amazon’s fully managed ML platform, Amazon SageMaker.
Practical Data Science with Amazon SageMaker
Explore real-world use cases with Machine Learning (ML) and using Amazon SageMaker in the new 1-day classroom training course.
Classroom (virtual or in person) |
The Machine Learning Pipeline on AWS
Explore how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. You will learn about each phase of the pipeline from presentations and demonstrations by AWS instructors. You will then apply that knowledge to complete a project solving one of three business problems. By the end of the course, you will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves your selected business problem.
Classroom (virtual or in person) |
Machine Learning Security
Secure your applications and environments with specific topics detailing NACLs, security groups, AWS identity and access management, and encryption key management.
Digital | 30 minutes
Exam Readiness: AWS Certified Machine Learning – Specialty
Explore the AWS Certified Machine Learning - Specialty exam’s topic areas, see how they relate to machine learning on AWS, and map them to machine learning (ML) and deep learning (DL) foundational areas for future self-study.
Related ML specialty courses
Speaking of: Machine Translation and NLP
These courses explore how machines interact with the human language. Review AWS services that help you with neural networks and natural language processing topics like automatic speech recognition, natural and fluent language translation, and insights and relationships in text.
Digital | 80 minutes
Seeing Clearly: Computer Vision Theory
This curriculum explores how machines achieve understanding of images and videos.
Math for Machine Learning
To understand modern machine learning, you also need to understand vectors and matrices, linear algebra, probability theorems, univariate calculus, and multivariate calculus. This course covers it all.
Linear and Logistic Regression
Explore models for regression, least squares error, maximum likelihood estimate, regularization, logistic regression, empirical loss minimization, and gradient-based optimization methods.
Digital | 8.5 hours