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.

  • Follow 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.

    Digital | 8 hours

    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.

    Digital | 2.5 hours

    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.

    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.

    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.

    Classroom | 4 hours
    Digital | 2 hours

  • 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. 

    Digital | 2.5 hours

  • Optional training

    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.

    Digital | 8 hours

    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

  • AWS Certification

    AWS Certified Machine Learning - Specialty

    The AWS Certified Machine Learning - Specialty certification was created by AWS experts and validates in-demand skills required to build and tune data models. Differentiate yourself and your organization in this growing field.

    Exam | 180 minutes