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 become machine learning (ML) subject matter experts within their organization. Progress through foundational, intermediate, and advanced courses to learn how machine learning frameworks and analysis tools can apply to your work and improve collaboration.

Learn more about the courses in each learning progression below.

  • Primary progression

    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

    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.

    Classroom | 1 day

    Types of Machine Learning Solutions

    Review the three different disciplines for machine learning: computer vision, natural language processing, and chat bots. Go through practical applications and the AWS services used in each. 

    Digital  |  15 minutes

  • Branching content areas

    Communicating with Chat Bots

    Learn how to build smart chat bots with the Communicating with Chat Bots curriculum. 

    Digital  |  3.5 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