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.
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
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 | 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.