1 - Day 1
Module 0: IntroductionPre-assessmentModule 1: Introduction to Machine Learning and the ML PipelineOverview of machine learning, including use cases, types of machine learning, and key conceptsOverview of the ML pipelineIntroduction to course projects
2 - Day 2
Module 4: PreprocessingOverview of data collection and integration, and techniques for data preprocessing and visualizationPractice preprocessingPreprocess project dataClass discussion about projects
3 - Day 3
Module 5: Model TrainingChoosing the right algorithmFormatting and splitting your data for trainingLoss functions and gradient descent for improving your modelDemo: Create a training job in Amazon SageMakerModule 6: Model EvaluationHow to evaluate c
4 - Day 4
Checkpoint 3 and Answer ReviewModule 7: Feature Engineering and Model TuningFeature extraction, selection, creation, and transformationHyperparameter tuningDemo: SageMaker hyperparameter optimizationPractice feature engineering and model tuningApply
Actual course outline may vary depending on offering center. Contact your sales representative for more information.
Who is it For?
This course is intended for:
Researchers and IT Professionals interested in an introduction to machine learning using Python and Amazon SageMaker
We recommend that attendees of this course have:
Basic knowledge of Python programming language
Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
Basic experience working in a Jupyter notebook environment