Machine Learning Engineer Nanodegree
Introduction to Machine Learning
In this course, you'll start learning what machine learning is by being introduced to the high level concepts through AWS SageMaker. You'll begin by using SageMaker Studio to perform exploratory data analysis. Know how and when to apply the basic concepts of machine learning to real world scenarios. Create machine learning workflows, starting with data cleaning and feature engineering, to evaluation and hyperparameter tuning. Finally, you'll build new ML workflows with highly sophisticated models such as XGBoost and AutoGluon.
Project: Predict Bike Sharing Demand with AutoGluon
Source code: vnk8071/predict_bike_sharing_demand
Developing Your First ML Workflow
This course discusses how to use AWS services to train a model, deploy a model, and how to use AWS Lambda Functions, Step Functions to compose your model and services into an event-driven application.
Project: Build a ML Workflow For Scones Unlimited On Amazon SageMaker
Source code: vnk8071/ml_workflow_for_scones_unlimited
Deep Learning Topics within Computer Vision & NLP
Project: Image Classification using AWS SageMaker
Source code: vnk8071/image_classification_using_sagemaker
Operationalizing Machine Learning on SageMaker
This course covers advanced topics related to deploying professional machine learning projects on SageMaker. Students will learn how to maximize output while decreasing costs. They will also learn how to deploy projects that can handle high traffic, how to work with especially large datasets, and how to approach security in machine learning AWS applications.