AWS Machine Learning Engineer Nanodegree

Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Overview of key background around Machine Learning and preparing you to be successful in the rest of this course.

Use AWS SageMaker Studio to access S3 datasets and perform data analysis, feature engineering with Data Wrangler and Pandas. And finally label new data using SageMaker Ground Truth.

In this lesson you'll learn about ML Lifecycles, how to differentiate between supervised vs. unsupervised ML, regression methods, and classification methods.

In this lesson you'll load a dataset, clean/create features, train a regression/classification model with scikit learn, evaluate a model and tune a model's hyperparameter.

In this lesson you'll train, test, and optimize on liner, tree-based, XGBoost, and AutoGluon Tabular models. And you will also create a model using SageMaker Jumpstart

This lesson gives an introduction to the course, including prerequisites, final project, stakeholders, and tools & environment.

This lesson will go over SageMaker essential services such as training jobs, endpoints, batch transforms, and processing jobs.

This lesson will discuss machine learning workflows and AWS tools such as Lambda, Step Function for building a workflow.

This lesson will go over monitoring a machine learning workflow and some useful services within AWS to help you monitoring the healthy of data and machine learning models.

In this lesson, we will give a background around Deep Learning for Computer Vision and NLP and preparing you to be successful in the rest of this course.

In this lesson, you will learn about neural networks, cost functions, optimization, and how to train a neural network.

In this lesson you will learn about Model Architectures, Convolutions, and Fine-tuning.

In this lesson, you will learn how to apply all you have learned about deep learning in AWS SageMaker.

In this introductory lesson, we will give you a course overview of topics and design. We will also introduce what exactly operationalizing machine learning means as well as how it applies.

This lesson is about managing computing resources effectively. We’ll talk about lowering costs and getting more with less.

This lesson is about training models on large datasets. We’ll talk about distributed models, distributed data, and some skills related to distributed training.

This lesson is about high throughput, low latency models. Essentially, this means that we’ll be talking about preparing your projects to deal with high traffic and minimal time delays.

Our final lesson is about security. Security is crucial for all major machine learning projects, so these skills can be very helpful in your career.

Put your Machine Learning Engineer skills to the test by solving a real-world problem using all that you have learned throughout the program.

Congratulations on your graduation from this program! Please join us in celebrating your accomplishments.