AWS Machine Learning Engineering Training Course | Udacity

AWS Machine Learning Engineer Nanodegree

Refine your machine learning skills with this AWS Machine Learning Engineer Nanodegree. Learn to deploy models on SageMaker and design automated workflows with AWS Lambda and Step Functions.

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1. An Introduction to Your Nanodegree Program

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

2. Getting Help

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

1. Introduction to Machine Learning

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

2. Exploratory Data Analysis

   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.

3. Machine Learning Concepts

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

4. Model Deployment Workflow

   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.

5. Algorithms and Tools

   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

6. Predict Bike Sharing Demand with AutoGluon

   Train a model using AutoGluon to predict bike sharing demand, and see how highly you can place in the competition!

1. Introduction to Developing ML Workflows

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

2. SageMaker Essentials

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

3. Designing Your First Workflow

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

4. Monitoring a ML 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.

5. Project: Build a ML Workflow For Scones Unlimited On Amazon SageMaker

   In the project, you will build and ship an image classification model with AWS SageMaker for Scones Unlimited, a scone-delivery-focused logistic company.

1. Introduction to Deep Learning Topics within Computer Vision and NLP

   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.

2. Introduction to Deep Learning

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

3. Common Model Architecture Types and Fine-Tuning

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

4. Deploy Deep Learning Models on SageMaker

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

5. Image Classification using AWS SageMaker

   In this project, you will use AWS SageMaker to finetune a pretrained model and perform a image classification using profiling, debugging, and hyperparameter tuning.

Machine Learning Engineer

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