Prerequisites:
No experience required
Machine Learning Engineer Nanodegree
Nanodegree Program
This course concentrates on training the learner to become a machine learning engineer and apply predictive models to massive data sets in fields like education, finance, healthcare, or robotics.
This course concentrates on training the learner to become a machine learning engineer and apply predictive models to massive data sets in fields like education, finance, healthcare, or robotics.
Intermediate
4 months
Last Updated April 30, 2024
Intermediate
4 months
Last Updated April 30, 2024
Prerequisites:
No experience required
Courses In This Program
Course 1 • 4 weeks
Introduction to Machine Learning
In this Nanodegree, 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.
Lesson 1
Introduction to Machine Learning
Overview of key background around Machine Learning and preparing you to be successful in the rest of this course.
Lesson 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.
Lesson 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.
Lesson 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.
Lesson 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
Lesson 6 • Project
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!
Course 2 • 4 weeks
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.
Lesson 1
Introduction to Developing ML Workflows
This lesson gives an introduction to the course, including prerequisites, final project, stakeholders, and tools & environment.
Lesson 2
SageMaker Essentials
This lesson will go over SageMaker essential services such as training jobs, endpoints, batch transforms, and processing jobs.
Lesson 3
Designing Your First Workflow
This lesson will discuss machine learning workflows and AWS tools such as Lambda, Step Function for building a workflow.
Lesson 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.
Lesson 5 • Project
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.
Course 3 • 2 weeks
Deep Learning Topics within Computer Vision & NLP
Lesson 1
Introduction to Deep Learning Topics within Computer Vision & NLP
Lesson 2
Introduction to Deep Learning
Lesson 3
Common Model Architecture Types and Fine-Tuning
Lesson 4
Deploy Deep Learning Models on SageMaker
Lesson 5 • Project
Image Classification using AWS SageMaker
Course 4 • 3 weeks
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.
Lesson 1
Introduction to Operationalizing Machine Learning on 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.
Lesson 2
Manage compute resources in AWS accounts to ensure efficient utilization
This lesson is about managing computing resources effectively. We’ll talk about lowering costs and getting more with less.
Lesson 3
Train models on large-scale datasets using distributed training
This lesson is about training models on large datasets. We’ll talk about distributed models, distributed data, and some skills related to distributed training.
Lesson 4
Construct pipelines for high throughput, low latency models
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.
Lesson 5
Design Secure Machine Learning Projects in AWS
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.
Lesson 6 • Project
Operationalizing an AWS ML Project
Your goal in this project will be to use several important tools and features of AWS to adjust, improve, configure, and prepare the model you started with for production-grade deployment.
Ratings & Reviews
Average Rating: 4.5 Stars
516 Reviews
Pedro T.
June 19, 2022
A complete course, liked it so much!
Atamert A.
June 18, 2022
all good
Mateus F.
March 23, 2022
The program is really complete. It teaches everything on the machine learning workflow.
Osama H.
March 20, 2022
Great
Rodrigo T.
February 17, 2022
This forst project was really intersting and I had to study a lot to deliver it. Also the feedbacks from the Udacity's staff was amazing. Thank you very much
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