About This Course

Strengthen your Machine Learning skills and develop practical experience training, validating and evaluating models with Amazon Web Services


To share in LinkedIn

You can share your Certificates in the Certifications section of your LinkedIn profile, on your printed resume, or in other documents.



Learning Objectives

Test Python code and build a Python package of their own.
Build predictive models using a variety of unsupervised and supervised machine learning techniques.
Use Amazon SageMaker to deploy machine learning models to production environments, such as a
web application or piece of hardware..
A/B test two different deployed models and evaluate their performance.
Utilize an API to deploy a model to a website such that it responds to user input, dynamically.
Update a deployed model, in response to changes in the underlying data source.


  • Intermediate Python programming knowledge,
  • Intermediate knowledge of machine learning algorithms,

Target Audience

  • This program assumes that you are familiar with common supervised and unsupervised machine learning techniques. As such, it is geared towards people who are interested in building and deploying a machine learning product or application. Are you interested in deploying an application that is powered by machine learning? If so, then this program is right for you.


390 Lessons

Welcome to the Nanodegree program

Welcome to the Machine Learning Engineer Program & Projects
Program Structure
Skills that Set You Apart
Access the Career Portal
How Do I Find Time for My Nanodegree?

Introduction to Software Engineering

In this lesson, you’ll write production-level code and practice object-oriented programming, which you can integrate into machine learning projects.

Software Engineering Practices Pt I

Software Engineering Practices Pt II


Portfolio Exercise: Upload a Package to PyPi

Cloud Computing

Introduction to Deployment

Learn how to deploy machine learning models to a production environment using Amazon SageMaker.

Building a Model using SageMaker

Implement and use a mode

04. Hyperparameter Tuning

Updating a Model

Project: Deploying a Sentiment Analysis Model

Population Segmentation

Apply machine learning techniques to solve real-world tasks; explore data and deploy both built-in and custom-made Amazon SageMaker models.

Payment Fraud Detection

Interview Segment: SageMaker

Deploying Custom Models

Time-Series Forecasting


Introduction to NLP

Learn Natural Language Processing one of the fields with the most real applications of Deep Learning

Implementation of RNN _ LSTM

Sentiment Prediction RNN

Convolutional Neural Networks

Transfer Learning

Weight Initialization


Job Search

Find your dream job with continuous learning and constant effort

Refine Your Entry-Level Resume

Craft Your Cover Letter

Optimize Your GitHub Profile

Develop Your Personal Brand

390 lectures
English Español 中国人

Material Includes

  • Workspaces
  • Hands-on Projects
  • Quizzes
  • Progress Tracker

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