Welcome to the Nanodegree program

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

01. Introduction to Amazon SageMaker

Hi, I’m Sean, and together we will learn about Amazon’s SageMaker service.

To get started, we will need to configure a few things.

Note: Amazon is constantly iterating and improving the various services it offers. Sometimes this involves changes to the appearance of certain things, however the functionality must be the same. So in the screenshots and videos that follow, if your screen doesn’t look exactly the same, don’t worry!