Lesson List

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

OOP

Portfolio Exercise: Upload a Package to PyPi

Part 15-Module 01-Lesson 06_Web Development

Portfolio Exercise: Deploy a Data Dashboard

Introduction to Data Engineering

ETL Pipelines

Introduction to NLP

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

Machine Learning Pipelines

Disaster Response Pipeline

Project1: Disaster Response Pipeline

Part 17-Module 02-Lesson 01_Concepts in Experiment Design

Part 17-Module 02-Lesson 02_Statistical Considerations in Testing

Statistical Considerations in Testing

Part 17-Module 02-Lesson 03_AB Testing Case Study

A/B Testing Case Study

Part 17-Module 02-Lesson 04_Portfolio Exercise Starbucks

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations

Part 17-Module 04-Lesson 01_Recommendation Engines

Part 17-Module 05-Lesson 01_Upcoming Lesson

Sentiment Prediction RNN

Convolutional Neural Networks

Transfer Learning

Weight Initialization

Autoencoders

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

Go to course home
Welcome to the Machine Learning Engineer Program & Projects

Welcome to the Data Scientist Nanodegree program!

We’re excited that you’ve decided to start on in your journey to becoming a Machine Learning Engineer. Next, let’s talk about what you can expect to learn in this program.

Projects

This Nanodegree program includes four projects, which you can learn more about below! Completing the projects will not only help you build your skills with topics like software engineering and machine learning model deployment, but also show you how those skills are used in practice and build out your technical portfolio.

Don’t worry if you are not familiar with how you would even approach some of the items discussed below. You will be learning the needed skills in the lessons ahead!

Project 1: Deploying a Sentiment Analysis Model

You have experience building and training machine learning models, and in this first project, you’ll learn how to deploy a model to a production environment. Using Amazon SageMaker, you’ll deploy your own PyTorch sentiment analysis model, which is trained to recognize the sentiment of movie reviews (positive or negative). Deployment gives you the ability to use a trained model to analyze new, user input. Once you deploy a trained model, you can create a gateway for accessing it from a website.

Project 2: Deploying a Plagiarism Detector

In this project, you’ll complete a machine learning workflow, going from analyzing and exploring a corpus of text data, to extracting features that may be used to indicate plagiarism between a source and answer text. Finally, you’ll upload transformed data into a SageMaker notebook instance and train and deploy a custom model for plagiarism classification! This project tests your ability to do feature engineering and model deployment.

Project 4: Capstone Project

The final project is open for you to do any project of your choosing, to add to your portfolio. One of the projects you may choose to do is a second project with the messy Arvato data. Alternatively, you might choose another project using Deep Learning to classify dog breeds. Here is a glimpse of the final project.

Arvato Final Project

 

 

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