[Udacity] Machine Learning Engineer Nanodegree v4.0.0

Nanodegree Program–nd009t

Become a Machine Learning Engineer

Learn advanced machine learning techniques and algorithms — including how to package and deploy your models to a production environment.
  • Estimated Time
    3 months

    At 10 hrs/week

Prerequisites
Intermediate Python & Machine Learning Algorithms

In collaboration with

  • Kaggle
AWS

What You Will Learn

SYLLABUS

Machine Learning Engineer

Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in industry.

This program is intended for students who already have knowledge of machine learning algorithms.

Learn advanced machine learning deployment techniques and software engineering best practices.

3 months to complete

Prerequisite Knowledge

To optimize your chances of success in this program, we recommend intermediate Python programming knowledge and intermediate knowledge of machine learning algorithms.

  • Software Engineering Fundamentals

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

    Build a Python Package

  • Machine Learning in Production

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

    Deploy a Sentiment Analysis Model

  • Machine Learning Case Studies

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

    Plagiarism Detector

  • Machine Learning Capstone

    In this capstone lesson, you’ll select a machine learning challenge and propose a possible solution.

    Capstone Proposal and Project

Learn with the best

Cezanne Camacho

Cezanne Camacho

Curriculum Lead

Cezanne is a machine learning educator with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she’s applied machine learning to medical diagnostic applications.

Mat Leonard

Mat Leonard

Instructor

Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.

Luis Serrano

Luis Serrano

Instructor

Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.

Dan Romuald Mbanga

Dan Romuald Mbanga

Instructor

Dan leads Amazon AI’s Business Development efforts for Machine Learning Services. Day to day, he works with customers—from startups to enterprises—to ensure they are successful at building and deploying models on Amazon SageMaker.

Jennifer Staab

Jennifer Staab

Instructor

Jennifer has a PhD in Computer Science and a Masters in Biostatistics; she was a professor at Florida Polytechnic University. She previously worked at RTI International and United Therapeutics as a statistician and computer scientist.

Sean Carrell

Sean Carrell

Instructor

Sean Carrell is a former research mathematician specializing in Algebraic Combinatorics. He completed his PhD and Postdoctoral Fellowship at the University of Waterloo, Canada.

Josh Bernhard

Josh Bernhard

Data Scientist at Nerd Wallet

Josh has been sharing his passion for data for nearly a decade at all levels of university, and as Lead Data Science Instructor at Galvanize. He’s used data science for work ranging from cancer research to process automation.

Jay Alammar

Jay Alammar

Instructor

Jay has a degree in computer science, loves visualizing machine learning concepts, and is the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.

Andrew Paster

Andrew Paster

Instructor

Andrew has an engineering degree from Yale, and has used his data science skills to build a jewelry business from the ground up. He has additionally created courses for Udacity’s Self-Driving Car Engineer Nanodegree program.

Get started with

Machine Learning Engineer

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Learn
Learn advanced machine learning techniques and algorithms, including deployment to a production environment.

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Average Time
On average, successful students take null months to complete this program.

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Benefits include
  • Real-world projects from industry experts
  • Technical mentor support
  • Personal career coach & career services

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STAY SHARP WHILE STAYING IN
  • Financial support available worldwide to help in this challenging time
  • Spend your time at home learning new, higher-paying job skills
  • Commit to a brighter future by learning today

Program Details

PROGRAM OVERVIEW – WHY SHOULD I TAKE THIS PROGRAM?
Why should I enroll?
As more and more companies are looking to build machine learning products, there is a growing demand for engineers who are able to deploy machine learning models to global audiences. In this program, you’ll learn how to create an end-to-end machine learning product. You’ll deploy machine learning models to a production environment, such as a web application, and evaluate and update that model according to performance metrics. This program is designed to give you the advanced skills you need to become a machine learning engineer.
Size: 2.90GB
Course Updated July 2020: https://www.udacity.com/course/machine-learning-engineer-nanodegree–nd009t
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