Nanodegree Program
Become a Machine Learning Engineer
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Classroom Opens May 15, 2018
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Time
1 Term — 6 months
Study 10 hrs/week and complete in 6 mo.
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Classroom Opens
May 15, 2018
Classroom opens in 20 days.
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Student Rating
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Estimated Salary
Based on US job data
Prerequisites and Requirements
Intermediate Python programming knowledge, of the sort gained through the Introduction to Programming Nanodegree, other introductory programming courses or programs, or additional real-world software development experience. Including:
- Strings, numbers, and variables
- Statements, operators, and expressions
- Lists, tuples, and dictionaries
- Conditions, loops
- Procedures, objects, modules, and libraries
- Troubleshooting and debugging
- Research & documentation
- Problem solving
- Algorithms and data structures
Intermediate statistical knowledge, of the sort gained through any of Udacity’s introductory statistics courses. Including:
- Populations, samples
- Mean, median, mode
- Standard error
- Variation, standard deviations
- Normal distribution
- Precision and accuracy
- Hypothesis testing
- Confidence Interval, P-values, T-test, Statistical Significance
Intermediate calculus and linear algebra mastery, addressed in the Linear Algebra Refresher Course, including:
- Derivatives
- Integrals
- Series expansions
- Matrix operations through eigenvectors and eigenvalues
In Collaboration With
Why Take This Nanodegree Program?
In this program, you’ll master valuable machine learning skills that are in demand across countless industries. Investment levels in this space continue to rise, thousands of highly-valued startups have entered the field, and demand for machine learning talent shows no signs of leveling. Program graduates emerge uniquely prepared to excel in machine learning roles.
Effective and Engaging Content
Get started learning Machine Learning through interactive content like quizzes, videos, and hands-on programs. Our learn-by-doing approach is the most effective way to learn Machine Learning skills.
Beneficial and Supportive Project Review
Advance quickly and successfully through the curriculum with the support of expert reviewers whose detailed feedback will ensure you master all the right skills.
AWS Credits to Deploy Your Models
Get free access to Amazon Web Services - the same platform used by Machine Learning Engineers around the globe - to build and deploy your models.
Practical Career Support
Receive personalized feedback from our expert Careers Team, to help you perfect your resume, refine your LinkedIn profile, and prepare for a Machine Learning interview.
Learn with the Best
Arpan Chakraborty
Instructor
Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.
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
Curriculum Lead
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.
Alexis Cook
Instructor
Alexis is an applied mathematician with a Masters in computer science from Brown University and a Masters in applied mathematics from the University of Michigan. She was formerly a National Science Foundation Graduate Research Fellow.
Jay Alammar
Instructor
Jay is a software engineer, the founder of Qaym (an Arabic-language review site), and the Investment Principal at the Riyad Taqnia Fund, a $120 million venture capital fund focused on high-technology startups.
Sebastian Thrun
Instructor
As the founder and president of Udacity, Sebastian’s mission is to democratize education. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass and more.
Ortal Arel
Instructor
Ortal Arel is a former computer engineering professor. She holds a PhD in Computer Engineering from the University of Tennessee. Her doctoral research work was in the area of applied cryptography.
What You Will Learn
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Syllabus
Machine Learning
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Prerequisite Knowledge
To succeed in this program, you should have experience programing in Python, and knowledge of inferential statistics, probability, linear algebra, and calculus. See detailed requirements.
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Machine Learning Foundations
Explore the core concepts of Machine Learning which involve understanding the nuances in your data.
Predicting Boston Housing Prices
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Supervised Learning
Now that you have a background in model building, you will learn about supervised learning, a common class of methods for model construction.
Find Donors for CharityML
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Unsupervised Learning
In this lesson, we will cover unsupervised learning and how it is suitable for different kinds of problem domains.
Creating Customer Segments
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Deep Learning
In this lesson, we’ll cover topics in Deep Learning including Convolutional Neural Networks.
Dog Breed Classifier
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Reinforcement Learning
In this lesson, we'll cover topics in Reinforcement Learning like Markov Decision Processes, Monte Carlo methods and Temporal Difference methods.
Train a quadcopter how to fly
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Capstone Project
This section has two phases. The first is the Capstone Proposal, during which you will draft a proposal outlining the domain of the problem you would like to solve, and your approach. This is followed by the Capstone Project: Here, you will leverage your newly-learned skills to solve the problem—as outlined in your proposal—by applying machine learning algorithms and techniques.
Capstone Proposal
Capstone Project
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Full Program
Machine Learning Engineer
total
This program will teach you how to become a Machine Learning Engineer, and apply predictive models to massive data sets in fields like finance, healthcare, education, and more.
FAQ – Machine Learning Engineer
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Why should I enroll in the Machine Learning Nanodegree Program?
Machine learning is everywhere, and is often at work even when we don't realize it. Google Translate, Siri, and Facebook News Feeds are just a few popular examples of machine learning's omnipresence. The ability to develop machines and systems that can automatically improve themselves puts machine learning at the absolute forefront of virtually any field that relies on data. If you are interested in the field of Machine Learning, and want to get hands on experience building models to topical datasets, so that you can join the pioneers who lead this field in the industry today, this program is ideal. This program is also excellent for Data Analysts who want to move into a more machine learning centric role because this program focuses specifically on building real world skills that you will be able to apply to your Machine Learning Engineer job. The goal of the Machine Learning Nanodegree program is to equip you with key skills that will prepare you to fill roles within companies seeking machine learning experts as well as those looking to introduce machine learning techniques to their organizations.
Previous
Project
Predicting Boston Housing Prices
In this project your task is to build an optimal machine learning model to estimate the best selling price for your client’s home in the Boston metropolitan area based on a statistical analysis of the historical data that is available to you.
Project
Find Donors for CharityML
CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. Your goal will be to evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent to ask for donations.
Project
Creating Customer Segments
Your task in this project is to use unsupervised learning techniques to see if any similarities exist between customers of a fictitious wholesale retailer, and how to best segment customers into distinct categories using various clustering techniques in order to help the retailer make more informed business decisions.
Project
Dog Breed Classifier
In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed.
Project
Capstone Proposal
Project
Capstone Project
Once your Capstone Proposal is approved, you will leverage your newly-learned skills to solve the problem—as identified in your proposal—by applying machine learning algorithms and techniques.
Project
Train a quadcopter how to fly
In this project, you will design an agent that can fly a quadcopter, and then train it using a reinforcement learning algorithm of your choice! Try to apply the techniques you have learnt in this module to find out what works best, but also feel free to come up with innovative ideas and test them.
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