Machine Learning Engineer
In this program you will master Supervised, Unsupervised, Reinforcement, and Deep Learning fundamentals. You will also complete a capstone project in your chosen domain.
Study 5-10 hrs / week
CO – CREATED WITH
- amazon webservices
Become career-ready faster
INDUSTRY SIZE & DEMAND
Machine Learning Market will be worth 8.81 Billion USD by 2022; growing at a CAGR of 44%.
Machine Learning Engineer is one of the most in-demand jobs in the industry
RANKED #08 CNBC
Udacity ranked as the most disruptive learning company in the world for 2 years in a row by CNBC
Join a global community of over 50,000 ML Engineers who have learned with Udacity
Our Hiring Partners for Machine Learning
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 (listed in our FAQ at the bottom of this page). Including:
- Populations, samples
- Mean, median, mode
- Standard error
- Variation, standard deviations
- Normal distribution
- Precision and accuracy
- Hypothesis testing
- Problem solving
- Confidence Interval, P-values, T-test, Statistical Significance
Intermediate calculus and linear algebra mastery, addressed in the Linear Algebra Refresher Course, including:
- Series expansions
- Matrix operations through eigenvectors and eigenvalues
WHAT YOU LEARN
Study cutting edge Content
Term 1 : Machine Learning – Basics
Term fee includes
Best in-class content by industry leaders in the form of bite-size videos and quizzes.
Machine Learning Foundations
Explore the core concepts of Machine Learning which involve understanding the nuances in your data.
Now that you have a background in model building, you will learn about supervised learning, a common class of methods for model construction.
In this lesson, we will cover unsupervised learning and how it is suitable for different kinds of problem domains.
Industry relevant projects + unlimited project reviews by our global reviewers
Predicting Boston Housing Prices
Find Donors for CharityML
Creating Customer Segments
We guide and support you throughout your learning journey through these services.
Search-based Q&A forum
Collaborate with Fellow Students
Project reviews & feedback
Receive actionable feedback from expert project reviewers until you get your code right!
Your Nanodegree journey
ENROLL IN TERM 1
enroll by 23 Jan 2019
BRUSH UP ON PRE-REQUISITES
while you wait for classroom to open, brush up on pre-requisites
classroom will open on 23 Jan 2019In case you feel unsure about the program, we offer a full refund on cancelling within 7 days of classroom opening.
submit all projects within 3 months
COMPLETE TERM 1
finish requirements for graduation
ENROLL FOR TERM 2
you will now be prepared to enroll for Term 2
Learn from top Industry Experts
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 is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.
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 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 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.