Real-world projects from industry experts
With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.
Intro to Machine Learning with TensorFlow
Nanodegree Program
Learn foundational machine learning techniques - from data manipulation to unsupervised and supervised algorithms.
04Days06Hrs56Min27Sec
Estimated time
3 months
At 10 hrs/week
Enroll by
September 28, 2022
Get access to classroom immediately on enrollment
Prerequisites
Intermediate Python
In collaboration with
What you will learn
Intro to Machine Learning with TensorFlow
3 months to completeLearn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning. At each step, get practical experience by applying your skills to code exercises and projects.
This program is intended for students with experience in Python, who have not yet studied Machine Learning topics.Prerequisite knowledge
Supervised Learning
In this lesson, you will learn about supervised learning, a common class of methods for model construction.
Deep Learning
In this lesson, you will learn the foundations of neural network design and training in TensorFlow.
Unsupervised Learning
In this lesson, you will learn to implement unsupervised learning methods for different kinds of problem domains.
All our programs include:
Technical mentor support
Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you, and keeping you on track.
Career services
You’ll have access to Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.
Flexible learning program
Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
Program offerings
Class Content
- Content co-created with Kaggle
- Real-world projects
- Project reviews
- Project feedback from experienced reviewers
Student services
- Technical mentor support
- Student community
Career services
- Github review
- Linkedin profile optimization
Succeed with personalized services.
We provide services customized for your needs at every step of your learning journey to ensure your success.
Get timely feedback on your projects.
- Personalized feedback
- Unlimited submissions and feedback loops
- Practical tips and industry best practices
- Additional suggested resources to improve
1,400+ project reviewers
2.7M projects reviewed
88/100 reviewer rating
1.1 hours avg project review turnaround time
Mentors available to answer your questions.
- Support for all your technical questions
- Questions answered quickly by our team of technical mentors
1,400+ technical mentors
0.85 hours median response time
Learn with the best.
Learn with the best.
Cezanne Camacho
Curriculum LeadCezanne 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
InstructorMat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.
Luis Serrano
InstructorLuis 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
InstructorDan 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
InstructorJennifer 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
InstructorSean 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
Data Scientist at Nerd WalletJosh 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
InstructorJay 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
InstructorAndrew 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.
Juan Delgado
Content DeveloperJuan is a computational physicist with a Masters in Astronomy. He is finishing his PhD in Biophysics. He previously worked at NASA developing space instruments and writing software to analyze large amounts of scientific data using machine learning techniques.
Michael Virgo
InstructorAfter beginning his career in business, Michael utilized Udacity Nanodegree programs to build his technical skills, eventually becoming a Self-Driving Car Engineer at Udacity before switching roles to work on curriculum development for a variety of AI and Autonomous Systems programs.
Intro to Machine Learning with TensorFlow
Get started today
Monthly access
Pay as you go
Enroll now
per/
/- Maximum flexibility to learn at your own pace.
- Cancel anytime.
- access
Pay upfront and save an extra 0%
Enroll now
for - access- Save an extra 0% vs. pay as you go.
- 3 months is the average time to complete this course.
- Switch to monthly price after if more time is needed.
- Cancel anytime.
Best Value
Learn
Build a strong foundation in Supervised, Unsupervised, and Deep Learning.
Average Time
On average, successful students take 3 months to complete this program.
Benefits include
- Real-world projects from industry experts
- Technical mentor support
- Career services
Program details
Program overview: Why should I take this program?
Why should I enroll?
What jobs will this program prepare me for?
How do I know if this program is right for me?
What is the difference between Intro to Machine Learning with PyTorch, and Intro to Machine Learning with TensorFlow Nanodegree programs?
Enrollment and admission
Do I need to apply? What are the admission criteria?
What are the prerequisites for enrollment?
If I do not meet the requirements to enroll, what should I do?
Do I have to take the Intro to Machine Learning Nanodegree program before enrolling in the Machine Learning Engineer Nanodegree program?
Tuition and term of program
How is this Nanodegree program structured?
How long is this Nanodegree program?
Can I switch my start date? Can I get a refund?
I have graduated from the Intro to Machine Learning Nanodegree program, but I want to keep learning. Where should I go from here?
Software and hardware: What do I need for this program?
What software and versions will I need in this program?