Learn foundational machine learning skills in the Intro to Machine Learning Nanodegree program and learn how to apply these skills to a variety of tasks. In the Machine Learning Engineer Nanodegree program, learn how to create a machine learning product and deploy machine learning models to a production environment, such as a web application. In this program you will master Supervised, Unsupervised, and Deep Learning fundamentals. You will also complete a capstone project in your chosen domain.
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.
Machine Learning in Production
Learn how to deploy machine learning models to a production environment using Amazon SageMaker.
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.
Machine Learning Capstone
In this capstone lesson, you’ll select a machine learning challenge and propose a possible solution.
Cezanne Camacho, Mat Leonard - Color, Luis Serrano, Dan Romuald Mbanga, Jennifer Staab, Sean Carrell, Josh Bernhard_color, Jay Alammar - nd892 and Andrew Paster
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This Machine Learning Nanodegree program is great.
This program is going quite well. It is clear how the coursework applies to actual job skills.
One thing that made this amazing is the different project we are working on it. The submit project process is very easy and the response to it is quick and clear.
The mentor point the things that was well done and on the one that was not well done, he/her will explain and guide how you can solve it.
until now I am satisfied with this nanodegree experience. The path is rich in information. I especially appreciate the opportunity to begin to know various aspects of the AWS cloud services, learning in the first place how to take care of costs, that any inexperienced people can face at the first approach. After completing this nanodegree, I am sure that I can be more confident in adopting AWS in my future work. Thanks
The program covers the engineering part quite well and gives student opportunity to practice skills needed for a machine learning engineer, e.g. deploy a model, create an API, build a web application. I appreciate the superb resources given exclusively by Udacity and would be excited to finish the project.
I'm guessing the best complementary or continuation course of Deep Learning Nanodegree (also from Udacity), and both taken together, I think would be the best introduction for a ML Eng career :).
One of the bests online, classes are greatly designed to make you learn in a simple, but growing dificulty, dealing with the real path to desing a machine learning pipeline
Good start for a machine learning course
Quality of the teaching videos is high and dialogue enjoyable. The project works are challenging and still developing as the course is new. The reviewers are professional and guide your work nicely. Some projects require a lot of work and maybe some improvement on the instructions. All in all I was very happy with this Nanodegree.
Content and Project Quality
I liked the projects ,and except for the content presentation for Reinforcement Learning most was presented extremely well and easily consumable. RL was slightly complicated to understand , and also I would have liked better mentor support on the Quad project.
Thank you Udacity
This was great experience and a lot of learning! Given constant support and follow up on project reviews along the course I felt sort of liability to perform well and excitement from learning my favorite subject. Consequtive topics selection and interesting midterm projects made it captivating! I mastered new techniques, got answers on my questions and leveled up with this course.
Good course for Machine Learning introduction
A complete, although somewhat superficial, course on machine learning fundamentals. It covers Supervised, Unsupervised and Reinforcement Learning. Now recently it also added Deep Learning. So it has all the fundamentals. The course content is goodl altough it could be better harmonized, because sometimes it feels (and it is) content from various courses all glued together, with hard transitions between content. All in all, it's a good degree.
Good all round support for students
ML Engineer - The course material is quite good with not too much focus on the mathematics. The project work helps one apply what was learned. Completing these projects increases the students confidence in being able to tackle new projects. The reviewers provide quick feedback with additional links that can help one improve their deliverable even further.
Perfect place to learn
In my practice-based opinion, Udacity is a perfect place to learn. They cover everything in great detail and in the simplest form possible. Perfect bang for the buck. :D