Udacity Machine Learning Nanodegree Review 2024: My Thoughts on Machine Learning from Udacity

Why I enrolled in Udacity’s Machine Learning Nanodegree?

I have been working as a software engineer for over thirteen years in different companies and different levels. I’m working at Microsoft as a software engineer currently.

Udacity’s Nanodegree Machine Learning Engineer Nanodegree was a program that I had been thinking of attending for a long time. I believe having AI/ML skills will be very critical for the next several years since it has started to shape the world. One of the reasons I preferred Udacity was it has a proven experience for many years.

Today I am going to take you through the entire Udacity machine learning Nanodegree review, right from the course syllabus to the reviews of earlier machine learning Nanodegree graduates.

So keep reading. 

Claim your discount on Udacity >>

If you buy the course through the links in this article, we can earn some affiliate commission.  This helps us to keep OnlineCourseing up and running for your benefit.

This course is intended for students with knowledge of Python and ML algorithms, hence, if you are new to this, I suggest looking for intro courses.

1. Intro to Machine Learning with PyTorch
2. Intro to Machine Learning with TensorFlow

For this article, I have to spend a dozen hours to identify whether the course is truly recommendable to you or not.

1. How many have benefited from this course?
2. How many have landed a successful job after completing this course?

Hence, I decided to write this complete analysis of Udacity Machine learning Nanodegree.

My end goal was to identify whether the Udacity Machine Learning Nanodegree was worth it.

Like several machine learning courses, this one also requires some prerequisites, like python and basic machine learning.

Out of all the reviews that I read over social media, one thing all the Nanodegree graduates praised was Udacity’s project review system and career support for this course.

One of the earlier machine learning Nanodegree graduates, Balaji M.J. who has taken both Udacity Machine Learning Nanodegree and Coursera’s machine learning specialization writes about Udacity on Quora.

Here’s the screenshot of it

Many people ask me this question, how is Udacity machine learning Nanodegree compared to that of Coursera’s machine learning specialization?

According to Logan Spears who is a graduate of both, says that it’s good to take both courses, starting with Coursera to build a strong conceptual foundation and further refine skills by doing practical projects at Udacity.

In case you have limited time and money he suggests going with Udacity. 

Here are his both certificates

Udacity Machine Learning Engineer Nanodegree

Coursera Machine Learning Specialization

Let’s dig deep into Udacity machine learning Nanodegree review

Udacity’s Machine Learning Engineer Nanodegree program is a reliable alternative to the one from Coursera.

The course will teach a plethora of ML techniques that might help you to complete real-world projects as a machine learning engineer.

Many graduates are satisfied with the quality of projects in the program that they even forked their repos on GitHub and left solutions up as portfolio items.

The last step in completing this program is to do the capstone project. You are supposed to choose this capstone project on your own.

We shall discuss the entire syllabus in the coming section.

According to some, the greatest motivator is the desire to complete the Nanodegree such that you might end up putting a lot of effort than normal. 

An ex-ML Nanodegree graduate says,

“I ended up creating something of which I am truly proud. Udacity’s program doesn’t so much teach as it does provide a framework and motivation for you to teach yourself.”

Now we shall have a detailed talk on the Programming Environment.

Udacity teaches you ML in a modern Python environment with frameworks like Sklearn, TensorFlow, and Keras.

Also, you are taught to use AWS in deploying machine learning software to the cloud.

This is not the case with Coursera’s specialization. ML on Coursera is taught in the “OG” 3D math language and Matlab.

As Matlab is a costly affair, machine learning is mostly done on Python today.

Because of this reason, many prefer Udacity as it provides industry best practices for students to get a job-ready profile.

Now the next section covers the pricing and duration of the Nanodegree

I hope you are enjoying this Udacity Machine Learning  Nanodegree Review.

Also Read: My review of Udacity’s Deep Learning Nanodegree

Udacity Machine Learning Nanodegree Price

With the given amount of offerings from Udacity in terms of content and dedicated support, the Machine Learning Engineer Nanodegree comes with a flat fee of $399.

You are required to complete this in a period of 3 months, expecting that you will learn at least 10 hours per week.

Cost: $399

Duration: 3 months

You can also pay monthly at a cost of $199/month.

I paid $270 with a 75% discount. I believe it’s overpriced without a discount code, I would advise everyone to wait for the best price.

In my opinion, given the quality of instructor feedback, such a high price also seems reasonable as there are highly educated mentors who meticulously review your projects.

It’s self-paced training, so you can decide how or when to work. It took 3 months to complete for me, but I believe it’s also possible to complete it below 2 months if you have time.

In case you have made up your mind to enroll, you can sign up here

Who are the instructors?

Udacity has the finest, knowledgeable instructors that can guide you through all the complex concepts of machine learning.

Here’s the list of them,

1. Cezanne Camacho-Master’s in Electrical Engineer at Stanford
2. Mat Leonard– Physicist, neuroscientist, and data scientist from the University of Berkeley
3. Luis Serrano– Machine Learning Engineer at Google
4. Dan Romuald Mbanga-Lead at Amazon’s AI business development
5. Jennifer Staab-Statistician and computer scientist with RTI
6. Sean Carrell-University of Waterloo, Canada
7. Josh Bernhard– Data Scientist at Nerd Wallet
8. Jay Alammar-Investment Principle at STV
9. Andrew Paster– Engineer from Yale

Getting taught by such experienced instructors is exciting.
Isn’t it?
Now let’s shade some light on the prerequisites of the course.

Also Read: Udacity data scientist Nanodegree review

Prerequisites of the Machine Learning Nanodegree Program

In order to squeeze more out of this program, you should have the knowledge of the following topics

One should be an intermediate Python programmer who has at least 40 hours of programming experience in Python. If you are new to Python, I recommend you to take an introductory python course on Udacity.

Along with Python programming, you should be familiar with data structures like dictionaries and lists which are integral parts of Python itself and should have hands-on experience with libraries like NumPy and pandas.

Apart from Python, you are required to have an intermediate understanding of machine learning algorithms, which includes Supervised learning models and Unsupervised models.

In short, this program is intended for students who are already familiar with machine learning algorithms.

The best part about the program I found is that while going through the program, if you have questions about anything, you can directly reach the support team at machine-support@udacity.com. 

That’s all about the prerequisites, let’s have a look at the course syllabus

Check this out -> Udacity Artificial Intelligence(AI) Nanodegree Review

Syllabus of Machine Learning Engineer Nanodegree Program

When I think about the course syllabus, all I can say is that preparing training content from the basics to advanced machine learning techniques is a tough job. I think Udacity is doing well here. I was happy with the content during the training.

The entire Nanodegree program is divided into 4 courses, each followed by respective projects.

All the projects are meticulously crafted to showcase your skills and build a career portfolio that defines your ability to write complex machine learning algorithms and model deployment.

Let’s have a closer look at each course.

The first course is Software Engineering Fundamentals

Having some software engineering skills such as object-oriented programming, clean and modular code and code documentation are the main topics in this section. Python probably is the best programming language for AI and ML. I learned and understood Python more in this lesson.

Project 1: Build a Python Package 

This was a simple Python task to learn how to build your own Python package.

Here, you will have to write production-level code and practice object-oriented programming that can help you to build machine learning projects.

This project will demonstrate your skills in Object-oriented programming, Clean and modular code and Code documentation

The second lesson is about Machine learning in production.

In this lesson, you will use Amazon SageMaker to deploy machine learning models to a production environment

The purpose of this lesson is to teach you how to predict the sentiment of a user using Amazon SageMaker(User who provides a movie review. You need to predict whether the movie review is good or bad).

Also, you will be allowed to create a simple web app that can accept input from a user.

Project 2: Neural Networks

In this project, I created a neural network for the purpose of determining the sentiment of a movie review using the IMDB data set using Amazon SageMaker.

I enjoyed deploying a model since I could find a chance to test and apply what I learned in this section. It was nice to see the results of my works on the cloud.

The next is a Machine Learning case study

In this section, I learned how to use and deploy unsupervised algorithms using Amazon SageMaker with real-world examples. Deploying custom models was another important topic.

Project 3: Plagiarism Detector

In this project, I built a plagiarism detector that examines a text file and performs binary classification; labeling that file as either plagiarized or not, depending on how similar that text file is to a provided source text.

Capstone Project

In the capstone project, I leveraged what I learned throughout the training. There were four different machine learning engineer project options to choose: customer segmentation report for Arvato Financial Services, optimizing app offers with Starbucks, using CNN to identify dog breeds and any project you want to build.

I preferred dog breed classification using CNN since I found it more interesting compared to other projects. In the capstone project, I tried to determine the breed of a dog using an image, if an image of a human is provided instead, our algorithm identifies the resembling dog breed.

Udacity Machine Learning Nanodegree Reviews

Great Learnings

★★★★★

“Challenging but very interesting experience going from Stage 0 to work with the data, understand models along with writing the code

– Rajesh Chaurasia

---

Practical approach

★★★★★

I completed the Machine Learning Engineer Nanodegree course at Udacity. I learned advanced machine learning techniques and algorithms and how to package and deploy my models in a production environment. We had hands-on experience using Amazon SageMaker to deploy trained models to a web application and evaluate model performance

– Murilo V 

---

Awesome projects

★★★★★

Ending the month of February with another completed course! Nanodegree from Machine Learning Engineer. I learned how to develop and deploy machine learning in the AWS environment, develop feelings analysis models and plagiarism detection, deliver packages in PyPi, and improve skills already developed.

–Matheus Sena Vasconcelos

You can find his capstone project here

Started the udacity course on ML, it’s a great course. Goes deep into the algorithms, and talks about real world applications @udacity #MachineLearning

— Ambhi Bhandage (@AmbhiBhandage) November 26, 2021

One of the most interesting Capstone projects I found was the Dog Breed Classification by Rahul

You can find his complete project on GitHub here;

Machine Learning Dog Breed Classification Capstone Project

Here’s one more from Praveen Bandaru

Conclusion: Do I Recommend Udacity’s Machine Leaning Nanodegree?

So in a nutshell, I would say the course gives a better hands-on experience on machine learning projects.

If you have some knowledge about machine learning, this course is a good source to practice your machine learning concepts.

As ML is an ever-growing field, it is one of the hottest jobs of the 21st century and this is the right time to make a decision.

At the end, I would say ‘Yes’ to this Nanodegree.

I hope I have convinced you a bit. I did my job, it’s time for you to take the action.

If you are planning to enroll later, I request you to come back and buy the course through the links here. This will help me keep the blog running and write more articles to help learners like you.

You just read about Udacity Machine Learning Nanodegree Review.

If you find this useful, do share it with your friends

Also Read: My Experience with Udacity Nanodegrees