How Good is Udacity Deep Learning Nanodegree in 2024?

Do you have a question, “How Good is Udacity Deep Learning Nanodegree“? If yes, then read my Udacity Deep Learning Nanodegree Review and clear your doubts. In this article, I will share my learning journey and experience with Udacity Deep Learning Nanodegree.

Now without further ado, let’s get started-

How Good is Udacity Deep Learning Nanodegree?

First, I would like to share my personal experience with Udacity Deep Learning Nanodegree in a summary table-

My Personal Experience

Aspect	Review Summary
Course Content	The course content of the Udacity Deep Learning Nanodegree was comprehensive and informative. It covered essential deep learning concepts like neural networks, CNNs, RNNs, and NLP. However, some topics felt rushed and lacked in-depth exploration.
Quality of Instruction	The quality of instruction was generally high. The video lectures and interactive quizzes were helpful in my learning process. Moreover, the instructors seemed knowledgeable about deep learning.
Hands-on Projects	The hands-on projects were my favorite part of the Nanodegree. They provided valuable practical experience, allowing me to apply what I learned to real-world scenarios and collaborate with peers.
Support and Community	The support from the community of learners was beneficial, and I appreciated having access to mentors for personalized guidance. On the other hand, response times for mentor support varied, and sometimes, I relied more on peer-to-peer assistance, leading to mixed support experiences.
Time Commitment	The flexibility in scheduling was a plus, as I could complete the Nanodegree at my own pace. However, balancing the course with other commitments was challenging, and I faced some time management difficulties.
Value for Money	Considering the course’s content and hands-on projects, I felt that the Nanodegree provided good value. Nevertheless, I expected more extensive post-graduate career support or additional resources for the price paid.
Pros	In summary, the Udacity Deep Learning Nanodegree had several positive aspects. ✔️ First, the course content was comprehensive, covering essential deep-learning concepts. ✔️ Second, the hands-on projects provided valuable practical experience. ✔️ Third, the supportive community and access to mentors were beneficial. ✔️ Fourth, the flexible scheduling allowed for self-paced learning. ✔️ Lastly, it offered opportunities for potential job placements.
Cons	On the other hand, there were some areas that could be improved. ❌ First, some topics felt rushed, lacking in-depth exploration. ❌ Second, variations in teaching style led to occasional confusion. ❌ Third, certain projects could have been more challenging. ❌ Fourth, response times for mentor support varied. ❌ Lastly, I expected more extensive post-graduate career support or additional resources for the price paid.

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Now, let’s see-

How I had Chosen this Nanodegree program?

I am a Research scholar and my research topic is- “Depression Detection from Social Media“.

So I started my machine learning journey by watching Sentdex, freeCodeCamp, and various other popular YouTube channels.

Because as a beginner, we don’t want to pay huge money for any course. And these YouTube channels helped me to understand the machine learning basics.

But I thought this information is not enough for my research work, then I took Machine Learning A-Z™: Hands-On Python & R In Data Science course on Udemy.

In this course, Kirill taught me Machine Learning algorithms such as Regression, Classification, Clustering, Association rule learning, Reinforcement learning, NLP basics, and Deep Learning basics. This course was really a brilliant entry point into the world of machine learning for me.

After learning deep learning basics in this course, I had finalized that I have to learn more advanced concepts of deep learning.

So I studied various books such as Neural Networks and Deep Learning by Michael Nielsen, etc. But I was looking for something that teaches me deep learning in a practical way.

Because I am planning to learn deep learning for my Research work. And I believe you can’t get 100% from theoretical knowledge until you apply your learning to real problems.

And then I heard about the Udacity Deep Learning Nanodegree program that this program has practice-based courses. So I thought to enroll in this Nanodegree program.

But the next challenge for me was-

Udacity Deep Learning Nanodegree program Cost

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Udacity Deep Learning Nanodegree program costs around $399 per month. For me, it was too expensive. So I was in doubt to spend that much amount on any course. But Most of the time Udacity provides Discount. When I enrolled, Udacity was providing a Personalized Discount.

And I got 70% off during this discount period. You can also check for the Udacity Current Discounts.

Another option is Udacity Scholarship. For this, you need to go on their Scholarship page and find the scholarship for the program you want to enroll in. If you found your Nanodegree program on the list, then you need to apply for the scholarship by filling out these details-

1. Background Information
2. Prerequisite Knowledge
3. Your Goals
4. Additional Questions

After filling out these details, you need to click on the “Save and Submit“ button. And by doing so, you have applied for Udacity Scholarship. And if you are selected, then you will be notified via email.

So this is how I had chosen this Udacity Deep Learning Nanodegree program. Now I would like to tell you a few important things related to the Udacity Deep Learning Nanodegree program–

Prerequisites for Udacity Deep Learning Nanodegree program

For enrolling in this Nanodegree program, there are some prerequisites mentioned on their official website. According to them, you should have only basic working knowledge of Python programming.

But I experienced that only basic python knowledge is not enough. You should be confident and fluent in Python for this Nanodegree program.

Along with Python, you should also be familiar with machine learning algorithms. You need to have a good understanding of Machine Learning algorithms especially supervised learning. And you must know how to train and evaluate the model.

So if you meet these requirements, then you can enroll in Udacity Deep Learning Nanodegree program.

Now I would like to share-

What did I learn in the Udacity Deep Learning Nanodegree program?

I liked the structure of the Deep Learning Nanodegree program because after every set of courses there was a project that I had to submit. And this is the best approach for learning.

Udacity Deep Learning Nanodegree program had 4 courses. And each course had a few lessons. These were the courses-

1. Introduction to Deep Learning
2. Convolutional Neural Networks
3. Recurrent Neural Networks
4. Generative Adversarial Networks

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Now let’s see what did I learn in each course-

Course 1. Introduction to Deep Learning

This was the introductory course to deep learning. But this course taught various topics on Deep Learning such as Neural networks, how to train a neural network, Gradient Descent, and Error Function.

PyTorch was used for data preprocessing. That’s why previous knowledge of the Deep Learning framework is good to have.

Overfitting and Underfitting concepts were also explained by the instructors.

After this course, there was one project.

Project 1- Developing a Handwritten Digits Classifier with PyTorch

To explore and test your deep learning skills, I think this is the best project to consider. In this project, we had to build a recognition system that recognizes human handwritten digits. PyTorch was used throughout the project.

Udacity provided a Technical Mentor Support feature, which was very helpful. Throughout the project, you can ask your doubts to the mentor.

Course 2. Convolutional Neural Networks

For me, this course was really amazing. In this course, I learned about Convolutional Neural Networks(CNN).

Convolutional Neural Network is an algorithm of Deep Learning. That is used for Image Recognition and Natural Language Processing. Convolutional Neural Network (CNN) takes an image to identify its features and predict it.

This course taught me the layers of CNN, and how to combine all these layers and build a CNN model using PyTorch.

These are the following layers of CNN-

1. Convolution Operation.
2. ReLU Layer.
3. Pooling.
4. Flattening.
5. Full Connection.

Autoencoders and Transfer Learning concepts were also taught by the instructor. All the implementation part was done in PyTorch.

After this course, there is one project-

Project 2- Landmark Classification and Tagging for Social Media

This project was helpful for my research work. landmark images dataset was used in this project. For this project, we had to build a CNN model that can predict the location of the image based on any landmarks shown.

This was a bit challenging project for me, but after some attempts, I cleared this project and submitted it successfully. I learned a lot while working on this project although it was challenging.

Course 3. Recurrent Neural Networks and Transformers

This course was focused on RNN or Recurrent Neural networks. A Recurrent Neural Network is a network that can understand sequences and time.

In this course, the instructor explained advanced concepts of deep learning such as Recurrent Neural networks, Long Short Term Memory Networks(LSTMs), Seq2Seq Architecture, etc.

This course was complex because it required previous math knowledge.

After this course, there was another project-

Project 3- LSTM Seq2Seq Chatbot

The objective of this project was to build a Chatbot using LSTM and Seq2Seq. Udacity provided us with a dataset of conversational dialogue.

We had to complete this project in PyTorch. We had to perform all the steps starting from training the dataset to evaluating the model performance.

After completing the project, we had to submit the project for review and the reviewer examined the project and gave feedback.

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Course 4. Generative Adversarial Networks

Generative Adversarial Network is used in Image Generation, Video Generation, and Audio Generation. GAN is able to create an image, videos, and audio in the same way as human creates. In short, GAN is a Robot Artist who can perfectly create any kind of art. This functionality of GAN makes it powerful.

And this was the most amazing and interesting course for me. In this course, I learned about Generative Adversarial Networks.

The instructor of this course was very helpful and explained the concepts practically.

After this course, there was the last project of this Nanodegree program.

Project 4- Face Generation

This was a fun project. For this project, Udacity gave us the CelebA dataset.

We had to build a GAN architecture that includes a generator and discriminator.

The generator network or model takes a random input and generates a sample. This random input may be the pixel values of the image. And with the help of these pixel values, a generator model can create an image.

The discriminator network learns from the real images and becomes able to detect real or fake. And the generator passes the image to the discriminator. And discriminator detects whether it’s real or fake.

The main objective of the generator is to fool the discriminator. Whereas Discriminator’s objective is not to fool by a generator.

So this is what I learned throughout this Udacity Deep Learning Nanodegree program. Now I would like to tell you one more important thing and that is how much time you need to give to this Nanodegree program.

How Much Time is Required?

According to Udacity, 4 months (If you spend 12 hours per week) are required to complete this Deep Learning Nanodegree program.

This program is not self-paced, meaning there is a deadline and you have to finish your work before the deadline. It’s a good method for lazy learners like me.

The nature of the Nanodegree program helped me to complete the whole program in 4 months. Now I would like to share What did I like and What I didn’t like about the Deep Learning Nanodegree program or the Pros and Cons of Udacity Deep Learning Nanodegree-

What did I like about the Deep Learning Nanodegree program?/ Pros of Udacity Deep Learning Nanodegree program

• The first thing that I liked about the Udacity Deep Learning Nanodegree program is its practical approach. They are not only focused on theoretical understanding but also practical understanding. All the projects associated with the program were interesting. I personally loved the Face Generation project via GAN.

• The structure of the whole Nanodegree program was amazing. I mean they covered a variety of material, which anyone can easily understand. As I mentioned that each course has some lessons, and each lesson has approx 10-30 videos 3-6 minutes long. Each individual video was easy to understand.
• Along with projects, there were also various demos on problems.

• Their second and fourth course(CNN & GAN) was really unique for me. These two courses helped me so much in my research.

• The community was very active and helpful. Whenever I had some doubts, they were ready to help me. The staff is continuously updating their content to make the Nanodegree program up to date. This is really amazing. Very few platforms do this.

Now I would like to discuss a few things which I didn’t like about the Deep Learning Nanodegree program–

What I didn’t like about the Deep Learning Nanodegree program?/ Cons of Udacity Deep Learning Nanodegree program

• They didn’t cover the math behind deep learning in-depth, which was surprising for me. They were more focused on coding. That’s why I would recommend brushing up on your math concepts before enrolling in this Nanodegree Program.

• Udacity doesn’t have an IOS or Android App for learning. You can’t learn on your mobile device.

So, these two things I didn’t like in the program.

Unique Udacity Features

1. Extra Materials for Learning

As I told you that Udacity Deep Learning Nanodegree required previous Python and Machine Learning knowledge. That’s why Udacity provided extra course materials for learning Python and Machine Learning. These courses are included in the Udacity Deep Learning Nanodegree. So, you don’t need to buy a separate course for Python and Machine Learning.

2. LinkedIn and GitHub Profile Support

You will also get help with your LinkedIn and GitHub profiles. Udacity provides a separate project, where you will learn how to improve your GitHub and LinkedIn profile.

3. Technical Mentor Support

This was the feature, I loved. You will get technical mentor support when you enroll in the Nanodegree Program. And throughout the program, you can clear your doubts with the mentor.

4. Support Community

Udacity also has a community where you can ask with other learners. This is a helpful and engaging community. You will get a quick response from other learners.

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Is Udacity Deep Learning Nanodegree Worth It?

Yes, It is worth it just because of their content and projects covered. Their content is updated and advanced. All four projects will help you in your resume. After working on these projects, you will gain more confidence in Deep Learning. Their Technical mentor support and Career Services were also worth it for improving GitHub and Linkedin Profiles. All the instructors are experienced and work in top companies.

Will Udacity Deep Learning Nanodegree Content and Projects Help in getting a Job?

Yes, The projects will help you to make your portfolio stronger. Throughout this program, you will learn advanced Deep Learning concepts and work on the Deep Learning Model building. That’s why you will get an idea of how deep learning algorithms work. This knowledge will help you in your job interviews.

Will Udacity Deep Learning Nanodegree Certificate help you in your Job?

No. The certificate provided by Udacity is not as helpful as the content and projects. First of all, the Udacity Certificate is not accredited by any University. Don’t enroll just for sake of a Certificate. Focus on learning and understanding the content and try to complete the project by yourself. This will be more helpful for you.

Final Thought

I have shared my complete experience with the Udacity Deep Learning Nanodegree program with you. Now the last thing which I would like to clarify is Who should really go for this program and who shouldn’t?

Who Should Enroll?

According to my experience, this Udacity Deep Learning Nanodegree program is best for those who have some serious objective for learning deep learning like if you are planning to switch your career as a deep learning engineer or like me who has some research goal related to deep learning.

Or you are interested to get some practical experience in deep learning and not concerned about maths. In all these cases, I would definitely recommend you to take this Udacity Deep Learning Nanodegree program.

But if you want to learn deep learning basics or if you are looking for a course that covers the theoretical part and math in detail, then the Udacity Deep Learning Nanodegree program is not for you.

In this case, there are other options available, which you can check here- Best Deep Learning Courses. I hope now you understood whether this Udacity Deep Learning Nanodegree is good for you or not.

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Conclusion

In this article, I tried to cover and share my experience. And I hope this article has given the answer to this question- How Good is Udacity Deep Learning Nanodegree?

If you have any doubt or questions, feel free to ask me in the comment section.

All the Best!

FAQ

Thank YOU!

Though of the Day…

‘ It’s what you learn after you know it all that counts.’

– John Wooden

Read Deep Learning Basics Here