Are you looking for Udacity Machine Learning Engineer Nanodegree Review?… If yes, this Udacity Machine Learning Engineer Nanodegree Review will help you to decide whether it is worth it for you or not.
Udacity Machine Learning Engineer Nanodegree Review
- My Personal Experience with Udacity Machine Learning Engineer Nanodegree in a Summary
- How I Enrolled in the Udacity Machine Learning Engineer Nanodegree?
- Projects and Course Content of Udacity Machine Learning Engineer Nanodegree
- Pricing of Udacity Machine Learning Engineer Nanodegree.
- Udacity AWS Machine Learning Scholarship Program
- What did I like about Udacity Machine Learning Nanodegree?/ Why Udacity Machine Learning Nanodegree is Best?
- What I didn't like about Udacity Machine Learning Engineer Nanodegree?/ Drawbacks of Udacity Machine Learning Engineer Nanodegree
- Is Udacity Machine Learning Nanodegree Worth It?/ Should You Enroll in Udacity Machine Learning Nanodegree?
- My Suggestion
My Personal Experience with Udacity Machine Learning Engineer Nanodegree in a Summary
|The course covered various machine learning topics, like algorithms and deep learning, helping me understand how it all works.
|Instructors and Support
|The instructors were good, and I could ask for help through the support system whenever I had questions.
|The projects were the best part, giving me hands-on experience and making me feel like a real machine learning engineer.
|I got to work with cool tools like TensorFlow, which is used a lot in the industry, giving me practical skills.
|I liked that the Nanodegree was self-paced, so I could learn at my own speed without feeling rushed.
|Community and Interaction
|Connecting with other students in the program was nice; we could discuss what we were learning and help each other.
|Cost and Value
|The cost was worth it for the knowledge I gained and the skills I developed.
|Job Placement Assistance
|Udacity offered job placement assistance and career services to support learners in their career paths.
|Before starting, it was good to have some programming and machine learning basics.
|Duration and Time Commitment
|The Nanodegree’s duration was manageable, and the weekly commitment was feasible.
|Accreditation and Recognition
|The Nanodegree was recognized and respected, which could positively impact my resume.
|Sometimes, the response time from the support system was a bit slow, and it would have been better to get quicker help.
Now, let’s start this review with how I enrolled in the program and how I got the discount on Udacity Machine Learning Engineer Nanodegree.
How I Enrolled in the Udacity Machine Learning Engineer Nanodegree?
I am a Research scholar and my research topic is- “Depression Detection from Social Media“.
I started learning ML by watching various YouTube channels such as Sentdex, freeCodeCamp, etc. After that, I bought a Machine Learning A-Z™: Hands-On Python & R In Data Science course on Udemy.
This course is suitable for understanding ML Algorithm basics. But this course was not enough to understand advanced machine learning topics.
Then I got to know about Udacity Machine Learning Engineer Nanodegree Program. I thought to enroll in Udacity Machine Learning Engineer Nanodegree.
But Udacity Machine Learning Engineer Nanodegree is expensive. During Covid 19, Udacity was offering financial aid. So, I applied for financial aid. And I got 75% off.
Now, Udacity has stopped providing financial aid. But they are providing a “Personalized Discount”. To get a “Personalized Discount”, simply visit Udacity Machine Learning Engineer Nanodegree. And you will see the “Personalized Discount” option.
- Click on the “Personalized Discount” and you will be redirected to the next page, where Udacity will ask you two questions. Answer these questions and press the “Submit Application” button.
- And you will get a personalized discount with a unique coupon code. You need to copy this code and paste this code at the time of payment.
By doing this, you will get 70% off on Udacity Machine Learning Engineer Nanodegree.
Now, let’s move to the Udacity Machine Learning Engineer Nanodegree Content and Projects.
In this Review, I will share what I have learned from Udacity Machine Learning Engineer Nanodegree.
Based on my experience, Udacity Machine Learning Engineer Nanodegree is not suitable for beginners. If you have not previously worked on Python Programming and Machine Learning Algorithms, then I would not recommend this Udacity Machine Learning Engineer Nanodegree.
I watched some previous courses on Machine Learning and I had previous knowledge of Python and ML algorithms, that’s why I enrolled in Udacity Machine Learning Engineer Nanodegree.
Now, let’s see what I learned throughout this Udacity Machine Learning Engineer Nanodegree.
Projects and Course Content of Udacity Machine Learning Engineer Nanodegree
The best part about Udacity Machine Learning Nanodegree was its deployment to AWS. There were 4 courses and 5 Projects in Udacity Machine Learning Nanodegree.
The whole Nanodegree program was full of quizzes and projects. After each course, there was one project, which I had to finish.
The Udacity Machine Learning Nanodegree program began with the first course which was Introduction to Machine Learning.
Course 1- Introduction to Machine Learning
The first course was an introduction to Machine Learning. This was an easy course for me. In this course, I learned about AWS SageMaker Studio and feature engineering with Pandas in SageMaker Studio.
This course was based on the whole processing of the Machine Learning Algorithm starting from the data cleaning to the hyperparameter tuning.
I also learned some advanced concepts of Machine Learning such as XGBoost and AutoGluon.
After completing this course, there was One Project. The project was “Predict Bike Sharing Demand with AutoGluon“.
Project 1- Predict Bike Sharing Demand with AutoGluon
This was the first project of this Nanodegree program. This was an easy project for me. There was nothing too complex to complete this project.
For this project, I downloaded the Bike Sharing Demand dataset from Kaggle and trained a model using AutoGluon. After completing the project, Udacity told us to submit the optimized model for a public Kaggle rank and we also had to showcase our work by writing a report on our findings.
The reviewer was very helpful. He reviewed my work and gave feedback to improve my code.
After successfully completing this project, I moved to the next course.
Course 2- Developing Your First ML Workflow
This course taught me how I can develop my Machine Learning Workflow on AWS. This was also not a too complex course for me. I learned the fundamentals of SageMaker for training, deploying, and evaluating a model.
The instructors of this course were really engaging and explained each topic clearly. Joseph Nicolls, Charles Landau, and Soham Chatterjee were the instructor and they also taught me the tools like Lambda and Step Functions.
After learning these concepts, there was another project.
Project 2- Build an ML Workflow on SageMaker
This project was based on the concepts taught in course 2. That’s why this was not so complex a project. I gained an understanding of AWS SageMaker and by using SageMaker, Lambda, and Step Functions, I developed an end-to-end ML Workflow.
Throughout this project, I took help and suggestions from my mentor and he guided me well. With his support, I completed this project on time and submitted it for review.
Course 3- Deep Learning Topics within Computer Vision and NLP
This course was about deep learning concepts. I previously learn deep learning basics in the Udemy course. So, I had a basic idea of deep learning.
But this course taught advanced topics of Deep Learning such as advanced neural network architectures like convolutional neural networks and BERT.
The main focus was on convolutional neural networks and computer vision topics. And the project of this course was also based on Computer Vision.
Project 3- Image Classification using AWS SageMaker
I had to perform image classification by using AWS SageMaker. And I learned the concept of convolutional neural networks in course 3.
I learned some good practices throughout this project such as Sagemaker profiling, debugger, and hyperparameter tuning.
But, I faced some difficulties while completing this project. And I resubmitted this project 2 times. And then, they approved my project.
Course 4- Operationalizing Machine Learning Projects on SageMaker
This was the last course of the Udacity Machine Learning Engineer Nanodegree. This was quite a tough course for me. Because the instructor covered advanced topics such as how to deploy professional machine learning projects on SageMaker.
Sometimes we have to handle large datasets and how to handle these large datasets was also covered in this course.
I thought this was a little bit complex but overall a good course to understand the deployment procedure of ML projects.
The best part about Udacity Machine Learning Engineer Nanodegree was that first, they taught us the concepts and then gave us a project to test our understanding.
Sometimes, I felt irritated when I was stuck but this method of learning helped me so much to clear the concepts.
So, after this course, there was the next project related to the deployment of the ML project.
Project 4- Operationalizing an AWS ML Project
As I mentioned earlier, this project also helped me to test my understanding. In this project, I had to deploy the project on AWS.
Along with that, I also had to include various essential features such as cost minimization, security, and redeployment on a separate server.
I took some extra time to submit this project because of some personal reasons but this course helped me to understand the whole procedure of project deployment.
Project 5- Capstone Project- Inventory Monitoring at Distribution Centers
This was the last project and this was the Capstone Project.
For this project, I used Amazon Bin Image Dataset. We had to design a model that will count the number of objects in each bin.
We were free to choose the model type and architecture of our choice. But we had to train our model using SageMaker. This was a time taking project.
I would suggest while uploading the dataset, make sure that the data should be uploaded correctly to the correct bucket using the AWS S3 CLI or the S3 UI. Because I uploaded the data in the wrong bucket and I got confused. I had to upload the data to the S3 bucket.
Another suggestion, I would like to tell you is that after you completed the Standout suggestions, briefly explain what and how you completed the project in your README file.
README file is a file that I had to create in the last step. In the README file, I explained my project and my results. By mentioning the project and results in detail, it will be easier for reviewers to understand and they can provide feedback on your project.
So, this is all about the projects and course content I learned in Udacity Machine Learning Engineer Nanodegree.
Now, let’s see the Pricing of Udacity Machine Learning Engineer Nanodegree.
At the beginning of this review, I mentioned how I got the Udacity Machine Learning Engineer Nanodegree at a low cost. But, let’s see the actual cost of Udacity Machine Learning Engineer Nanodegree.
Pricing of Udacity Machine Learning Engineer Nanodegree.
According to Udacity, the Udacity Machine Learning Engineer Nanodegree program will take 5 months to complete if you spend 5-10 hours per week.Udacity
And for 5 months they cost around more than $800. But Udacity offers two options- One is either pay the complete amount upfront or you can pay monthly installments of $399/month.
This is expensive as compared to other Machine Learning Courses available on Coursera, Udemy, etc.
At this price, I would say Udacity Machine Learning Engineer Nanodegree is not worth it.
But if you get a discount or scholarship, then you can go for this Nanodegree Program.
I already mentioned how to get a discount. Now, I would like to share how to apply for Udacity AWS Machine Learning Scholarship.
Check Current Discount on Udacity Machine Learning Nanodegree
Udacity AWS Machine Learning Scholarship Program
Udacity is offering an AWS Machine Learning Engineer Scholarship Program. Visit their page.
Press the “Apply Now” button. And you will be redirected to the next page, where they will ask about your information such as Background Information, Prerequisite Knowledge, Goals, and Additional Questions.
In the background information section, they will ask about your Country, Age, Gender, ethnicity, the highest level of education, current job role, years of professional experience, and how many hours can you dedicate to the program per week.
The prerequisite Knowledge section varies depending on the Nanodegree program you are applying for.
In the Goals section, you have to tell your primary purpose in participating in this scholarship program, what you hope to accomplish through this program?, and Why should you receive a scholarship.
In the last section which is Additional Questions, you have to agree to their terms and conditions.
You should fill out the Goals section very carefully so that you increase your chance of getting a scholarship.
After filling out these details, you need to click on the “Save and Submit“ button. And by doing so, you have applied for Udacity AWS Machine Learning Scholarship. And if you are selected, then you will be notified via email.
Now, I would like to share what I liked about Udacity Machine Learning Engineer Nanodegree and What I didn’t like about Udacity Machine Learning Nanodegree?
What did I like about Udacity Machine Learning Nanodegree?/ Why Udacity Machine Learning Nanodegree is Best?
- The first reason was its course structure. The structure of the course was well organized.
- All the instructors were experienced and their way of teaching was very clear.
- Another thing that I liked about this Nanodegree program was its method of teaching. They didn’t only cover the theoretical part. After explaining the concepts, there was a project. And these projects helped to clear the concepts.
- Their technical mentor support was also awesome. This feature was not available on other platforms. Whenever I got stuck, I asked my mentor and he cleared my doubts.
- Their review system was also very good. The reviewer reviewed the code personally and gave feedback based on the performance.
- Throughout the program, I learned various advanced topics of Machine Learning.
What I didn’t like about Udacity Machine Learning Engineer Nanodegree?/ Drawbacks of Udacity Machine Learning Engineer Nanodegree
- Udacity doesn’t have any IOS and android apps. That’s why I faced difficulty watching the videos on my phone.
- They didn’t cover the basics of ML algorithms. You should have a previous understanding of linear regression, logistic regression, and neural networks.
- The course also didn’t cover Python programming basics. If you are a beginner, you might feel lost in this Nanodegree program.
After completing this Udacity Machine Learning Engineer Nanodegree program, I am in a suitable position to answer this question-
Is Udacity Machine Learning Nanodegree Worth It?/ Should You Enroll in Udacity Machine Learning Nanodegree?
Yes, it is worth it for intermediate learners not for beginners. If you previously learned Python Programming and Machine Learning Algorithms, then this Udacity Machine Learning Nanodegree is worth it to advance your Machine Learning Concepts. You will learn the concepts by working on real-world projects. But if you are a beginner, then don’t enroll in this Udacity Machine Learning Nanodegree without learning Python and Machine Learning.
The biggest misunderstanding among many people is that they will get a job as a “Machine Learning Engineer” after completing Udacity Machine Learning Engineer Nanodegree.
But the reality is that this Nanodegree only helps you to learn the concepts. That’s all.
After completing this Nanodegree, work on more projects and make your portfolio stronger. This will help you to get a job.
That’s all. It’s time to wrap up this Udacity Machine Learning Engineer Nanodegree Review–
I hope this Udacity Machine Learning Engineer Nanodegree Review helped you and cleared your doubts regarding the Udacity Machine Learning Engineer Nanodegree program. If you have any doubts or questions, feel free to ask me in the comment section.
All the Best!
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‘ Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.– Henry Ford