Should you give 3 months for Udacity’s ML Scholarship?
My Honest Review — Was it worth the shot?
I chose to direct my learning with Udacity, why?
Reliability and the cost of Udacity’s programs made its reputation high end than its competitors. Unlike other online platforms, not many people can enroll in their programs due to them being pricey. Moreover, it is favored for high-class instructors. These motivations led me to experience the mystic hype for Udacity’s courses.
This article walks through the process + expectations + actionable crisply and compactly.
∘ I chose to direct my learning with Udacity, why? · Process and Time · Course-Content Overview · Ideally, for whom? ∘ 1- Fresh and junior developers ∘ 2- Aspiring Data Scientist ∘ 3- Someone interested in cloud · Suggestion and few practicable ∘ 1- Measure your time ∘ 2- Don’t overstress about the final assessment ∘ 3- Managing it as a side hustle: ∘ 4- Know how you will be tested · Concluding Comments on its valuation ∘ More From Author:
Process and Time
AWS and Udacity announced their collaboration earlier this year on all of its mediums. The application had 2–3 essays questions, majorly about the aims and how this scholarship will aid in the process. The response needs to be within the word limit, and after briefly answering them, I applied on 16th June 2021. Right after two days of the application deadline, on 25th June, I received an email about acceptance to the foundation course.
From July onwards, I had time till the first week of October to complete the study plan. That makes it a total of 3 months(ample enough) to get through the foundation and attempt the final assessment before the last date to qualify for the 2nd phase.
Course-Content Overview
Iterating what the course contained will be useless since it is already up on their website here. What I must comment upon is my learnings during it. As it was in collaboration with AWS, it was no surprise that it dominated the deployment phase. The study plan divides into six modules as follows:
. Lesson1: Introduction — Udacity navigates through its content and breaks the syllabus down. There wasn’t much takeaway from this lesson. It contained the course summary and rolled out the prereqs and extra supporting material before beginning the official content. (This is something you can get past away within 15mins.)
. Lesson 2: Introduction to Machine Learning — This briefs on two essential branches of ML namely supervised and unsupervised machine learning. It further detailed the steps to solve ML problems via vivid examples. I was acquainted with terminologies like CNN, Silhouette coefficient, Transformer, and a bag of words, all regarded as of cardinal in machine learning.
. Lesson 3: Machine Learning with AWS — It was less theoretical than lesson2 and briefed about advanced concepts like computer vision, generative AI, and reinforcement learning. It enthralled me to know how to train models with AWS services.
- AWS DeepLens: A video camera validated by deep learning and employs basics of computer vision to detect the target/audience.
- AWS DeepRacer: An autonomous race car designed to test reinforcement learning models by racing on a physical track.
- AWS DeepComposer: A composing device back with generative AI. It produces melodies that map back to the original song.
- Lesson 4: Software Engineering Practices, part 1 — It revolved around adding effective documentation, improving code efficiency for modular coding.
- Lesson 5: Software Engineering Practices, part 2 — This was an extension of lesson4 detailing the best practices one must employ and ways to test the code.
- Lesson 6: Object-Oriented Programming — This constituted stylings used in programming and it was very fulfilling to write my own Python package.
Ideally, for whom?
1- Fresh and junior developers
If you’re a fresh grad or have never worked with ML, it can be the best fit. It should be inevitable for them to know how the software market works, and this course offers precisely the same. It stresses software development flow, techniques, version control, software testing, and deployment. I am an early data professional and found it comprehensive to get the hang of fundamentals for revision.
2- Aspiring Data Scientist
If you want to set your mind to the motion similar to how Data scientists think, then this course can teach you the art of learning from data and asking the right questions. It additionally exemplifies the execution of Machine learning projects with baby steps.
3- Someone interested in cloud
AWS is the unnamed king in cloud services, and its platforms like Amazon SageMaker are parallel to none. One can look forward to gaining mastery over AWS services. Essentially on Amazon Sagemaker train, test, and deploy your Machine learning models.
Suggestion and a few practicable
1- Measure your time
If I was to go over this course again, I would advise myself to waste less time thinking about the spread of this course and its supporting material provided with it. I am by no means dereferencing its importance, but I gave unnecessary time over supporting material and didn’t start initially.
2- Don’t overstress about the final assessment
I have seen people literally getting greedy for a certificate and obsessing over the final assessment. Definitely, everyone aims to make it to the next phase. However, one shouldn't overpower the learning of phase1 along the way.
3- Managing it as a side hustle:
Initially, I was caught up with my job commitments so much that I thought it can be hard to take out time for it. But I was fortunate enough to find time for this by prioritizing. You may do it by short-listing a small number of things that top your importance and focusing on it only.
4- Know how you will be tested
This is something I was anxious about a lot. The final quiz is MCQ-based and tests the basics of the course. My tip will be to make short and precise notes of the learnings and revise them before attempting it. It puzzled candidates with the syntax of the packages, oop, magic methods, code testing, refactoring, and computer vision questions.
Concluding Comments on its valuation
Was spending 3months for this foundation course worth it? Umm..after having it finished my opinion lies somewhat in the middle. No, because, I found many repetitive things, and the videos became bland. I found it easier to go through its transcript rather than watch it on 2x speed.
Yes because I got acquainted with practical use cases from ML domain and found the problems relatable. But, a BIG but, this course nor any other like this can supplant hands-on experience on real projects. This popular program can be your next elevator to your journey towards machine learning.
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