Build AI Applications Easily with AWS Machine Learning Course on Coursera

Mohak Trivedi

08 June 2023

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Course Overview

You can learn the basics of ML right away to keep up with your growth, increase your skills, and even advance your career with Getting Started with AWS Machine Learning Course by Coursera. This course will teach you about AWS Machine Learning. The course will cover key topics: Machine Learning on AWS, Computer Vision on AWS, and Natural Language Processing (NLP).

Each topic is divided into several modules that dive deep into a variety of ML concepts and AWS services. Learners with a bachelor's degree, professionals in their mid-career, and developers are encouraged to enroll in this course. After course completion, one may work as an expert in various settings, including corporations, technology organizations, start-ups, and government bodies.

The course USP is the hands-on training and instructor-moderated discussions provided. This makes it the best possible learning experience for participants and prepares them for AWS certified Machine Learning Jobs. Blaine Sundrud, the instructor of this curriculum, has been teaching technology for many years. Blaine has taught students across the globe different disciplines, such as Security, Cloud Architecture, DevOps, Big Data, AI or ML, and Theater History.

"One of the best things about this course is that the instructors explain even the most advanced concepts such as ML Pipeline, Differences between ML and AI and Deep Learning, and ML algorithms, in a very concise and easy to understand manner."

- Mohak Trivedi

Course Structure

The online certification program is an intermediate-level course. It is spread over 9 hours and has the option of self-paced learning, keeping in mind working professionals. The critical areas you cover in this curriculum include AWS Global Infrastructure, AWS, Machine Learning Models, Computer Vision, etc.

Learners get access to high-quality recorded content and practice assignments given at the start of every week during the program. There are no set deadlines or instructional schedules, so learners can finish quickly if they want to.

After enrolment, learners are given access to the study material and weekly assignments. The assignments focus on both practical and theoretical learning. Assessments are integrated into the programs to ensure continuous learner engagement.

This practical-oriented program gives learners thorough knowledge. Technically, the AWS Machine Learning Course is spread over 3 prime modules:

Module 1: Introduction to Machine Learning

Module 2: Machine Learning Pipeline

Module 3: Amazon AI Services: Computer Vision

Module 4: Amazon AI Services: NLP

Module 5: Introduction to Amazon SageMaker

Insider Tips

In order to get the best out of this course, I have included some important tips below that I think you might find useful.

Take Quizzes and Projects Sincerely

The quizzes in this course are designed to ensure that you are able to retain the concepts you learn in the video lectures. Coursera allows you to re-watch video lectures, so you must use this feature and re-watch the videos to fill in your knowledge gaps. Once you are confident you may attempt the quiz again. However, I would recommend you to study the video lectures well before your first attempt so that you don’t have to make further attempts.

Apart from theoretical video lectures and quizzes, the course consists of interesting follow-along project videos in which the instructor will be coding up a machine learning project on the AWS platform. You should not just watch these lectures or take notes but try to grasp the practical workings of ML from the instructor’s discourse.

As for your first project, you will be working on Object Detection on Images labeled with Ground Truth. In the second one, you will build a text-classification model with Glue and Sagemaker.

Add Git and Terminal to your Charts

Git is easy to learn and has a tiny footprint with lightning fast performance. For this particular course, it is not mandatory to know Git, however, if you use Git while building your projects in this course, you’ll get used to maintaining a project repository. At the end, you will be able to showcase your project work along with a well-documented readme.md file to your recruiters, which would be quite impressive.

Document your Learning

One of the best things about this course is that the instructors explain even the most advanced concepts such as ML Pipeline, Differences between ML and AI and Deep Learning, and ML algorithms, in a very concise and easy to understand manner.

So, I would highly recommend you to take notes and write an article on it. You can look back on it and see how much progress you have made. Writing notes down will help you retain information in an effective manner.

Build a Side Project

Try to work on a project side-by-side with your studies. Set aside some time every day to work on this project. I would recommend building a text-classification project similar to the NLP project which is well explained in the course.
You may feel like the project work is overwhelming, so I recommend you to start simple and improve your model step by step. Work patiently on your project for some time and watch it come to life. Choose something that will challenge you to step up on your skills.

Prerequisites or References

I would recommend that you have at least an intuitive understanding of the working of ML and Deep Learning models and a good understanding of Python programming language. Note that you will be able to complete the entire course even without these prerequisites, but then you won’t be able to build any interesting projects of your own, all you’ll be doing is copying the code from video lectures. Here are some free resources: (just an intuitive understanding of all algorithms would be sufficient, however if time permits you may do these courses as well.

Understand the Code

Just finding a code that might work and applying it is not enough. You must understand how the code functions. Which syntax of the code is added to perform a specific task; it is very important for you to know that. If you do that, you will be able to manipulate it to fit your project style and conventions.
You must know which syntax and functions do what. In these cases, slow and steady wins the race. All of this will make you a strong engineer even if you don’t have much experience on paper.

Keep Learning

As a technology enthusiast, you must never stop learning. You can follow these 3 steps:
Learn it
Make it work
Make it fast
Start by learning things. Then start implementing them. Make sure the implementation is correct and error-free as much as possible. This will strengthen your base. After that, you must start working on your speed. Try to make things faster and more efficient.

Final Take

Currently, I am pursuing my graduation in Computer Science Engineering. I have taken this course to hone myself in AWS Machine Learning. The course USP is hands-on training and instructor-moderated discussions. This help create the best possible learning experience.

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