I’ve always been interested with Artificial Intelligence and Machine Learning field since I started working as a software engineer. I wondered how the machine learning algorithm work and how cool the result they produce, I mean, the programming I know was like, I need to define the flow from X to Y and the resulting software will follow exactly what we wrote.
But, Machine Learning is different, I only need to feed it with data, and the response that it produces will always be different depending on the data I fed into it. How dynamic the software is differs from the programming that I know.
A little background
I have some basic knowledge about Artificial Intelligence and Machine Learning because I studied it when pursing my Bachelor Degree. When I took the courses, I knew nothing about AI and ML, how and where it used in real life, I don’t even have a high interest in it, but a lot of my friends took the AI course, so I took it too.
After I graduate and started working in an e-commerce company, Artificial Intelligence and Machine Learning got more traction in my country. I started taking interest once again in AI and Machine Learning.
Data Scientist is arguably the most popular and hot career that uses machine learning and artificial intelligence. But, after I read about it, I thought it’s not for me because I enjoy building things more.
Why I took a MOOC
So, as a software engineer that is not interested in becoming data scientist, what are my reasons to study machine learning?
Here are the reasons.
Machine Learning is very popular right now
Machine Learning is very popular in technology industry, everyone is talking about it, many companies have started if not already implementing it in their system. It’s a growing topic that there is new research, article, and code added into it every day.
Learning about it will make my career more future proof. If I ever need to specialize in it for an opportunity, then I would already have the basics.
Artificial Intelligence seems to be the future
The use case and possibility for Artificial Intelligence is increasing day by day. I’ve heard people talking that artificial intelligence would be the next industry revolution.
Since machine learning is a subset of AI, learning it would introduce me to it.
Good for portfolio
Finishing the course will show the employer you have a determination and consistency to finish a long and expensive course. The certificate you got from finishing the course won’t instantly land you a job, but at least it will help you.
I’m craving for the knowledge
I’ve been interested in machine learning for many years. I learned a bit in college, but I’m interested in knowing more about how and how should they work.
There is a possibility that I will take a master degree
I’m considering taking a Master Degree, and I took one, I’ll probably focusing on Artificial Intelligence and Machine Learning. Knowing the basics about them before starting a master degree will help me a lot later on if I ever pursued a master degree.
The Execution
The course that I chose
The first question after deciding that I would take a MOOC about Machine Learning is which course I should take.
I visited the mainstream site that provides MOOC to find which course suites me the best.
After visiting and comparing the courses in edX, Udemy, Udacity, DataCamp, and Coursera, I decided that Machine Learning Specialization on Coursera is the one that suite me the best.
I thought the course would suite me the best because it teaches not only theory, but practical too. It has 2 types of quizzes, which are theory and programming quiz. I thought, after finishing the course, then I could understand the basics algorithm behind the machine learning, and how to implement it.
If there is any weakness that I could think of when choosing it, it would be that the programming course teaches mainly with Turi Create library as opposed to the more popular library skicit-learn.
The process
There are 4 courses in the Machine Learning Specialization MOOC, with each course containing 6–8 modules. There is a 1-week deadline between module.
It was difficult, but the instructors teach the courses thoroughly and at a high level, so even someone that doesn’t have any background in Math except in high school and college like me can understand it.
I finished the course in 5 months, I only missed one deadline because I was busy with work. It cost me $250 to finish the specialization.
It took a long time to finish it, and it’s not cheap.
Was it worth it?
The specialization that I took was worth it. The specialization was expensive and time consuming, but I don’t think I will get an information with that quality cheaper.
Since the quizzes come in theory and programming, I could understand the fundamental algorithm behind the machine learning, and I can also implement it in code.
My decision to join MOOC. To be honest, I’m still not sure. I have got no use of it yet.
But I’m sure in the long term, it would benefit me because of the reasons I stated. Even if not, at least I learned to be consistent and productive.
What’s next
I want to continue learning artificial intelligence and machine learning. There are many ways to do it. For one, I could go to Kaggle and try competing there, I could create a machine learning project, or I could try another MOOC.
I still don’t know which way I am going to go, but right now I’m thinking to take Deep Learning Specialization by deeplearning.ai on Coursera.
Conclusion
Even if you’re not working with AI and Machine Learning yet, there is no reason to not learn it. As a good software engineer, you must be flexible to learn new and popular technology, and AI seems to be that.
Thank you for reading this article, I hope it helps you.
