I haven't yet started to learn about Machine Learning. I have a simple definition of Machine Learning. Let's see if that changes at the end of the day.
ML before learning
According to my understanding, if a computer is trained to detect an algorithm to process some data on the basis of input and output provided to it then it is called Machine Learning. Simply put, if I want to identify if the picture is that of a cat or a dog then I will take lots of images of dogs and cats and give them to the computer and I will also tell the computer if the image is that of a cat or a dog. Meaning I am providing input as well as output and I am expecting the machine to figure out its own way to identify them.
Now the machine can tell if the picture provided is that of a cat or of a dog.
ML after learning
Today I learned, Machine Learning is the science of getting computers to learn without being explicitly programmed. Some of the common examples of the use of Machine learning are:
- Search Engine's algorithm to rank the websites
- Movie/video recommendation by video streaming services
- Asking the voice assistant to perform some task on your behalf
- Email spam detection
How it started?
I started ML in a month of October. Yes, the same month when hacktoberfest is organized. It was my first hacktoberfest, I wanted to do some real contributions to the open-source projects. My main focus was on doing meaningful contributions. Actually, Hacktoberfest was one of the main reasons why I started Machine Learning.
MindsDB was organizing the Hacktoberfest special event/challenges for the month of October. And the prize was one of the things that I got attracted to. It was a Razer Blade 15 Laptop. I mean you are getting a laptop with all of the high-end specs. Who doesn't want it?
Resources I explored
- A Roadmap video from Apna College
- Data Science and ML by Harvard
- ML by Google
- Stanford Course by Andrew
Why did I quit?
I realized that I was learning a lot of things at the same time. I think I am a good Multitasker but I know I am not. And this is what happened to me. I was understanding the issues of open-source projects. Continuously, I was learning new tools and ways to solve those issues. Even if I wasn't able to contribute to those projects, I learned about them a bit more.
At the same time, I was building some side projects. One of them was a Email status finder, I named it Mailsbe. And another major project I built was the GitHub Lines of code calculator. It calculates how many lines of code you have written till now and in which language. Simultaneously, I was improving my video editing skills. And I was making some videos.
By this point, you may have understood that I didn't have time for a new thing. I was not even prepared. On top of that, I am very bad at managing time. Yes, too bad. I watched some videos of machine learning on the first day then I had a very long gap (a week-long). And when I resit to learn ML, I forgot what I have learned. Then I told myself that I am not doing this thing now.
Do I regret it now?
Instead of being completely lost, I have contributed to some projects and I now know the basics of Docker. If I hadn't quit ML, I would still have fear of using Docker. One thing lost to gain another thing has been true for me.
When I will restart ML?
This was all I had to share. If you want to give any suggestions to me, my DM is always open at @aashishpanthi11.