Today, I’d like to explain how I got involved with AI/ML but before, I shall introduce myself a bit more.
So, basically, I’m just a teenager, I’m16 and apparently one of the youngest dean of School Of AI. I wrote my first line of code at the age of 13 in C++ language and I couldn’t stop ever since :)
Even though I was a newbie and didn’t understand many basic (not for a kid who’s in secondary school) concepts like functions and classes, I never gave up learning. This, as I believe, is the key to get better in any field you love.
At that time (circa 2015), Artificial Intelligence was at the peak of its existence and everyone (me included) wanted to get involved with it. But there was a major blockage for most of us and that was non-other than good old Mathematics.
Let’s accept it, math is still a nightmare for most of us and AI loves to scare us with math. As a 7th grader, I also hated mathematics but someone had to learn ML right? Either me or me.
I believe most people reading this are CS students or enthusiasts but if you don’t know yet, let me give you an advice:
I mean, for learning anything, just use YouTube. That’s how I found and learned coding. One day, out of curiosity, I decided to learn machine learning; this was a year after my journey in programming. Like a normal person would do, I typed “Machine Learning tutorials” in the search bar and YouTube’s algorithm brought me a video with an Indian guy on the thumbnail.
“Hello world, it’s Siraj…!” and there he comes, the guy who would change my life forever; and his name’s Siraj Raval. Those words: Neural Networks, Backpropagation, Deep Learning, TensorFLow… what do they mean?
Joking aside, Siraj is an AI expert and a YouTuber. He recently built an online school named “The School Of AI” which aims to offer world-class AI education. His channel is also dedicated to teaching modern AI and announcing new breakthroughs in the field. You can check his channel HERE.
As an ML practitioner, you have to learn a lot of advanced mathematics; e.g calculus, linear algebra, probability theorem, and statistics. Siraj has a bunch of fun and educating playlists on his channel. I’d like to recommend some of them but not so fast, right?
Learning is a life-long journey, therefore you must be prepared for harsh hills and thorns along the way:
- I get many questions regarding the hardware and software needed for machine learning. Remember that you’re a learner, all you need is a pen and a paper. The best hardware for ML is the one you have. when the time comes, you might not even use any GPUs since there are some good cloud platforms such as Google Colab.
2. The second most asked problem is the capability of the brain for such a harsh topic. Many people think they’ll immediately fail to learn ML because they are not good at math, and that is the biggest block on your path of learning. Often times, the main reason for this hate-relationship with math is the school.
School Of AI believes that free and online education will dominate the world very soon and we are diligently working to make it happen. If one lacks the mathematical knowledge for understanding ML, he/she may learn from the internet where teachers educate VOLUNTEERLY. There are websites like Khan Academy, Coursera, edX etc. offering ML and mathematics courses. There will be links to our recommended courses at the resources part below.
3) Moving on, the next FAQ is “Can I, as 15–16–17…n years old, learn ML too?”
Although I learned this subject when I was too young, I won’t recommend it for anyone, especially to our young audience. Everyone can learn to programme for their and other’s good but Machine Learning needs more passion and curiosity, not even mentioning maths. My advice for teenagers is not to learn ML until they’re in high school and studying Calculus plus Probability; because only then things will make sense.
For people who are graduated (CS or not), the advice is to constantly revise maths and sharpening Software Engineering and algorithms skills, then check the resources we’ve provided in question #2.
Note: Please, if you don’t like none of these topics, quit ML. You’ll be doing a favour for yourself by following other passions!
These are Sobhan’s favorite Courses from Coursera:
- AI for everyone by deeplearning.ai
- Intro to calculus by Sydney University
- Machine Learning by Stanford
The article is written by Sobhan Haggi who is one of the youngest deans at the school of AI. Published by Beril Sirmacek who is the head of the School of AI the Netherlands. Beril provides all the School of AI meet up lectures at her YouTube channel. You can also subscribe to her monthly newsletter to receive the latest lecture videos, free source code and relevant vacancies. Here is Beril’s Twitter page to follow as well.
Image sources (except Sobhan, Siraj and Beril’s photos): pexels.com