Hello, I'm Yuvraj Singh, currently a 2nd-year student at Chandigarh University pursuing a bachelor's degree in artificial intelligence and machine learning. Today, my main motivation for writing this blog isn't to win prizes or anything; rather, it's because I want my story to be heard. For making this possible, I am extremely grateful to Hashnode's Dev Retro 2022 and Kunal Kushwaha's Community Classroom for motivating me to share my knowledge with others through blogs and for rewarding me for my efforts.
I'll be breaking up my tale into four parts to make it easier for you to grasp each stage, and I promise that after reading each one you'll have some ideas that might aid you on your incredible life journey.
Chapter 1: Gave JEE but not to get into IIT
For those who don't know what IIT is, it is just an entrance exam for admission to one of the most prominent engineering colleges in India. If you are an Indian student, you may find this remark weird to hear. Before I go any further with what you just read in the title, allow me to briefly describe my background. My primary residence is Ropar, Punjab, and I graduated from Shiwalik Public School there in 2021 with an overall grade point average of 91.8% in my 12th grade, which was pretty good.
However, the issue is that no one takes these scores seriously since they believe that receiving this many points in your board exam during COVID time was simple. This comment is quite depressing to hear, not just for me but for everyone who worked hard for 8 to 9 hours every day at that time to earn decent grades. But set that aside and continue to the next paragraph, where you will read something you certainly wouldn't anticipate reading from someone studying for a board exam in India.
Got to know about JEE for the very first time just 2 months before the exam 🤯
I learned about the JEE exam for the first time in December 2020 from a friend who had just applied to sit for the exam in February (2021). I recall his reaction: "Really, bro?!" You've never heard of JEE, right? I replied with a firm, expressionless face, "No," to which he replied, "You will not be successful in life, especially in India, because you are not preparing for JEE." He continued, "Since you are not preparing for JEE, it is obvious that you will not get into IIT." Because of this, you will receive a tier-3 college with a 3–4 lakhs per year package, and you will struggle for your whole life with a Rs 25,000–35,000 salary.
Even though I didn't know what field I would be choosing (such as computer science, mechanical engineering, or aerospace engineering) at the time, I just started looking for what are other good colleges in India in which I could enroll. However, while browsing the internet, I came across one website that completely helped me discover what I want to do in my lifetime.
Chapter 2: How I started my developer journey
As I previously mentioned, I just started browsing for what are some other reputable colleges in India that I may get enrolled in after listening to my friend. Fortunately, I stumbled onto 👉 this website 👈. Once you visit the website, you will be taken to an interface where, as you type, some magic will occur, letting you know if your input was positive, negative, or neutral.
When I first utilized that website, my mind was utterly blown since I couldn't believe that the software could accurately determine what I was trying to say when I entered something. I then began learning more about it, and after about a week of investigation, I learned that this miracle occurs in machine learning under ( Sentiment analysis ).
The thing was that, after researching sentiment analysis for a week, I developed a strong interest in AI and ML and realized how useful these tools are. As a result, I knew without a doubt what I wanted to do, so I chose CSE-AIML as my bachelor's degree at Chandigarh University, and from the start of my first semester, my new journey began.
I started browsing YouTube for what we should learn about machine learning at the beginning of my first semester because I was so interested in it. While doing so, I came across this dude. 👇
This dude right here explained what is required to become a machine learning engineer. After viewing the video, I created a flowchart, but as soon as I did, I realized that it might take me a year or two to fully comprehend the ideas and to be proficient in using them.
So, as soon as I realized what I needed to learn to be proficient in the field of ML, I began looking for the best resources to follow, and during my research, I discovered three amazing YouTube channels that I would recommend you watch if you want in-depth knowledge about machine learning concepts.
To begin your journey, you can view any of the above-mentioned channels.
Moving on, I was introduced to the mathematics underlying machine learning as soon as I began studying the various machine learning algorithms. At this point, I found it to be a little challenging and began to wonder if I would be able to succeed in machine learning.
I decided to first go over some of the core concepts of machine learning mathematics to overcome my phobia of it. To do this, I followed the playlist shown below.
After watching this statistics playlist, I finally feel a little more comfortable using machine learning methods. And after consistently learning new things for approximately 6-7 months, I felt very good about machine learning, so I decided to start creating projects in this area. After doing some research, I discovered a goldmine on LinkedIn about over 817 machine learning projects.
Deep dive in the field of AI
As time went on, I worked day and night on new projects to gain practical machine-learning knowledge, but when my machine-learning algorithms started to perform poorly, I started to do some research and discovered that deep learning is also a field of artificial intelligence and is a subset of machine learning.
I was very eager to learn more about it because I entered deep learning not just to study but also to find a solution to an issue I was having. As before, I found some incredible resources after exploring the top deep-learning resources.
Chapter 3: Start of technical content creation journey
I still remember that I was looking for some Docker-related videos on Youtube, and fortunately, I came upon the Kunal Kushwaha channel, and believe me when I say that if you are not familiar with Kunal, you are unknowingly missing out on a priceless gem. I learned about tech communities, learning in public, and open source from Kunal's channel.
And from that point onwards, I started joining a lot of machine-learning communities and also started attending some of the meetups, both offline and online, Some of the places where you can find ML communities include:
Reddit: The r/machinelearning and r/artificial subreddit are popular forums for discussing ML and AI topics.
Stack Exchange: The Machine Learning and Data Science Stack Exchange is a question-and-answer site for people interested in machine learning and data science.
Kaggle: Kaggle is a platform for data science and machine learning that hosts competitions, datasets, and other resources for the community.
LinkedIn: LinkedIn has several groups focused on ML and AI, including the Machine Learning and Data Science group and the Artificial Intelligence group.
Meetup: There are many ML and AI meetup groups in cities around the world, where you can connect with like-minded individuals and attend talks and events.
Google Groups: There are several Google Groups dedicated to ML and AI, including the Google Machine Learning group and the Google AI group.
In addition to joining communities, I set up a Twitter account on Kunal's advice and started posting stuff related to machine learning and deep learning in the form of threads and blogs. You can also check out some of my machine-learning blogs.
Final message
My final message to everyone reading this blog is to always do what you want to do and don't worry about how you'll do it at first; you just need to figure out why you want to do it. For instance, I was interested in machine learning but was intimidated by the mathematics required. Instead of being afraid, I sat down and explained why I needed to understand the mathematics underlying ML algorithms, and I gradually learned it. As a result, I can now say with confidence that I understand the mathematical aspects.
So keep learning, keep sharing, and keep growing. From my side soldier, best wishes!
Connect with me on 👉 Twitter