My Career Journey from a Beginner to a Master in Machine Learning Engineering

We all get fascinated when we hear the words like “Machine Learning”, “AI”, “Data Science” “Generative AI” and whatnot. Sure these words are tempting and make us dive into this field but one should not choose a career like this just because it sounds satisfying. We need to understand what it takes to learn these important tech skills and what measures to take to avoid mistakes that can lead to a waste of time and effort. In this article, I am speaking about my journey of how I started my journey in Machine Learning and the mistakes I have made which you can avoid and can learn more in less. Before that, I’m giving a brief overview of who I am. I am a sophomore in an engineering college pursuing BTech in Computer Science and Business Systems.

I started my machine learning journey from my first year of BTech. Unknown what this ML is, how to study it, become a Data Scientist or ML engineer. I used to think that ML was just Python and bits of math. Since math is one of my favorite subjects, I went with ML and thought of becoming a Data Scientist. It wasn’t easy at all in the beginning though I was enrolled in an ML course which helped me give an overview of ML and the things needed to for learning ML. Just learning basics of pandas takes me fifteen days or something. I used to find very hard to understand those ‘loc’ or ‘iloc’ method for selecting rows or columns. Then the date of my first-semester exam was announced and without giving a thought I left everything I learned in ML before the exam as it is.

Now, the second semester begins, and again, I start my ML journey, learning Python, pandas, matplotlib, and seaborn which are the basics things needed in ML. Then I get to know more in my second semester like scikit-learn and deep learning and learned new topics in ML like decision tree, classification, k-means clustering, and other ML topics. Once again, my second-semester exam came and due to my lack of preparation of exam, I left whatever I learnt in ML as it is and start studying for my exam. Now, third semester starts, and again I start learning everything from scratch and I realized one thing from my past experiences that I have never done any project which are needed to solidify the concept. Hence, I start doing projects in ML after learning the ML topics from YouTube videos which really helped me to implement my knowledge in a practical life.

Now, I am in my fourth semester and I have learnt a lot in ML and because of my past mistakes I am improved a lot when it comes to learning. So instead of putting a week or a month in learning one thing I am covering more concepts in less time, and even jump into web development part this time. I even learned a bit more about the deployment part of the model and how to keep fine tune of the model with time.

Do’s

• Always make a plan for learning these tech skills.
• Have a clear goal of what will be your next step, this will help you to avoid getting stuck.
• Make sure to seek help from your friends or seniors or look for Discord servers or online videos.
• Implement your learning by doing some project or practice it by giving online tests.
• Try to learn as many tech skills possible in sort amount of time but don’t rush and skip topics.
• Make sure to grasp good knowledge of the algorithm you are using in ML and why and when to use.
• In your initial learning stage, do experiment with different types of data like texts in spam-ham detection, images in classification problem, time series to forecast the future to gain a broad skill set.

Don’ts

• Don’t get stuck at learning one thing from long time.
• Don’t stop your learning for more than one day because of your exams or functions in college or home.
• Don’t focus on mastering python or any tech stack required in ML only, ML is very broad field and just mastering python won’t help you much.
• Don’t skip documentation and start making a habit of reading them. It will help you in a long run.
• Don’t ignore the data preprocessing part and put lot of time in data exploration and analysis.
• Don’t get discouraged by the failure and let them teach you to learn about your mistakes.

If this article even able to help even one of the single people from the world, my effort would be successful. Writing this helps me realize what mistakes I need to focus on as well and how can I overcome it now. My whole intention was to make sure any beginner who is starting to learn ML must avoid these mistakes to save time and apply in some useful place. Although, the mistakes I have done are very silly and may not even come handy to others but I want to let others know them as well that what a piece of cake learning for them might turn out to be a hassle for others.