Is Hkon Hapnes a Self-Taught Machine Learning Engineer?

Hkon Hapnes is a self-taught Machine Learning (ML) engineer who has built a successful career through continuous self-learning and practical experience. His journey into the field began with a passion for financial investment analysis and a desire to build an algorithmic trading engine. This article explores his path to becoming a proficient ML engineer and the key factors that contributed to his success.

The Importance of Independent Learning in Software Engineering

("every successful software engineer is self-taught to some extent.") According to industry experts, the ability to learn independently is crucial for success in software engineering. You can't solely rely on formal education or lectures to build software. A significant portion of the work is solitary and requires you to figure things out on your own, often through trial and error, reading, and researching on platforms like Stack Overflow.

Hkon's Journey into Machine Learning

("My journey into machine learning started when I was studying financial investment analysis and wanted to build my own algorithmic trading engine.") Hkon's path to becoming a machine learning engineer began with a fascination for financial investment analysis. The desire to build a custom algorithmic trading system sparked his interest and led him to explore the field of quantitative analysis and financial engineering, finding it incredibly exciting. However, he soon realized that algorithmic trading is less effective without a substantial financial backing and that quantitative analysis is just a small part of a larger domain: data science. This realization opened new opportunities for him.

Self-Taught and the Learning Process

("I taught myself Python and started working on machine learning projects in my spare time like implementing neural networks from scratch and constructing AIs for games.") Despite not having a formal degree in computer science or statistics, Hkon focused on teaching himself Python and delved into practical machine learning projects. He implemented neural networks from scratch and created AI systems for games, which, though challenging, allowed him to make significant progress. Later, he attended the Machine Learning course on Coursera to learn proper techniques and methodologies.

Becoming a Machine Learning Professional

("I was sent to courses and given a lot of freedom. This was hugely instrumental in my journey to become a full-fledged professional.") After landing his second job as a software engineer in 2015, Hkon was provided with the opportunity to lead the company's analytics initiative. His employer recognized his interest in machine learning and supported his professional development by sending him for courses and giving him extensive freedom to run projects. As a student, Hkon also pursued coursework in statistics and big data and completed a master's thesis on a machine learning project.

Continuous Improvement and Side Projects

("I am still working on side projects and reading a lot in my spare time up to this day. I’m constantly working to improve my skillset.") Hkon continues to work on side projects and remains committed to lifelong learning. He frequently reads and engages in supplementary learning, which has helped him improve his skillset and stay ahead in the rapidly evolving field of machine learning. Furthermore, his involvement with Quora has been invaluable, serving not only as a knowledge resource but also as a platform to connect with other enthusiasts and share his insights.

Through a combination of self-directed learning, practical experience, and ongoing education, Hkon has successfully transitioned from a self-taught enthusiast to a proficient machine learning engineer. His journey highlights the importance of independent learning and continuous improvement in the field of software engineering and machine learning.