Topic of my bachelor thesis was about machine learning. After graduating I couldn’t get job in this field. Instead I worked as a R&D Engineer in Nokia Solutions and Networks.
Next touch to ML was when I taught kids to code in Kodarit. I found a great environment where kids where able to try different machine learning algorithms. I enjoyed it a lot like in the university, even the math behind the algorithms where difficult to understand.
Turning point
Couple of weeks ago I was at the park with my kids. There I ended up talking with an entrepreneur who have a start-up company, which develops ML solutions for the different industry areas. From that inspiring conversation I got new energy. I started to think, can I still come back to machine learning even though I had been away from it so long.
To be honest, I am not sure where this ML journey will take me, but I feel good. I started Andrew Ng’s Machine Learning course at Coursera. I am currently learning about how to implement linear regression with Octave scientific programming language which syntax is largely compatible with Matlab.
Next steps
What’s next?
I want write blog posts which supports my learning. In that way I can keep myself accountable and maybe help some others, who are at the same situation. As I said, the math part is quite difficult for me because I didn’t have the advanced math at the hight school. I hope my enthusiasm will narrow the cap. In conclusion, writing the problems open, will help understanding the math topics.
As I have noticed before, to write full and detailed posts can be exhausted. Moreover it can stop writing for a long time. My plan for prevent it, is to allow me to write short cheet sheets. E.g. cheet sheets from the basic Octave commands would be nice and useful post.
As a recap, first I will finish the 11 weeks long ML course and update my status in blog posts. After that I am going to make a decision, is this field for me. Welcome to follow my journey!
Featured image by Alexander Sinn on Unsplash.