After having merged and roughly cleaned my reference data, I was eager to start annotating.
After I had narrowed down the topic of my first machine learning project to building a movie recommendation algorithm, I quickly found some movie data from the IMDB database that I could use. That way, I could basically skip the whole step of learning how to use an API properly and get the data myself (haha! I thought. Read on).
After identifying the topic of my first ML project, I needed to outline my business problem. Following what I had learned in online courses and YouTube videos, I went through these 5 steps.
I looked for a project in which data played a vital role, and where machine learning could be applied – mainly because data preparation and machine learning, as well as data visualization were areas where I wanted to improve my skills. While I meditated about this topic, I identified five aspects that stood out.
It is usually quite easy to get excited for a new topic, to delve into the basics and to learn just enough to recognize buzzwords around that new topic. However, in every learning journey, there comes the time where you stand at that metaphorical junction and have to decide where to go next.