I've been a student of Machine Learning for the past two years, but this past year was when I finally got to apply what I learned and solidify my understanding of it. So I decided to share 7 lessons I learned during my "first" year of Machine Learning and hopefully make this article an annual tradition. Nowadays, it is relatively easy to learn about Machine Learning thanks to the vast selection of learning resources that exist online. Unfortunately, many of them tend to gloss over the data collection and cleaning steps. During my first serious Machine learning project, my team and I run into the BIG question of where do we get our data from?