To kickoff DAC’s brand-new Project Series, our team of subcommittee members had the opportunity to learn more about the foundation skills needed in a Machine Learning project and pick up more knowledge about Machine Learning from Traci, a SIM Alumni who is currently working as a Data Scientist at SAP. We started off by learning about the Math foundations required in Machine Learning. Here’s some of the things that Traci shared with us in this session:
Everyone has had experience learning about Math in school, but how does it relate to Data Science, or more specifically, Machine Learning? What kind of Math knowledge would be required for a career in Machine Learning?
Firstly, lets answer some misconceptions about Machine Learning…
1. ML is only for Math/Computer Science students
ML applies the knowledge of Algorithms, which in real-life, we have all been applying different rules and steps our entire life! ML just applies the idea of rules/steps into computers. For example, it could work like this in real-life:
Click on the ‘Play’ icon to run the code
2. It is not a must to understand ML Math
There’s of course a lot of different algorithms involved in ML and you don’t need to understand everything, but Math is still a integral part of ML that you will need to understand. You might not need to understand 100%, but it is still important to understand the basics of it to change our algorithms and improve in our accuracy levels of our algorithm.
3. I don’t have a Quantitative Degree, Learning ML would be impossible.
There are indeed data scientists that majored in psychology, philosophy, political science, and they are doing well in Data Science. You can find ways to learn and explore more about Data Science, your major doesn’t define who you are!
Take a look at some of the things that Traci has shared during the session!