I have been working as a Data Scientist for over two years. Over time, I have learned and mainly studied machine learning (ML). To me, it’s probably the most fascinating part of the job.
ML is a BIG space, there is so much to learn and understand. However, taking it one step at a time makes the whole process less daunting and much easier to handle.
In this article, I want to go over the steps I would take if I had to learn ML from scratch again. Let’s get into it!
Machine learning revolves around algorithms, which are essentially a series of mathematical operations. These algorithms can be implemented through various methods and in numerous programming languages, yet their underlying mathematical principles are the same.
A frequent argument is that you don’t need to know maths for machine learning because most modern-day libraries and packages abstract the theory behind the algorithms.
However, I would argue that if you want to become a top-level Machine Learning Engineer or Data Scientist, you need to know the basics of linear algebra, calculus, and statistics at least.
There is of course more maths to learn, but best start with the basics and you can always enrich your knowledge later on.
You don’t need to understand all these concepts to a master’s degree level but should be able to answer questions like what is a derivative, how to multiply matrices together and what is maximum likelihood estimation.
That list I just wrote is the bedrock of nearly every machine learning algorithm, so having this solid foundation will set you up for success in the long run.
Some of the key things I recommend you learn are:
- Multivariable calculus
- Matrices and their operations
- Eigenvectors and eigenvalues
- Probability distributions
- Statistical uncertainty (confidence intervals, prediction intervals, etc.)