Ok now you know what Machine Learning, Data Science means let’s dive into it. But wait !,should we jump straight into the meat of ML?. I have done some courses on Machine Learning and Data mining, in college and through some MOOC’s (Massive Open Online Courses). What I observed was most instructor’s jump straight to the core of ML. And most students have no problem with it. They just want to learn the “core” algorithms and quickly apply it to some real world problem. There are many open-source projects out there (scikit-learn for python, mlpack for C++ etc.) from where you can just call the algorithm you want to apply to the data and it will build a model for you.
But I believe that is not a correct way to learn Machine Learning. All successful Data Scientist’s have a PhD in this field. They have solid foundation in the subject, that’s why they are so successful in this field. So for becoming a successful Data Scientist you need to have solid understanding of Linear Algebra, Probability theory, Statistics, Computer Science (Relational Database, One Programming Language, Standard textbook algorithms, Algorithm Complexity Theory), and Optimization methods.
It’s OK if you are not an expert in these area, but basic knowledge of these subjects is essential to be able to apply Data Science to some real world problem.
Note that I’ll be using Machine Learning and Data Science interchangeably as for now you can assume both of them to be same. I’ll explain the detailed difference between these two at the right time.
So as I mentioned that every week I’ll discuss something from Data Science and ML, this week I’ll cover basics which are pre-requisites for Machine Learning.
Till next time 🙂