Why Data Analysis and Not Math Is A Prerequisite For Machine Learning

Machine Learning is a very intriguing area. If you are a beginner, you might even wonder how can a machine learn something? Well, if you are really curious to explore the depths of machine learning then your math basics like Linear Algebra must be strong but as an entry level data scientist you would be presented with data cleaning and structuring more often than using the real math.

What is Data Analysis and why do we use it in Machine Learning?

Well, for one you first need to obtain and collect the data in whatever format it is available and store it some data base server. The next part that is structuring the data is very important. Also, it is not enough to just clean and structure the data you will be asked to graphically show the results of your structuring which means data visualization is also a major part in your machine learning career. If you are not sure where to start, data analysis is the best way to kick start your learning process.

Don’t think that just knowing how to analyze the data is enough to get going!

Machine learning is as complicated as it sounds. It requires enormous amounts of computing power as well as smart algorithms which can effectively cut down the delay time. If you are somewhat not sure about your mathematics basics in linear algebra or probability you may want to revisit your text books. As an absolute beginner to understand the concept of machine learning data analysis is required. Though you use mathematical expressions all throughout your machine learning career, with out the knowledge of data analysis everything will look obscure. So, once a person has gained confidence in data analysis they can move on to learning the basic and advanced concepts of probability, statistics and linear algebra.

Most of the existing algorithms are devised based on these concepts. To make your own machine learning design it will take time because of sheer complexity and lack of experience in the field for a beginner. The very commonly used languages in Machine Learning are R and Python. If you have heard about R, you may know that it is primarily used to model mathematical problems in statistics. Python is a versatile language capable of handling Machine Learning algorithms very well. You can never pick one language over other or one platform over other since everything has its own purpose.

If you are starting your career as a Machine Learning engineer, 80% of your work load would to clean the data and structure it. Then comes the most difficult and exciting part – application or designing of ML algorithms. You may also want to familiarize yourself with graph theory and optimization techniques before you start anything. Optimization techniques would be of paramount importance in writing and developing efficient codes for Machine Learning models!

Machine Learning has been around for a while now and it is slowly grasping the reign of technologies available today. It is a very great field to work and it is equally complicated and tiring.


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