Our society is going through various technological changes. In just a couple of decades, it will be not quite the same as what it is today. One significant factor that impacts and transforms various aspects of everyday life is the vast expansion of artificial intelligence and machine learning.
There are not many who have not been affected by artificial intelligence or machine learning in some form or the other; through social media, smart banking, healthcare or even Uber. According to a study, 75% of what people watch on Netflix is recommended by AI and ML.
For what reason do I need to Learn Math?
Math is used in physics to describe the motion of planets to the motion of electrons in particles. Math is used in economics to describe the fluctuations in the stock markets. Math is used in computer science to demonstrate the correctness of programs. Mathematics gives us a powerful answer, in the form of minimization methodology and back-propagation, which have been known freely for quite a time. The language of math is very precise.
But why is it so precise? Well, in the beginning, when individuals needed to keep track of things they started counting and soon they recorded them with their own symbols, which we presently refer to as numbers. This was the birth of math.
We have to understand the fundamental ideas of Maths, write our own Algorithms, and execute existing Algorithms to solve many real-world issues. There are four pillars of Machine Learning, in which a large portion of our genuine problems is addressed.
Statistics- It is addressed in reaching conclusions from data. It manages the statistical strategies of gathering, introducing, investigating and interpreting the Numerical data.
Probability- In the field of Machine Learning, it is utilized in predicting the likelihood of future occasions, based on old experiences.
Calculus- It deals with optimizing the performance of machine learning models or Algorithms. Without understanding calculus, it is difficult to process probabilities on the data and we cannot draw the potential results from the data we take.
Linear Algebra- It plays an important role in knowing the background theory behind Machine learning and helps in making better choices.
My views about it
For machine learning enthusiasts and aspirants, mathematics is a crucial perspective to focus on, and it is essential to build a strong foundation in Math. Every single idea you learn in Machine Learning, each small algorithm you compose or implement in solving a problem directly or indirectly has a connection to Mathematics. It is the theoretical knowledge that we acquire at a previous stage, but in the area of Machine Learning, we experience the practical use cases of math that we have studied earlier.
The most ideal approach to get comfortable with the ideas of Mathematics is to take a Machine Learning Algorithm, discover a use case, and solve and understand the math behind it.
Conclusion
Mathematical statistics permit us to discover connections in a large amount of data and distinguish random coincidences from patterns. And the optimization theory allows us to quickly choose such setups of parameters of a complex system for which the working of this system is appropriate for us. Machine learning is a field that combines probability, statistics, computer science and algorithms that are utilized to make intelligent applications. These applications have the ability to gather useful and insightful data from data that is valuable to arrive at business insights. Since machine learning is all about the study and use of algorithms, it is important that you have a base in mathematics.
