Making computers perform intelligent tasks without explicitly programming them to do so is the goal of machine learning, and this is accomplished by giving the computer a lot of data to learn from. Machine learning can identify spam in emails, read handwritten digits, spot fraud in transactions, and more. When creating machine learning models, the fundamental ideas of math and statistics can actually be very helpful in guiding strategic decisions. So, if you plan to pursue a career in data science, you need to start appreciating math concepts and using them in your daily life because this is a requirement for machine learning.1 

The Connection Between Math and Machine Learning

Math is a fundamental component of machine learning, which aids in developing an algorithm that can learn from data and make precise predictions. A straightforward prediction might involve categorizing dogs and cats from a set of images or advising a customer on what products to buy based on previous purchases. Therefore, it is crucial to fully comprehend the mathematical principles underlying any major machine learning algorithm. It assists you in selecting the appropriate algorithms for your project in data science and machine learning in this way. Since machine learning is largely based on mathematical principles, it will be more interesting if you can understand why the math is used. With this, you’ll comprehend the reasons behind our selection of one machine learning algorithm over another, as well as how it impacts the effectiveness of the machine learning model.1

So, What Kind of Math is Required in Machine Learning?

The amount and level of math required to comprehend these techniques is the main concern when attempting to understand an interdisciplinary field like machine learning. Depending on the individual’s level and interests, the answer to this question is complex. There is ongoing research into mathematical formulations and the theoretical development of machine learning, and some researchers are developing more sophisticated methods. 

The four key ideas—statistics, linear algebra, probability theory, and calculus—are the foundation of machine learning. Calculus aids in model learning and optimization, even though statistical concepts are the foundation of every model. When working with a large dataset, linear algebra is especially helpful, and probability aids in the prognostication of future events. These are the mathematical ideas that you will frequently run into in your career in data science and machine learning.2 1

  • Statistics: Statistics and machine learning are closely related fields. You must be familiar with probability distributions, sampling, hypothesis testing, regression, and decision analysis. You also need to be familiar with descriptive statistics, combinatorics, axioms, Bayes rule, random variables, and so on. Simply put, statistics is the primary branch of math used in machine learning. 
  • Linear Algebra: The development of central machine learning algorithms requires a fundamental understanding of how to build linear equations. These will be employed to assess and monitor data collection efforts. Understanding matrices and their fundamental operations, such as addition, subtraction, scalar and vector multiplication, inverse operations, transposition, and vector spaces, is essential for working with matrices in linear algebra.
  • Calculus: Many students who did not enjoy learning calculus in school will be in for a rude awakening because it is a crucial component of machine learning. Fortunately, you might not need to be an expert in calculus; rather, it’s important to learn and comprehend the fundamentals of calculus. Additionally, you must comprehend calculus in order to build models while understanding the practical applications of machine learning.
  • Probability: Probability is necessary in order to complete a machine learning predictive modeling project successfully, it would seem. The process of building prediction models from ambiguous data is known as machine learning. Uncertainty requires working with inaccurate or insufficient information. Although uncertainty is essential to machine learning, it is also one of the elements that presents the greatest challenges to newcomers, especially those with a background in programming. There are three main sources of uncertainty in machine learning: noisy data, scant coverage of the issue, and naturally flawed models. However, we can estimate the answer to the problem with the aid of the appropriate probability tools.

This blog attempts to give a well-intentioned explanation about the value of math in machine learning, as well as the necessary subjects. The post may be discouraging to some machine learning enthusiasts who are weak in math, but beginners don’t need a lot of math to get started with machine learning. The basic prerequisites are explained in this blog post, and as you gain proficiency in more methods and algorithms, you can pick up the math on the fly. To learn more about the types of coding and languages required for machine learning, read the blog post What Type of Coding is Required for Machine Learning.  More interesting articles about math and coding can be found on BYJU’S FutureSchool Blog.

References:

  1. Mathematics For Machine Learning |Mathematics for Data Science. (n.d.). Retrieved July 14, 2022, from https://www.analyticsvidhya.com/blog/2021/06/how-to-learn-mathematics-for-machine-learning-what-concepts-do-you-need-to-master-in-data-science/ 

5 Must-Have Skills to Become a Machine Learning Engineer – DZone AI. (n.d.). Retrieved July 14, 2022, from https://dzone.com/articles/5-must-have-skills-to-become-machine-learning-engi

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