Our lives have been transformed by automation and machine learning. From the most tech-savvy person working in leading digital platform companies like Google or Facebook to the most casual smartphone user, few have not been impacted by artificial intelligence or machine learning in some way. Whether through social media, smart banking, healthcare, or even Uber. From self-driving cars to robots, image recognition, diagnostic assessments, recommendation engines, photo tagging, fraud detection, and other applications, the future of machine learning training and AI is bright and full of untapped potential.
With so much innovation and ground-breaking ideas on the horizon, anyone remotely interested in futuristic technology may wish to pursue a career in machine learning. As a beginner, how can you learn about the latest technologies and the various fields that contribute to them? You may have heard of many cool-sounding job profiles such as Data Scientist, Data Analyst, Data Engineer, Machine Learning Engineer, and so on, which not only pay well but also allow you to grow as a developer and creator while working at some of the most successful technology companies of our time.
But where do you start if you wish to pursue a career in machine learning? What educational path should you take, and what skills should you learn? Machine learning is a big field that includes probability, statistics, computer science, and algorithms used to create intelligent applications. These applications are capable of extracting valuable and insightful information from data to arrive at business insights. Because artificial intelligence and machine learning courses are all about studying and applying algorithms, a solid foundation in mathematics is required.
Table of Contents
What is the Purpose of Learning Mathematics?
Math has become ingrained in our daily lives. Math is used in every aspect of our lives, from the time we wake up to the time we go to bed. However, you may be wondering how important math is in machine learning and whether and how it can be used to solve real-world business problems.
Whether you want to become a Machine Learning Engineer, Data Scientist or Data Analyst, your primary focus should be on “Mathematics.” Math is the fundamental building block for solving all business and data-driven applications in the real world. Math is used in almost every aspect of business, from analyzing an organization’s transactions to learning how to grow in the everyday market, making future stock predictions to predict future sales.
Math applications are used in many industries such as retail, manufacturing, and information technology to provide a company overview in terms of sales, production, goods intake, the wage paid, prediction of their level in the current market, and much more.
Foundation Pillars of Machine Learning
To get a head start and become acquainted with the most recent technologies such as Machine Learning training, Data Science, and Artificial Intelligence, we must first understand the fundamental concepts of Math, write our Algorithms, and implement existing Algorithms to solve a variety of real-world problems.
The majority of our real-world business problems are solved using these four pillars of Machine Learning. They are:
This is a branch of mathematics that teaches the rates of change of quantities. It is concerned with improving the performance of machine learning algorithms or models. Understanding the calculus concepts, working on probabilities can be quite problematic. And getting all the possible outcomes from the provided data is not possible at all. Calculus focuses primarily on integrals, limits, derivatives, and functions. It is employed in backpropagation algorithms for the training of deep neural networks.
It is employed in the process of deriving conclusions from data. It is concerned with statistical methods of collecting, presenting, analyzing, and interpreting numerical data. Statistics are important in the field of Machine Learning because it deals with large amounts of data and is a key factor in an organization’s growth and development.
Linear Algebra is more concerned with computation. It is essential for understanding the underlying theory of machine learning and is also used for Deep Learning. It provides us with better insights into how the algorithms actually work in real life, allowing us to make better decisions.
The term probability refers to the occurrence of a specific event and the likelihood of that event occurring based on previous experiences. It is used in the field of Machine Learning to predict the likelihood of future events.
Dealing with data is central to machine learning. Generally, the data is collected from organizations or repositories such as Kaggle, UCI, and others, and then perform various operations on the dataset such as cleaning and processing the data, visualizing and predicting the data’s output. One common foundation helps us achieve all of this through computation for all of the operations we perform on data, and that is Math.
Mathematics is an important aspect for machine learning enthusiasts and aspirants to focus on during machine learning training, and it is critical to building a strong foundation in Math. Every concept you get to know in Machine Learning, every small algorithm you write or implement to solve a problem, directly or indirectly relates to Mathematics.
The math concepts used in machine learning are based on the fundamental math that we learn in 11th and 12th grades. At that point, we gain theoretical knowledge, but in the field of Machine Learning, we get to see the practical applications of the math we studied earlier.
If you want to have practical experience with mathematical concepts, take a machine learning algorithm use case. Work on it by understanding the math in it. While you are working on it by utilizing your existing knowledge, it helps in testing your skills.
Understanding math is essential for developing machine learning solutions to real-world problems. A solid understanding of math concepts also aids in the development of problem-solving abilities. These abilities play a vital role in the business world and an organization’s growth.