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Machine learning is all about maths, which in turn helps in creating an algorithm that can learn from data to make an accurate prediction. The prediction could be as simple as classifying dogs or cats from a given set of pictures or what kind of products to recommend to a customer based on past purchases. Hence, it is very important to properly understand the maths concepts behind any central machine learning algorithm. This way, it helps you pick all the right algorithms for your project in data science and machine learning.
Machine learning is primarily built on mathematical prerequisites so as long as you can understand why the maths is used, you will find it more interesting. With this, you will understand why we pick one machine learning algorithm over the other and how it affects the performance of the machine learning model.
No matter what kind of love-hate kind of relationship you had with maths back in school. The core concepts used in Maths and Statistics are actually very useful to make strategic decisions while designing machine learning models. So, if you have decided to choose this career path in the field of data science, you need to start loving the concepts of maths and implement them in your future as it is one of the prerequisites for machine learning.
Machine learning is powered by four critical concepts and is Statistics, Linear Algebra, Probability, and Calculus. While statistical concepts are the core part of every model, calculus helps us learn and optimize a model. Linear algebra comes exceptionally handy when we are dealing with a huge dataset and probability helps in predicting the livelihood of events that will be occurring. These are the mathematical concepts that you will encounter in your data science and machine learning career quite frequently.