There is always a question in enthusiast learners that what is the need of mathematics in machine learning? As computers can solve mathematics problems faster than humans. Why to learn Mathematics for Machine Learning?
Note: It is not required to go deep in learning Mathematics for working with simple machine learning models rather, knowing essential Maths concepts is enough to understand how it is applied in ML. In this topic, we will learn all the essential concepts of Mathematics that are used in Machine Learning. It is also very helpful to learn the concepts behind this. But with the growth of machine learning, people are getting motivated to learn mathematics as it is directly used in designing ML algorithms. Many students don't find interest in mathematics around the globe as they think that topics covered in mathematics are less or not relevant to practical or real-world problems. Mathematics has always been a good friend for some people and a phobia or anxiety for some people. Therefore, it is very important to understand the Maths before going into the deep understanding of ML algorithms. Mathematics is the core of designing ML algorithms that can automatically learn from data and make predictions. But here, an important question arises: what is the magic behind ML, and the answer is mathematics.
It is about developing models that can automatically extract important information and patterns from data. It is being widely used in almost all areas of the real world, from Google Assistant to self-driving cars.
Nowadays, machine learning is one of the most trending technologies among researchers, industries and enthusiastic learners because of making human life easier. Next → ← prev Essential Mathematics for Machine Learning | Important concepts of Mathematics for Machine Learning
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