John P.

John P.

4 minute read

Mathematics Behind Machine Learning

Mathematics Behind Machine Learning

Mathematics behind Machine Learning: Core Concepts that you need to know

Machine Learning is the technique that is responsible for machines responding to a problem statement in a certain way. The art of building technologies that know how to respond in a given problem statement is called Machine Learning. It is one of the most trending topics today and is contributing to the vast growth of the world. It is responsible for a number of processes happening around us.

Machine Learning

The domains like Machine Learning, Data Mining, Data Analysis, and Artificial Intelligence are not just computer science-backed. These domains are very much a part of Mathematics as are of Computer Science. Going by machine learning, it is one such domain aimed at creating machines or technologies that are well-trained to respond in a particular situation on unknown datasets.

Machine learning models are created by training a dataset on a particular algorithm ( a set of instructions to perform any computing), evaluating the training model and then applying it on unknown datasets and values. These models are mostly of 3 types based on the type of algorithm applied: Supervised, Unsupervised and Reinforcement. Machine learning models are mostly used for predictive problems, classification and regression problems or neural networking. If you want to make career machine learning Python Training Institute provide Best Machine Learning Course in Delhi.

Mathematics Required for ML

The background processes involved in these techniques are completely mathematical and require knowledge and proper intuition to understand what’s happening exactly. Some of the most important Mathematics topics that any ML enthusiast should be aware of include linear algebra, statistics, probability, calculus (preferably multivariable) and optimization. Not many of us who want to explore the world of Artificial Intelligence and Machine learning might find so much of math a little intimidating and daunting but there is nothing to worry a lot. Just the basic knowledge of these topics with a proper in-depth understanding of their different areas is sufficient. This is because it is not expected by any data analyst to work on the mathematical areas on a pen and a paper. All these functions are already in-built in any of the data analysis modules which don’t require manual calculations. Yet it is advised to have the knowledge in order to understand and be aware of what processes are happening in the background of any computation.

Linear Algebra

Linear algebra is always one of the first topics to be taught to secondary students. We have always been in touch with linear algebra in some way or the other. Machine learning makes use of Linear algebra the most out of the other Math topics mentioned. Regression, working with matrices, Eigenvalues, and vectors, spaces, image processing, etc. are some areas where linear algebra always appears. Linear algebra is not a very difficult topic to brush-up if one has not been in touch with it off lately. The availability of resources online and offline makes it very easy to learn Machine Learning algorithms that are based on linear algebra.

Statistics

As it is not known to many, Statistics is that domain of Mathematics, Machine learning is based on. Statistics is the most important and complex part of the Mathematics that should be known by any ML enthusiast. Machine learning deals with a lot of data analysis tool that deals with expectations, distributions, and different functions. Statistics is not just useful for building models on algorithms, various parts of it are used for evaluation of the model using different metrics that make sure the model is precise and accurate.

Probability

Machine Learning deals with a lot of prediction stuff that is very helpful in business and stock analysis, weather reports, etc. These fields require a stronghold in the math behind prediction and classification that involves the knowledge of probability. Probability is not a very difficult topic but if one gets the knack of it, it becomes smooth.

Calculus

Integration, differentiation, complex calculus, distributions and testing are all parts of Calculus that form the backbone of Machine Learning. Calculus techniques are easy to understand and quite interesting. It makes learning ML easy and stronger.

Optimization

Data structures like hashing, trees, stacks, graphs, and concepts of dynamic programming, etc. help in understanding the concepts and different libraries of Machine learning easily. Graphs are a very convenient method of visualizing the data and have a complete idea of the data.

Conclusion 

These few topics of Mathematics are really important and should be known by any ML enthusiast who wishes to move forward in this field. Today’s world and the future are a gift of machines that are making our lives much easier and accessible. These topics should be memorized that everything can be well understood by the developer. It is a trending topic that appears to be a revolution about to change the world. It is going to rule the internet and make our lives easy. TGC India is one of the best institute which is provide best artificial intelligence, data analytics and Machine Learning Course in Delhi .