People rarely think about the application of linear algebra when they think of data science. In general, they think about, or specific subfields like computer vision, natural language processing, or machine learning.
The modern tools we use to perform data science algorithms do a good job. This is disguising the underlying math. And all these make everything function without ignoring linear algebra.
Most people avoid the application of linear algebra. The reason is it is tough or difficult to learn. While this is true! Linear algebra knowledge is still a necessary ability for data scientists and computer engineers.
But if someone says, “I can implement many data science and machine learning techniques without knowing the math!” While this is true to some extent. But it is important that you must understand the core mathematical concepts behind the algorithms. These offer you a different viewpoint on the method. It allows you to explore additional options.
So, check where the applications of linear algebra exist in the field of data science. But first get a quick overview of linear algebra.
Overview of linear algebra
Linear algebra is a branch of mathematics. It widely recognises as a necessary concept for a deeper understanding of machine learning or artificial intelligence.
Although linear algebra is a huge term with many useful ideas and results, machine learning practitioners can benefit from the different tools and notations developed in this discipline.
Moreover, it is feasible to focus on only the excellent or relevant elements of linear algebra if you have a firm understanding of what it is. So, before taking the course of ML or AI, just first get some knowledge of linear algebra to make your learning journey easier.
Different application of linear algebra in data science
- Computer vision
It is a branch of artificial intelligence (AI) that uses videos, pictures, and deep learning models. It uses to teach computers how to interpret and understand the visual environment. That is why algorithms can correctly detect and categorize items. Algorithms, in other words, learn to recognise visual data.
We have the application of linear algebra in computer vision in the field. It is like image recognition, as well as various image processing techniques. These techniques are like image convolution and picture representation as tensors. (And we call them in linear algebra, vectors).
Note: Data scientists commonly utilize the OpenCV library to conduct image convolution in Python. However, you may use NumPy to improve your skills by creating random pictures.
- Natural Language Processing
NLP is an area of artificial intelligence (AI). It is concerned with computer-human interaction using natural language, most often English. Speech recognition, chatBots, and text analysis are examples of NLP applications.
We are sure you’ve used a natural language processing program previously even without knowing. Have you used Grammarly or other text grammar editors before? What about Siri or Alexa, the digital assistants? They construct using NLP principles.
Word embeddings portray words as numerical vectors (application of linear algebra). This is by keeping their context on the page. These representations are created using a language modeling learning approach. It involves training various neural networks on a significant volume of text called a corpus.
Note: Word2vec is one of the most widely used word embedding methods.
- Machine Learning
Without a question, ML is the most well-known application of artificial intelligence (AI). Machine learning algorithms enable systems to learn and develop without the need for human intervention. It works by creating programs that access and analyze data (static or dynamic). It is in order to discover patterns and learn from them. Once the algorithm has discovered data linkages, it may apply what it has learned to new data sets.
Regularization, loss functions, support vector classification, and other application of linear algebra. All these may be found in machine learning.
Note: We’ll look at linear algebra in loss functions for our goals.
So far, we’ve learned that machine learning algorithms take data, analyze it, and develop a model using one of many methods ( logistic regression, linear regression, decision tree, random forest, etc.). They may then forecast future data queries based on the findings.
Loss functions are a way of determining how accurate your prediction models are. Will your model hold up against additional data sets? Your loss function will produce a greater value if your model is completely incorrect. The loss function, on the other hand, will provide a lower value if the model is good.
Modeling a link between a dependent variable, Y. And numerous independent variables, Xi’s, are called regression. You can try to fit a line in space on these variables after graphically representing them. And then use that line to forecast future Xi values.
There are many other types of loss functions. Some are more complex than others. Nevertheless, the two most usually employed are Mean Absolute Error and Mean Squared Error.
Let’s wrap it up!
Of course, there is still another application of linear algebra in data science domains. We could discuss them on another day. Linear algebra is employed in all fields of computer science. And it includes clustering algorithms, cybersecurity algorithms, and optimization algorithms. And it’s the only type of arithmetic required in quantum computing. But that’s another topic to discuss.
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