The two most well-known concepts in computer science are machine learning and data structures and algorithms (DSA). We allow our machines to recognize patterns from past data using machine learning. On the other hand, data structures are a notion used to store data effectively and create optimal computer programs.
We learned through interviewing many candidates for positions in deep learning and machine learning fields that many believed the DS method was optional for machine learning interviews. The contrary, however, is actually true! In this post, we'll go through five compelling reasons for learning data structures and algorithms in the context of data science, machine learning, and deep learning.
The results would be terrible if real systems did not operate in real time. Hence industries investigating machine learning technology are very concerned about the real-time performance of machine learning algorithms. Consider the scenario where you wish to use machine learning algorithms to tackle an object detection issue.
Let's imagine that for real-time performance, 15 frames of photos should be processed per second or 15 FPS, but your algorithm only produces 10 FPS. As a result, the prediction may be perceived as sluggish, leading to a poor user experience. As a result, algorithms created using algorithm analysis knowledge can improve performance from 10 FPS to 15 FPS, enabling your object detection algorithm to operate in real time.
In order to interface our code with actual systems or machines, edge devices like Arduino and Raspberry Pi have often utilized IoT (Internet of things) devices. Industries are inclining toward such technologies because ML algorithms are fortunate in nature. However, most of the solutions could be more convenient to deploy on any edge device. Many businesses, including Facebook and Google, are reducing the complexity of ML algorithms. The ability to build effective code that can be quickly deployed on IoT devices and be helpful in machine learning is thus made possible by understanding data structures and algorithms. To learn how DSA is essential for machine learning techniques, visit the DSA course offered by Learnbay. They also offer system design and machine learning courses for aspirants.
There may be times when, as you work on the actual problem statements, you discover that none of the libraries is able to assist you. Consider the situation when we need to determine the product of two matrices. However, we must stop the operation and throw away those matrix pairings if the product of two elements in that matrix multiplication exceeds a specific threshold value.
A few options are using pre-existing libraries, full matrix multiplication, and a threshold value comparison to compare the new matrix entries. However, it may require more work if the input matrices are enormous.
A less time-intensive option would be to use your understanding of DS Algo to create a matrix multiplication solution. As a result, it will reduce the cost of computation significantly. IoT devices can also be used in another illustration. Consider deploying your code to a location where you have utilized Scipy, a signal-filtering library. Scipy is a library, but it also has a lot of other capabilities so that it can take up a lot of room on your edge device—and you can't afford that much room for just one library. Then, if that is the case, you can also create an ideal algorithm without the need for the parent library.
Many students view machine learning (ML) algorithms as black boxes that create results when given input data. They are experts at selecting the correct algorithm for each unique issue statement. But what if we require a novel strategy to tackle a brand-new issue? Therefore, treating ML algorithms as a black box might not be helpful in certain situations.
So learning how machine learning algorithms operate would also be one of the finest concepts. It might give us extensive control over issues and extra knowledge to create new remedies. Knowledge of DSA is required to understand the underlying concepts governing how these algorithms function. For instance, a well-known ML technique like Decision Tree uses a tree data structure.
Interviewees adore asking about DSA principles for any position involving computer science. In terms of machine learning, this is not an exception. Knowing algorithms demonstrates your ability to approach any issue from all angles and offer the most effective answer. Additionally, it proves your superior problem-solving skills. Therefore, if you participate in or plan to participate in a machine learning or data science interview, this can provide you with a distinct advantage. So begin upgrading your DSA skills by registering in a comprehensive data structures and algorithms course right away and ace challenging tech interviews.