Machine learning and Data structures & algorithms are the two most popular concepts in computer science. In machine learning, we provide our machines with the ability to learn patterns from historical data. While on the other hand, Data structures are the concept used to store data efficiently and write optimized computer programs.

While interviewing many aspirants for the deep learning and machine learning domain, we figured out that many interviewees think the DS algorithm is not required in machine learning interviews. But the truth is just the opposite! In this article, we will discuss five critical reasons to learn Data Structure and Algorithms for Data Science, Machine Learning, and Deep Learning.

If real systems did not perform in real-time, the outcome would be disastrous; hence, industries exploring Machine Learning technology are highly concerned about the real-time performance of machine learning algorithms. For example, suppose you want to solve an object detection problem using machine learning algorithms.

For real-time performance, let’s say 15 Frames of images should be executed every second, i.e., 15 FPS, but your algorithm gives you only 10 FPS. Because of that, prediction can be seen as laggy, which could be a bad user experience. So algorithms written with the knowledge of algorithm analysis can increase the performance from 10 FPS to 15 FPS, making your object detection algorithm work in real-time.

Edge devices like Arduino and raspberry-pi are widely used IoT (Internet of things ) devices to integrate our code with real systems or machines. Because of the auspicious nature of ML algorithms, industries are leaning toward such technology. But most of the solutions are heavy to be deployed on any edge device. Various companies like Facebook, Google, and Deeplite. etc., are working to reduce ML algorithms' complexity. So knowledge of data structure and algorithms can allow you to write efficient code that can easily be deployed on IoT devices and become helpful in machine learning production.

While working on the real problem statements, there may be situations where you will find that none of the libraries are helping you out with your problem statement. For example, suppose we need to find the product of two matrices. But if the product of two elements in that matrix multiplication crosses a certain threshold value, we need to terminate the process and discard those matrix pairs.

One possibility is to use pre-existing libraries, perform complete matrix multiplication, and compare the new matrix entries with the threshold value. But if input matrices sizes are large, it will possibly take higher computation.

Another possibility would be to use DS Algo knowledge and implement a matrix multiplication solution with less time complexity. Thus, it will save a considerable amount of computational cost. Another example can also be related to IoT devices. Suppose you want to deploy your code where you have used one signal filtering library, say Scipy. As a library, scipy contains several other functions also, and as a consequence, it may take up significant space on your edge device, and you can not afford that much space for just one library. Then, in that case, you can also build an optimal algorithm that will not need the parent library.

Many learners treat ML algorithms as a black box: feed input data to algorithms, and it will produce the output. They have mastered the art of using various algorithms for different problem statements. But, what if we need some non-conventional approach to solve any new problem? So considering ML algorithms as a black box may not help in such scenarios.

So one of the best ideas would also be to learn How ML algorithms work? It can give us broad control over problems and provide additional insights to design new solutions. To know the principles behind the working of these algorithms, DS and Algo knowledge is a must. For example, one famous ML algorithm, Decision Tree, is a version of the tree data structure.

Interviewees love to ask DS algo concepts for any computer science-related role. This is not an exception in the field of Machine Learning. Knowing algorithms shows that you can think around any problem and provide the best optimal solution. In addition, it shows your strength of problem-solving skills. So this can give you an added advantage if you appear or planning to appear in a machine learning interview.

There may be several other reasons you can find somewhere, but we tried to give you some real-life examples where we need the knowledge of Data Structures & Algorithms.

**Popular data structure and algorithms used in machine learning and deep learning:** Array, vectors, matrices, Linked list, Binary trees, Graph, Stack, Queue, Hashing, Set, Dynamic Programming, Greedy Algorithms, Randomized Algorithms, etc.

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