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Why Data Structures are Needed in Machine Leading Projects
Why Data Structures are Needed in Machine Leading Projects

November 29, 2022

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Machine learning is one of the hottest technologies utilized by data scientists or ML professionals to launch a real-time project. To solve problems in the real world and create better products, you need more than just machine learning skills; you also need to become well-versed in data structures and algorithms.

 

The data structure employed in machine learning is relatively comparable to those employed in other software development fields. A branch of artificial intelligence known as "machine learning" uses various sophisticated algorithms to solve mathematical issues substantially. Data structure aids in the creation and comprehension of these challenging issues. You can create machine learning models and algorithms more quickly and effectively than ML experts if you understand the data structure. 

 

We will explore several data structure ideas utilized in machine learning and the connection between data structure and ML in this topic, "Data Structure for Machine Learning." Let's begin by briefly introducing data structures and machine learning.

Data Structure: What Is It?

The definition of a data structure is a fundamental building block in computer programming that aids in managing, organizing, and storing data to facilitate effective search and retrieval.

 

In other words, a data structure is a group of data type "values" that have been arranged and stored to facilitate quick access and modification.

Data Structure Types

The data structure is an ordered list of data that instructs the compiler how to use the data, such as integers, strings, booleans, etc.

 

Data structures can be divided into two categories: linear and nonlinear.

 

  1. Linear Data Structure: 

A unique kind of data structure known as a linear data structure aids in managing and organizing data in a particular order where the pieces are attached next to one another.

 

Following are the four primary types of linear data structures:

 

  • Array

The array is one of the most fundamental and often used data structures in machine learning. In order to tackle challenging mathematical issues, it is also employed in linear algebra. In machine learning, you will frequently employ arrays, whether it be:

 

  • To change a data frame's column into a list format during pre-processing analysis
  • To arrange the word frequency in datasets.
  • Grouping topics by starting with a list of tokenized words
  • By building multi-dimensional matrices for word embedding

 

An array of index numbers, starting at 0, represent each element. The first element is represented by arr[0], the lowest index.

 

Let's look at a machine-learning example using a Python array. 

Although Python's array differs significantly from arrays in other programming languages, the Python list is more widely used because it allows for greater data types and length flexibility. If somebody is utilizing Python for machine learning techniques, starting your journey from an array is best.

  • Stacks: 

The LIFO (Last in, First out) or FILO principle is the foundation of stacks (First In, Last Out). Deep learning employs it for binary classification. Although stacks are simple to understand and apply in ML models, a solid understanding can be beneficial in many areas of computer science, like parsing grammar.

 

Because they act like a stack of blog posts, stacks enable your computer's undo and redo buttons. Adding a blog to the bottom of the list is pointless. However, we are only able to check the most recent addition. At the top of the stack, addition and subtraction take place.

 

  • Linked List:

 A linked list is a collection of nodes that have been allocated in various ways. Or, to put it another way, a list is a particular kind of grouping of data elements consisting of a value and a pointer that points to the next node in the list.

 

  • Queue: 

The "FIFO" definition of a queue (first in, first out). In real-time programs, it is important to foresee a queuing scenario, such as individuals lining up to take money from a bank. As a result, the queue is important in a program that needs to process multiple lists of codes.

  1. Narrow Data Structures (Non-Linear)

As the name implies, elements in non-linear data structures are not organized in any particular order. In a hierarchical arrangement, all elements are connected to one another; one element may be connected to another.

 

  • Binary Trees

A linked list and a binary tree have extremely similar concepts; the main distinction is in the nodes and their pointers. While each node in a binary tree has two pointers to succeeding nodes instead of just one, each node in a linked list only has one pointer to the node after it in the list.

  • Charts

In machine learning, a graph data structure is extremely helpful for predicting links. Graphs are concepts containing nodes and ordered or unordered pairs which can be directed or undirected. As a result, you need to be familiar with the graph data structure for deep learning and machine learning.

  • Maps

In the realm of programming, maps are a common data structure that is most helpful for reducing run-time algorithms and quick data searches. Data is stored as a (key, value) pair, where the key needs to be distinct, but the value can be replicated. It is called a map because each key maps to a value.

  • Heap data structure: 

Heap is a data structure that is arranged hierarchically. A heap data structure is very similar to a tree in that there is no horizontal ordering; instead, there is vertical ordering.

 

 


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