If you have been following the development of AI projects in recent times, you would have understood that nothing moves without programming in Data Science and ML. Programming skills have become native to developing new-age machine learning and AI tools of the new era. Different types of programming languages are available as part of online courses that form the foundation of any subject discussion related to components of AI and machine learning. In the modern data science industry, both programming and machine learning skills are considered pivotal pillars or components of AI development.
So, when we are analyzing what are the components of Artificial intelligence, we should understand the different activities that are involved in giving the machines enough “intelligence” and data to accurately find the best possible answers based on programmable instructions.
Python, based on recent trends, supports a majority of Machine Learning projects. Even Google guides refer to Python as an essential programming language for Machine Learning, ably supported by all the online courses, libraries, and events organized to deliver the best end-to-end infrastructure for Artificial Intelligence components. Whether you are developing a supervised, semi-supervised or unsupervised machine learning model, Python will remain the choice for 90% of the programmers who think this programming language can solve all the problems related to the domain’s development in the future of AI.
So, what are the components of Artificial Intelligence that we can also label as pillars of machine learning development projects?
Let’s try to answer this using context from a modern application.
Common Data Structures
Like humans, AI also requires some level of training and learning before it can accomplish a task. This is accomplished through organizing data in a relevant structure and format. These are written rules based on lists, dictionaries, and libraries that provide some degree of functionality and logic to the flow of instructions. These common data structures are useful in building Machine Learning algorithms at a basic level. Common examples of such algorithms would be Searching, Sorting, Classification, and Recommendation Systems.
Commonly used data structures used in algorithms are mentioned below:
- Linked List
- Binary Trees
- Balance Trees
- Random Forest
- Stacks and Queues
- Custom neural networks
In a majority of the machine learning programs, the data type that is most commonly preferred I array. The other types could be included on top of arrays, thanks to the extensible functionality offered by array data structures.
So, an array is the basic component of AI algorithms.
An array is a collection of objects or defined items at an identifiable memory location. The purpose of working with array data structure is to ensure all data of the same type, or relationship are stacked at the same level, which could be further expanded into “Elements” and “Lengths”.
The best thing about working with arrays is that you can expand indefinitely with the help of a modern standard template library or STL, ensuring ML development becomes faster and more agile with quicker sorts and insertions.
So, what’s the next step to handling large data sets for AI?
Writing scripts that would allow the developers to work with a large volume of data in stacks or databases forms the next level of activities. This is followed by splicing through multi-dimensional arrays and transposing relevant information to generate scientific computing platforms for mapping and visualization to compare how data behaves and delivers results. You can either choose to work with Python, R or Numpy to check how data behaves when you manipulate or extricate data frames using mapping functions.
Without data visualization functionalities, machine learning developers could suffer massive failure in their projects. When professionals ask why data visualization, I often tell them that machine learning is the science, and plotting the results in a meaningful manner on the dashboard is the “art” of working with different components of Artificial Intelligence.
AI engineers prefer to use Matplotlib for data visualization as it allows a lot of different customizations when it comes to adding API flow, indexes, colors, and schemes. Advanced plotting can be created using Matplotlib.
So, AI components are inherently data-based and therefore require extra attention.