Top 20 Artificial Intelligence Skills Required: List of AI, ML Skills in 2023

Machines these days are demonstrating intelligent behavior. AI makes our computers smarter than they used to be. The computers of this age can understand and solve complex tasks and process them. Artificial intelligence has helped machines develop a sense of decision-making.

AI is gathering more attention every day. Thus, the requirement for skilled personnel in this field is also rising. This article will be helpful to all those who wish to make their mark in the field of artificial intelligence. Listed below are the top 20 AI skills suggested by experts for 2023.

The AI industry has grown to create many exceptional applications. With the advent of Siri, Alexa, and Google voice recognition, the requirement for AI experts has increased in the market. These experts need to be skilled in the following fields:

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Top 20 AI Skills By Experts: List

1. Machine Learning
2. Natural Language Process
3. Robotics
4. Deep Learning
5. Data Science
6. Programming Languages
7. Data Analysis
8. Statistics
9. Neural Network Architecture
10. TensorFlow
11. Big Data
12. Signal Processing Technique
13. Shell Scripting
14. Cluster Analysis
15. Creativity
16. Computing
17. Natural Language Generation
18. Computer Vision Proficiency
19. AI Ethics
20. Soft Skills

Top 20 AI Skills By Experts: Explained

Let us understand each of the above in detail below.

1. Machine learning (ML)

Machine learning is a sub-branch of Artificial Intelligence. With the help of Machine learning computers can remember the data collected and perform better with time. Three major concepts define machine learning:

1. Supervised Learning: These models can be created with the help of labeled data learning. Understanding this with the help of an example would be better. If you have used a smartphone, you may be aware of the feature to block calls or your phone asking you if the call was spam. If you store the information as yes, then the next time someone calls from that number or a similar number, the calls get blocked or remain unanswered. It happened because you once labeled the number as spam and added it to your blocked list.
2. Unsupervised Learning: This model uses predefined data to find patterns or correlations.
3. Reinforced Learning: Here, the AI model learns based on the environmental changes it faces. There are various algorithms used to enforce this type of action.
   Machine learning is an important skill as it helps AI observe and make intellectual decisions. It also helps AI modify itself to respond to its surroundings.

2. Natural Language Process (NLP)

This skill is a kind of AI engineer skill that helps the machine understand, analyze, and communicate with the language of humans. It aids in text and sentiment analysis and helps in language creation. It is the language bridge between a human and his machine.

Any person using NLP skills must be adept in tokenization, part of speech tagging, and named entity recognition.

3. Robotics

Robotics helps establish the link between Artificial Intelligence and engineering to create autonomously functional models. It uses AI techniques like object detection and languages like Java and C++.

4. Deep Learning:

Deep learning is a branch of machine learning that has recently gained popularity in the AI world. It aids the machines in solving complex problems and helps them in decision-making. It finds use in fraud detection, video games, medical diagnostics, etc.

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5. Data Science:

It includes data preprocessing skills, data visualization skills, and feature engineering for the development of AI models.

6. Programming Language:

Knowledge of programming languages is essential to working in the field of Artificial Intelligence. The languages used mainly are Python, R, and Java with C++. These languages help in game development, robotics, and statistical analysis of data.

7. Data Analysis:

Data analysis helps AI engineers understand the needs of consumers. It also helps keep track of the progress made in any AI model based on its performance. Business organizations need to analyze data to keep track of their profits as well. The process involves the following steps:

Data cleaning involves correcting inconsistent data.

Data Transformation: This involves engineering and encoding variables based on model requirements.

Data normalization: Scaling or normalizing the data set into a consistent range is called data normalization.

8. Statistics:

We need to create machines that learn from their experience. With the help of reasoning and logic, it can be done. AI engineers need to program the AI machine to understand and reason. Here’s where statistics and math come into the picture. Statistics helps the machines collect and analyse data providing it with tools to understand it. 

9. Neural Network Architecture:

Neural network architecture is similar to the human brain taking inputs from the external environment and processing the information to generate a result. The consequences are systematic. Three types of neural networks are involved in the deep learning process:

Input layer– Gather information to process

Hidden layer– Complex input data gets isolated through this technique

Output layer– Data gathered from hidden layers is classified

10. TensorFlow:

It is an open-source machine learning platform available to AI beginners. It provides them with flexible tools and libraries to create complex MI applications. This skill, if possessed, makes it very easy for AI beginners to do well in the field.

11. Big Data:

This technology involves the extraction and management of large amounts of data in a single go. The data requires heavy computation resources and comes with excellent statistical usage that can provide the engineer with behavior analysis, predictive analysis, and many other analytical requirements for the AI machine.

Read Also: What is ChatGPT and How to use this AI tool?

12. Signal Processing Technique:

It is a greatly desired skill by various organizations running the AI business. Since machine learning involves the transformation of pixel values into meaningful information using a feature extraction process, the engineer needs to be familiar with signal processing techniques to achieve it. Signal processing techniques include time-frequency analysis, convolution, Fourier analysis, etc. 

13. Shell Scripting:

Shell scripting is a computer program run by a command-line interpreter known as a Unix shell. This language is used for file manipulation, text printing, and program execution. However, the most useful aspect of this technique is pattern recognition.

14. Cluster Analysis:

This skill involves grouping similar objects in a group. It uses algorithms that differ in terms of their components and their ability to be found. This skill finds wide usage in various sectors and is thus the need of the hour for various AI aspirants.

15. Creativity:

This is the major requirement for all AI beginners and those who wish to make their career in artificial intelligence. You must be creative in your approach if you wish to design useful machines. Also, since the field requires innovation at all times, any AI professional needs to be creative.

16. Computing:

Any AI professional should be proficient in cloud platforms. He or she should know about AWS, Azure, or Google Cloud to deploy their AI skills.

17. Natural Language Generation:

This is the ability to create human-like text content using AI, which any AI professional should be familiar with. This technique enables the machines to mimic human language and also easily interpret it.

18. Computer Vision Proficiency:

This skill helps the AI engineer develop machines that elucidate and understand visual statistics from images or videos. This skill requires:

1. Object detection
2. Image segmentation
3. Facial recognition

The two major frameworks used for computer vision are TensorFlow, PyTorch, and OpenCV.

19. AI Ethics:

The AI developer should be aware of AI ethics at all times. As AI algorithms produce results based on information stored at times, it may bring out unfair results that may cause bias or discrimination. An efficient and fair AI engineer would always ensure the absence of these biases in the data. 

For example, if any data misinterprets information on those who lost their lives in diamond mining in Africa, it would be a biased data set.

20. Soft Skills:

Skills such as communication and teamwork are required in every field these days. If one has the ability to communicate clearly and with accuracy, it becomes easier for both parties to execute a task smoothly. In the field of AI, you have to bring your ideas to the table in front of everyone, which requires you to have good communication skills.

The future of AI is brighter than ever, as per various hiring platforms like LinkedIn. The pandemic gave a boost to this industry, affecting many others negatively. So now is the time to hone these skills and jump into the world of AI. We hope this article will be helpful for your AI career. 

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