Top 5 Artificial Intelligence Skills and How to Refine Yours

By Indeed Editorial Team

Published 6 May 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Artificial intelligence is becoming increasingly important due to substantial technological advancements. The extensive application of this technology has increased the demand for specialists in this field. If you're considering a career in artificial intelligence, learning about the applicable skills can give you an advantage as a candidate. In this article, we discuss various artificial intelligence skills that you can add to your resume to help increase your chances of securing a position in the industry and share how to improve them.

Top 5 artificial intelligence skills

Artificial intelligence skills are instrumental in helping you understand the artificial intelligence industry. Here are five skills you can acquire to increase your chance of securing a position in this field:

1. Programming language skills

Learning artificial intelligence-related programming languages can increase your likelihood of securing employment in the industry. Artificial intelligence algorithms use programming languages like Python, Golang and R. You can consider learning several of these to increase your expertise. Here are some tips for enhancing your programming language skills:

  • Read books on the topic: Although books can quickly become outdated due to constantly changing coding conventions, the basics of programming remain the same. You can use books on the relevant topic to refresh your programming knowledge and help improve your skills and expertise.

  • Join a community of programmers: Joining these types of communities allows you to ask questions to like-minded individuals and meet experts that can share their programming knowledge and advice. Besides asking questions, you can discover tips and tricks to increase your coding ability.

  • Contribute to open-source projects: You can improve your coding skills by making code contributions to open-source projects. This is also one of the best ways to challenge yourself.

  • Examine your favourite website: Another way to improve your programming skills is to examine your favourite website and deconstruct its individual components. You can review the source code to determine which languages and frameworks the programmer used and if you find something that you're unfamiliar with, try replicating it or ask a community of experts for a solution.

  • Participate in a project: You can also consider working on a code-intensive project since this can provide you with practical experience in the entire programming process, including ideation, deployment and maintenance. This can increase your coding ability since completing the project can teach you various new skills.

  • Practise new concepts: Improving your programming language proficiency requires constant practise. So, it's important to explore and practise new programming concepts as often as possible.

  • Engage in competitions: Competing typically requires you to set new limits for yourself. These competitions usually involve competing with expert programmers, which can significantly improve your programming skills.

Related: Technical Skills: Definitions and Examples

2. Mathematical skills

Since artificial intelligence is often mathematically intensive, you can consider improving your mathematical skills to advance your career and expertise. Creating complex rules for artificial intelligence models usually requires knowledge of mathematical concepts, such as algebra, probability and statistics. There are several ways to improve your mathematical skills, such as:

  • Teaching others: You can use teaching to develop your mathematical skills. When teaching others what you know, you often view this knowledge from a different perspective, allowing you to discover new mathematical concepts.

  • Creating realistic goals: When setting new goals to improve your mathematical skills, it's important that they're achievable. This increases your likelihood of achieving them, which can encourage you to aim even higher.

  • Playing mathematical games: You can improve your mathematical skills by browsing the internet to find the best mathematical games for your preferred topic. These games can also make learning more enjoyable and engaging while improving your skills when you play regularly.

  • Practising daily: Constant practise helps enhance your learning process. Practising your mathematical skills daily can help you easily understand the mechanics of a new concept. Some mathematical concepts may require more practise than others.

Related: Skills Test (Definition and Examples)

3. Deep learning

Deep learning is one of the most important artificial intelligence skills. It's a branch of data science used for predictive analysis and insight into natural occurrences. This skill is important for a successful career in artificial intelligence since emerging technologies often utilise this concept. As a result, many employers look for deep learning experts. You can consider participating in various deep learning projects to develop your skills in this area. These projects may include:

  • Exploring convolutional networks: A common belief is that it isn't possible to represent convolutional networks visually, but studies confirmed that this isn't entirely true and that it is achievable by constructing an image of the filter networks. You can try demonstrating this by embarking on a project involving convolutional networks and publishing your findings visually.

  • Building an API for your artificial intelligence model: Another way to improve your deep learning skills is to create an API for the production of your artificial intelligence model. This can significantly benefit the users since it's no longer necessary for them to identify your model framework or format their input to correlate with your system.

  • Examining research results: You can also examine the results of research papers and replicate them to produce a similar effect by following the researchers' steps. This can improve your deep learning skills because it helps you understand more advanced topics and, if you're observant, even develop a dissertation.

  • Depending less on frameworks: Frameworks are great ways to build a solution rapidly, as they help you focus on developing your ideas, especially if you're not familiar with creating these projects from the beginning. Consider reducing your dependence on frameworks during your next project to improve your deep learning skills.

  • Contributing to open-source projects: You can also contribute to open-source projects, such as PyTorch and scikit-learn. Participating in these projects can increase your experience with in-demand open-source platforms while helping you to improve your deep learning skills.

4. Big data analysis

Companies often require big data analysis to assess complicated problems. Proficiency in this skill can be a highly beneficial addition to your resume. Organisations typically use big data for complex predictive analysis. It involves extracting information from a large dataset using powerful machines and algorithms. Since organisations typically use big data to make important business decisions, they usually require skilled experts for this task. To improve your big data skills, you can consider enhancing your expertise in the following areas:

  • Computational frameworks: Apache Spark and Apache Flink are computational frameworks for streamlining extensive data analysis. Since big data analysis typically relies on these technologies, it's important to develop your skills in this area to improve your analytical ability.

  • Data warehousing: Data warehousing is a concept that helps improve the speed and efficiency of accessing different data sets. It can also help simplify a company's decision-making processes.

  • Data visualisation: It's also important to improve your knowledge of data visualisation to improve your big data analysis skills, as you often require this skill when working at a large corporation or reporting to a superior. Data visualisation helps communicate your work and ideas with non-experts.

  • Domain expertise: Domain expertise is important since you typically use this knowledge to validate and evaluate your data. It's also a highly recommended skill for big data analysts.

Related: Top 10 Careers in Data Science (Job Types and Salaries)

5. Signal processing skills

Employers often look for proficiency in this skill in potential employees. Signal processing involves machine learning to extract features by turning pixel values into meaningful information. It's applicable in speech recognition and video/image processing. To improve your signal processing skills, you can consider reading books on the topic, joining a community of experts and practising regularly. You can also improve your skills in the following areas:

  • Real calculus: Since some projects may require you to represent signals in mathematical equations, it's important to develop your knowledge of real calculus. There are various online study materials and video tutorials that can help you feel confident with calculus.

  • Algebra: Sufficient knowledge of algebra is important if you plan to work as a digital signal processing engineer. You can apply algebra for pseudoinverse calculation, matrix approximation, multi-variable control and rank determination.

  • Probability and statistics: You typically require adequate knowledge to analyse random signals. The machine learning technique ideally relies on probability and statistics, and this knowledge may be helpful across multiple domains during your artificial intelligence career.

  • Complex calculus: You usually require complex calculus knowledge to solve signal processing equations that may not be possible using real calculus. It helps simplify equations and provide solutions where real numbers are insufficient.

  • Fourier techniques: Signal processing often includes the use of Fourier techniques. You can consider improving your skills in the four Fourier methods, which comprise Fourier transform, discrete-time Fourier transform, discrete Fourier transform and short-time Fourier transform.

Please note that none of the companies, institutions or organisations mentioned in this article are affiliated with Indeed.

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