Essential Artificial Intelligence Skills

As the field of artificial intelligence (AI) continues to grow throughout the economy, companies in various industries need workers with the necessary skill set to contribute to this aspect of their businesses. To pursue a career in AI, you need to develop technical and workplace skills.

For the job's technical aspects, you may want to learn how to manage data and program software, utilize AI frameworks, and apply mathematics and statistics. Potential employers in AI may also expect you to foster workplace skills like communication, conflict resolution, problem-solving, and critical thinking. Discover the essential AI skills necessary for you to pursue a successful career in this field.

Core competencies in artificial intelligence

To obtain a position in AI and effectively do your job, you need certain core technological skills, such as a thorough understanding of machine learning, neural networks, and data processing. Take a closer look at these fundamental AI skills.

1. Understanding machine learning algorithms

Although machine learning (ML) is a subdiscipline of AI, it is extremely important, because its algorithms have contributed to many technological developments. For example, if you’re ever shocked when your device recommends a highly personalized song, movie, or search result, it’s because ML is working behind the scenes in applications like Spotify, Netflix, and Google. ML is also responsible for facial recognition during a visual search, speech recognition with Alexa and Siri, and fraud detection based on patterns in behavior.

2. Familiarity with neural networks

Because neural networks are a foundational component of AI, you need to develop an in-depth understanding of how they operate. Essentially, artificial neural networks (ANNs) are a tool for teaching computers how to refine and sort through data. ANNs attempt to mimic how the human brain works. Instead of actual neurons, the ANNs utilize artificial neurons, also known as nodes, which are interconnected units that transfer and process information.

Learn more about neural networks with the Deep Learning Specialization:

3. Knowledge of data preprocessing techniques

When working with AI and training an ML model, you will likely deal with large amounts of data. You’ll need to understand how to preprocess that data to prepare it for the ML algorithm to work with it effectively. You need to use high-quality data to train the ML algorithm, which you can achieve by applying data preprocessing strategies such as data reduction, data wrangling, data transformation, feature scaling, and feature selection.

Programming proficiency

Understanding programming languages is critical if you plan to work in AI. Three languages you may want to learn are Python, R, and Java. You’ll also need to develop a strong grasp of AI frameworks. 

1. Experience with Python, R, and Java

You’ll want to learn Python because it offers an extensive ecosystem for AI developers and is simpler to use than other languages. R provides tools for statistical analysis and data visualization, which are both important AI applications. These tools can lead to better decision-making for the organization. Finally, Java can help you with big AI systems because it is scalable and portable.

Read more: AI Programming Languages: What to Know

2. Familiarity with AI frameworks

Since AI frameworks are foundational components for producing advanced AI systems capable of learning, adapting, and progressing, understanding frameworks such as TensorFlow, PyTorch, and Keras could prove useful. To simplify the creation and implementation of an AI system, you need to use previously constructed functions and libraries available within an AI framework.

Data analysis and statistics

Employers also seek AI professionals with data analysis skills because these data-adept workers can gather and analyze data to gain valuable insights about the business. Knowledge of data analysis and statistics can potentially help you in your AI career.

Read more: AI in Analytics: Examples, Benefits, and Real-World Use Cases

1. Analyzing data sets for model training

Because many industries use ML to solve problems and make decisions, you’ll need to know how to train ML models with large data sets to increase the accuracy of the model. For your ML model to perform successfully, the data you use to train it must be high-quality, appropriate size, relevant, complete, clean, and diverse. 

2. Understanding statistical concepts

To create AI algorithms, you need a solid understanding of mathematics and statistics. Linear algebra and calculus are foundational pieces for neural networks. When you’re interpreting data and constructing predictive models, you will need probability and statistics.

3. Explaining complex AI concepts

When you’re an AI professional, you might need to discuss your work with non-technical people such as stakeholders or clients. To do this effectively, you need the ability to communicate complex ideas in an accessible manner to individuals without your knowledge of AI. 

4. Collaborating with data scientists and engineers

Since AI projects need contributions from data scientists, software engineers, and project managers, establishing a collaborative working relationship with all parties ensures a greater chance of success. If you can help foster a collaborative working environment, each of your colleagues will be more likely to offer their best work, making the AI project the best it can be.

How to gain more AI skills

If you’d like to develop your AI skills further, you have several options available such as certifications, online courses, or traditional degrees. Regarding degrees, you want to major in subjects like computer science, information technology, or data science. For online courses to increase your AI skill set, you might consider earning a credential like the IBM AI Developer Professional Certificate or the Generative AI for Software Development Skill Certificate offered by DeepLearning.AI.

For certifications, take a look at the following options:

• Google Cloud Certification: Professional Machine Learning Engineer

• Google Cloud Certification: Professional Data Engineer

• Artificial Intelligence Board of America (ARTiBA): AiE Certification

• Amazon: AWS Certified Machine Learning - Specialty

Advance your artificial intelligence skills on Coursera

If you’re hoping to advance your AI skills, you can focus on technical skills such as programming and data analysis, but remember to enhance certain workplace skills like communication and collaboration. To further develop your AI and interpersonal skill set, you can consider earning the AI for Business Specialization offered by the University of Pennsylvania or the Professional Skills for the Workplace Specialization provided by the University of California Davis.