How to Learn AI

Artificial intelligence (AI) is a transformative field impacting industries such as health care, finance, and manufacturing. AI has evolved immensely since its first iteration, going from implementing basic repetitive tasks to comprehending and interpreting mass amounts of data. Explore how to effectively learn AI, implement the key technologies within AI, and discover more about career opportunities within the field.

Understanding AI fundamentals

AI tools are capable of various tasks and generating diverse outputs. AI can enhance its performance by drawing insights and learning from large sets of data. 

What is AI?

AI is a technology that allows machines and computers to replicate human decision-making, problem-solving, learning, and creativity. AI has transformed various industries with its vast capabilities and potential. 

Types of AI

Artificial narrow intelligence is the only form of AI that exists today. General AI and super AI are future iterations of AI that have not yet been invented but, if achieved, are predicted to be the future of the industry. 

• Artificial narrow AI: Narrow AI can perform a single task faster and more efficiently than a human through model training. ChatGPT, Amazon’s Alexa, and Siri are all examples of narrow AI. 

• General AI: This form of AI, also known as artificial general intelligence (AGI), has not been invented yet, and today is only a theoretical concept. AGI will not require human training and could perform tasks efficiently based on past data across different tasks, learning new tasks the same way a human does. 

• Super AI: This form of AI, also known as artificial superintelligence, also doesn’t exist today. It’s a prediction of where AI may end up as it evolves. Super AI will learn, think, reason, make decisions, and retain cognitive abilities that surpass that of a human. These advanced abilities may help it evolve in a way that affects our view of intelligence. 

Key technologies 

AI uses these technologies: 

• Machine learning (ML): ML is a type of model that’s trained to make predictions and decisions based on data. Artificial neural networks are one of the most prevalent form of ML. Neural networks simulate the workings of a human brain, consisting of nodes that connect and work together to analyze data. 

• Deep learning: This is a form of an ML model that utilizes multilayered neural networks (deep neural networks) to replicate a human’s complex decision-making. This form of ML can extract information from data and make predictions without human intervention. 

• Generative AI (genAI): GenAI is a deep learning model that can develop unique, complex content when responding to a user’s input. This content could include text, images, audio, video, and more. 

Prerequisites and skills

Coding is the first step to understanding how AI works. Programming languages and mathematical foundations are the basis of machine learning. 

Essential AI programming languages

Some of the essential programming languages for AI include Python, R, and Java. 

Python is a user-friendly coding language with simple syntax. You can use this programming language on various operating systems, and it’s adaptable to any ML task. 

R is an excellent programming language to utilize for statistical analysis and data exploration. This language is for uncovering patterns in data and extracting insights from model outputs. 

Java is an adaptable programming language that is best for building AI infrastructure. It’s open-source and utilized for machine learning and data science like Python, although it may require more technical expertise. 

Mathematical foundations

Mathematical concepts are important to understand in order to excel and apply what you have learned when developing ML models. Mathematical foundations you should learn include: 

• Linear algebra: Helps you understand machine learning and data science 

• Calculus: Enables you to build neural networks 

• Probability and statistics: Learn about margin of error and conditional probability 

Hands-on experience

To better understand how AI works, you can start by building your own ML models in Python. The Data Science Discovery page from the University of Illinois provides you with the steps to build your own ML model in Python. [1] One of the simplest ways to learn ML is to utilize an ML library called scikit-learn (sk-learn). Sk-learn enables you to learn about models like decision trees and more. You can also learn more about how to build your own ML projects and develop your coding skills with TensorFlow. If you want to learn more about deep learning and understand the intricacies of neural networks, you can utilize PyTorch. 

Career opportunities in AI

AI is opening doors for new career opportunities in various roles, including AI development, ML engineering, and data science. 

Potential job roles and salary expectations

Potential career paths in AI include: 

• AI specialist/developer: Create and deploy AI software and systems utilizing computer science and software engineering. The average salary for an AI developer is $87,488 per year, according to Glassdoor. [2]

• Machine learning engineer: Develop and implement software to perform tasks and optimize workflows. According to Glassdoor, the average salary for an ML engineer is $122,471 per year. [3]

• Data science specialist: Deploy infrastructure and data science platforms to resolve data problems. The average salary for a data science specialist is $112,755, according to Glassdoor. [4]

Specializations within AI

Advanced areas within AI that are important to learn about include natural language processing (NLP) and computer vision (CV). NLP is a form of AI that utilizes ML to enable computers and machines to interpret and communicate with human language. It allows computers to create content, such as text and speech, by combining human language with ML and deep learning. CV is a form of AI that utilizes ML and neural networks to teach machines and computers how to extract meaning from visual data, such as images or videos. It also teaches the AI how to develop resolutions to problems and defects.

Community and networking

Before connecting and networking with other AI professionals, it’s important to identify the area of AI you’re most interested in pursuing. Once you choose your area of study, you can use keywords within your field to search for communities and networking opportunities. 

Connecting with other learners

You can connect with other learners through online forums and social media groups. You can find various online forums on sites such as OpenAI, which allow you to attend virtual networking events. LinkedIn also has various networking forums, such as Agile AI, that you can join to meet fellow AI enthusiasts.

Conferences and meetups

Conferences and meetups are other great ways to build your network. AI and ML events in various locations are available for you to attend to learn more about the field. Making offline connections can help you build relationships with AI professionals.

Ethical considerations in AI

AI's rapid growth has raised awareness of the need to address important ethical considerations, such as ensuring unbiased decision-making, safe usage of the technology, and solid security practices. 

• Bias: If AI is trained on biased data, the AI will reproduce and make decisions based on biased information. 

• Weapon development: AI in weapon development is an ethical concern because it may enable AI-based weaponry to facilitate life-or-death outcomes in battle. 

• Security risks: Without proper safeguards, people may use AI for malicious purposes such as cyberattacks, deepfakes, and invasive surveillance. 

Responsible AI development

It’s important for teams to address the ethical concerns of AI before it’s deployed. Technologists and policymakers are currently in the process of creating ethical guidelines, implementing regulations, and determining best practices to safely develop and deploy AI technologies.

Getting started with Coursera

Effectively learning how to build AI means expanding your knowledge of programming languages, mathematical foundations, and key AI technologies. Discover more about the core concepts of AI with IBM’s Introduction to Artificial Intelligence (AI), or learn more about building ML models with Stanford and DeepLearning.AI’s Machine Learning Specialization on Coursera.