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Every time you shop online, search for information on Google, or watch a show on Netflix, you interact with a form of artificial intelligence (AI). The applications of AI are everywhere and will only continue to grow.

From factory workers to waitstaff to engineers, AI is quickly impacting jobs. Learning AI can help you understand how technology can improve our lives through products and services. There are also plenty of job opportunities in this field, should you choose to pursue it.

Learning AI doesn’t have to be difficult, but it does require a basic understanding of math and statistics. In this guide, we’ll take you through how to learn AI and create a learning plan.

Beginner AI courses

Want to build foundational AI knowledge in just a matter of hours? Consider taking one of these popular courses on Coursera:

In DeepLearning.AI’s AI for Everyone, you’ll learn what AI is, how to build AI projects, and consider AI’s social impact in just six hours.

In IBM’s Generative AI: Prompt Engineering Basics, you’ll explore and practice different prompt engineering techniques in as little as seven hours.

What is artificial intelligence? And, why should you learn it?

Artificial intelligence (AI) is the process of simulating human intelligence and task performance with machines, such as computer systems. Tasks may include recognizing patterns, making decisions, experiential learning, and natural language processing (NLP). AI is used in many industries driven by technology, such as health care, finance, and transportation.

Learning AI is increasingly important because it is a revolutionary technology that is transforming the way we live, work, and communicate with each other. With organizations across industries worldwide collecting big data, AI helps us make sense of it all.

AI engineers earn a median salary of $136,620 a year, according to the US Bureau of Labor Statistics [1]. Professionals in this field can expect the number of jobs to grow by 23 percent over the next decade.

Besides being a lucrative career path, it is a fast-growing field and an intellectually stimulating discipline to learn.

Hear more about AI in this lecture from Stanford and DeepLearning.AI’s Machine Learning Specialization:

How long does it take to learn AI?

The amount of time it takes to learn artificial intelligence depends on several factors, including:

Career intent: If you want to pursue a job in the AI field, you’ll want a more comprehensive education than someone who simply wants to add context to their data analytics role.

Background knowledge: If you’re switching from another major or field, then it’ll take longer to learn than someone who is already working in the technology field and has a basic understanding of its complex jargon.

Artificial intelligence vs. machine learning: What’s the difference?

Artificial intelligence is computer software that mimics how humans think in order to perform tasks such as reasoning, learning, and analyzing information. Machine learning is a subset of AI that uses algorithms trained on data to produce models that can perform those tasks. AI is often performed using machine learning, but it actually refers to the general concept, while machine learning refers to only one method within AI.

How to learn artificial intelligence

Here are four steps to guide your learning. To start your journey into AI, develop a learning plan by assessing your current level of knowledge and the amount of time and resources you can devote to learning.

1. Create a learning plan.

Before you dive into a class, we recommend developing a learning plan. This includes a tentative timeline, skill-building goals, and the activities, programs, and resources you’ll need to gain those skills.

First, ask yourself the following questions:

How much money you can spend: Do you want to invest in a boot camp, take professional courses online, or watch some videos on YouTube and TikTok?

How do you want to learn: Are you interested in pursuing a degree program, a boot camp, or self-teaching through a variety of online courses?

Later in this article, we’ll provide an example of a learning plan to help you develop yours.

2. Master the prerequisite skills.

Before starting your learning journey, you’ll want to have a foundation in the following areas. These skills form a base for learning complex AI skills and tools.

Basic statistics: AI skills are much easier to learn when you have a firm grasp of statistics and interpreting data. You’ll want to know concepts such as statistical significance, regression, distribution, and likelihood, all of which play a role in AI applications.

Basic math: Understanding AI, especially machine learning and deep learning, relies on knowing mathematical concepts such as calculus, probability, and linear algebra. These frequently appear in AI algorithms and models.

Curiosity and adaptability: AI is complex and rapidly evolving, so there is a constant need to keep up with new techniques and tools. Those looking to pursue a career in AI should have an insatiable thirst for learning and an adaptable mindset for problem-solving.

The depth to which you’ll need to learn these prerequisite skills depends on your career goals. An aspiring AI engineer will definitely need to master these, while a data analyst looking to expand their skill set may start with an introductory class in AI.

If you already have a baseline understanding of statistics and math and are open to learning, you can move on to Step 3.

3. Start learning AI skills.

Once you’ve covered the prerequisites, let’s dive into the essential skills you’ll need for AI. Your level of mastery will depend on the type of role you’re pursuing.

Programming

Knowing how to code is essential to implementing AI applications because you can develop AI algorithms and models, manipulate data, and use AI programs. Python is one of the more popular languages due to its simplicity and adaptability, R is another favorite, and there are plenty of others, such as Java and C++.

Data structures

A data structure is a specialized format for organizing, storing, retrieving, and manipulating data. Knowing the different types, such as trees, lists, and arrays, is necessary for writing code that can turn into complex AI algorithms and models.

Data science

Data science encompasses a wide variety of tools and algorithms used to find patterns in raw data. Data scientists have a deep understanding of the product or service user, as well as the comprehensive process of extracting insights from tons of data. AI professionals need to know data science so they can deliver the right algorithms.

Machine learning

This popular subset of AI is important because it powers many of our products and services today. Machines learn from data to make predictions and improve a product’s performance. AI professionals need to know different algorithms, how they work, and when to apply them.

Deep learning

Deep learning is a subset of machine learning that uses many layers of neural networks to understand patterns in data. It’s often used in the most advanced AI applications, such as self-driving cars.

4. Get familiar with AI tools and programs.

Along with building your AI skills, you’ll want to know how to use AI tools and programs, such as libraries and frameworks, that will be critical in your AI learning journey. When choosing the right AI tools, it’s wise to be familiar with which programming languages they align with, since many tools are dependent on the language used.

Here are some popular tools and libraries specifically for Python:

NumPy
Scikit-learn
Pandas
Tensorflow
Seaborn
Theano
Keras
PyTorch
Matplotlib

Learn more: 9 Best Python Libraries for Machine Learning

How to develop a learning plan

Learning on your own and wondering how to stay on track? Develop a learning plan to outline how and where to focus your time. Below, we’ve provided a sample of a nine-month intensive learning plan, but your timeline may be longer or shorter depending on your career goals.

Month 1-3: Basics of mathematics and statistics, programming, and data structures

Math and statistics: Learn the basics by studying calculus, algebra, statistics, and probability, which will serve as a foundation for your AI journey.
Programming: Learn a programming language, like Python or R. You’ll then become familiar with libraries and packages.
Data structures: Start learning how to store, retrieve, and manipulate datasets, and then how to clean and prepare them, which is necessary for any AI project.

Month 4-6: Dive into data science, machine learning, and deep learning

Data science: Learn the basics of data science and how AI can help facilitate extracting and deriving insights from data.
Machine learning: Dive into the various types of machine learning algorithms, such as supervised, unsupervised, and reinforcement learning.
Deep learning: Understand neural networks and the concepts of deep learning.

Month 7-9: Get familiar with AI tools and choose a specialization

AI tools: Once you’ve mastered the basics, you can start using the different libraries associated with the programming language you learned, as well as other AI tools such as ChatGPT.
Specialization: You may want to specialize in a specific area of AI, such as natural language processing, or perhaps how to apply AI to another field.
Further learning and job search: Start looking for AI jobs, if that was part of your intention for learning. Continue to keep up with AI trends with blogs, podcasts, and more.

Start learning AI today

Your journey to a career in artificial intelligence can begin with a single step. DeepLearning.AI’s AI For Everyone, taught by top instructor Andrew Ng, provides an excellent introduction. In just 10 hours or less, you can learn the fundamentals of AI, how it exists in society, and how to build it in your company. Start your free 7-day trial today.

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