How to Learn Artificial Intelligence: A Beginner’s Guide

Key Takeaways

Learning artificial intelligence involves building foundational skills, choosing the right learning path, and applying AI tools to your goals.

• Start by assessing your current knowledge, defining your intention for learning, and following a learning plan that aligns with your personal or career goals.

• The amount of time it takes to learn AI depends on factors including your prerequisite knowledge, career intent, and background in technology.

• You can follow a learning plan (there's one outlined in this article!), enroll in a reputable online course, or practice with free to low cost tools.

Read on for a full breakdown of each step, including prerequisite skills, essential AI concepts, and a sample learning plan you can follow at your own pace. If you're ready to start building your AI skills right away, consider enrolling in the Google AI Professional Certificate program on Coursera. In as little as eight hours, you could master in-demand skills like using AI for data analysis, research, and communication. By the end, you'll have earned a shareable certificate for your resume.

How long does it take to learn AI?

According to the World Economic Forum, beginner-level AI skills can be learned in about 30 hours [1]. Using a structured learning program, you can gain a deeper understanding of AI in 3-4 months. You’ll want to learn Python, data manipulation, deep learning, machine learning operations, and ethics. You may also gain familiarity with AI by spending time with free AI chatbots like Google Gemini, ChatGPT, Claude, and Perplexity. By using AI chatbots to support your research, synthesize information from multiple sources, answer questions, or assist with code generation, you’ll learn more about how AI processes requests, formulates answers, and works within the constraints provided.

Where do I start with AI?

Learning Python is a foundational step in learning AI. Once you’re comfortable with the essentials, you can begin to study machine learning and generative AI frameworks. You may find it helpful to begin projects at this stage, too. You’ll be able to learn concepts while applying them and gaining hands-on experience.

Do I need to know how to code to learn AI?

No, you don’t need to know how to code to understand the essentials of AI or use chatbots. There are no-code tools that can help you bring ideas to life, but if you want to build and deploy AI systems, you’ll need to know Python. Beginner knowledge can be supplemented by using a library like TensorFlow or PyTorch. To drive AI engineering, you’ll need to know Python, in addition to C++, Java, and R.

How to learn AI for everyday use

You don't need to become an AI engineer to benefit from understanding artificial intelligence. For most people, learning AI is less about writing algorithms and more about knowing how to work alongside the tools already reshaping every industry. Here are a few places to start:

• Get comfortable with AI tools you already have access to. ChatGPT, Google Gemini, Microsoft Copilot, and similar tools are free or low-cost entry points. Spend time experimenting with prompting, because the way you phrase a request dramatically changes the output you get. Learning to write clear, specific prompts is quickly becoming a baseline professional skill.

• Understand the basics of how AI works without the math. You don't need calculus to grasp that machine learning models find patterns in data, or that AI outputs are probabilistic, not certain. Andrew Ng's AI for Everyone on Coursera is a widely recommended starting point built specifically for non-technical learners and takes around seven hours to complete.

• Apply AI to your current role. Start by identifying workflows that are repetitive, high-volume, or time-consuming, such as drafting routine communications, summarizing documents, generating first drafts, pulling together research, or brainstorming ideas. These are areas where AI tools tend to add the most immediate value. Tasks that require heavy creative judgment or nuanced human context are better kept in your hands, but even there, AI can be a useful thought partner.

How to learn artificial intelligence for your career

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 take a class, we recommend developing a learning plan. This plan should include 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:

• Your level of knowledge of artificial intelligence: Are you a true beginner? Do you have a foundation in math and statistical skills? Are you familiar with basic terminology and concepts? 

• Your intention for learning: Are you pursuing a new career or supplementing your current career?

• How much time you can spend learning: Are you currently employed? Do you want to learn full-time or part-time?

• 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?

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. 

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 many 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:

1. NumPy 

2. Scikit-learn 

3. Pandas 

4. Tensorflow 

5. Seaborn

6. Theano

7. Keras

8. PyTorch

9. Matplotlib

How long does it take to learn AI?

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

• Prerequisite knowledge: If you have general math and statistics knowledge, you can skip to learning AI skills and tools.

• Career intent: If you want to pursue a job in AI, you’ll want a more comprehensive education than someone who 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 for someone already working in the technology field and has a basic understanding of its complex jargon. 

Projects & portfolios

The skills you develop and how you choose to display them will depend on your goals. Whether you’re learning AI for fun, to advance your career, or to change careers, your portfolio will look differently.

• Beginner: Beginner AI projects can include non-technical tasks, like leveraging a chatbot to build a workflow.

• Advancing career: Showcase your AI skills by cleaning a dataset and building a predictive model to be utilized by a business.

• Changing career: AI portfolio ideas for established professionals may involve building autonomous AI agents to complete tasks related to their desired field of work.

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.

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

1. Math and statistics: Learn the basics by studying calculus, algebra, statistics, and probability, which will serve as a foundation for your AI journey.

2. Programming: Learn a programming language, like Python or R. You’ll then become familiar with libraries and packages.

3. 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.

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

1. Data science: Learn the basics of data science and how AI can help facilitate extracting and deriving insights from data.

2. Machine learning: Dive into the various types of machine learning algorithms, such as supervised, unsupervised, and reinforcement learning. 

3. Deep learning: Understand neural networks and the concepts of deep learning.

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

1. 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.

2. 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. 

3. 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. 

Have career questions? We have answers.

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Why should you learn AI?

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 is an integral part of business today. 

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.

Start your AI learning journey with these free resources

Continue learning about AI and related topics, and explore new career paths.

• Subscribe to our newsletter: Career Chat

• Watch our video on YouTube: Generative AI 101

• Study our glossary: Python Syntax Cheat Sheet

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