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.
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Google AI
Build your AI fluency and get more done, faster. Get the AI skills employers are looking for and create 20+ solutions you can use at work, right away
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Average time: 1 month(s)
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Skills you’ll build:
Artificial Intelligence, Brainstorming, Business Communication, Content Creation, Data Analysis, Generative AI, Machine Learning, Planning, Presentations, Project Management, Research, Responsible AI, Vibe coding, Data Visualization, Google Workspace, Writing and Editing, Gemini, Oral Expression, Data Presentation, AI Workflows, Multimodal Prompts, Technical Writing, Business Writing, Google Gemini, Google Cloud Platform, Process Analysis, Natural Language Processing, Design Reviews, AI Enablement, Brand Management, Style Guides, Video Production, Storytelling, Prototyping, Prompt Engineering, Organizational Skills, Business Planning, Dependency Analysis, Operational Analysis, Collaborative Software, Knowledge Transfer, Timelines, Strategic Planning, Coordinating, LLM Application, Project Schedules, Cross-Functional Collaboration, Product Roadmaps, Trend Analysis, Data-Driven Decision-Making, Spreadsheet Software, Big Data
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.
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AI For Everyone
AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone–especially your non-technical …
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Average time: 6 hour(s)
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Skills you’ll build:
Artificial Neural Networks, Data Science, Data Ethics, Responsible AI, Machine Learning, Artificial Intelligence, AI Product Strategy, Deep Learning, AI Enablement
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.
