How to Learn Artificial Intelligence: A Beginner’s Guide

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 Google's AI Essentials course, you'll learn how to use generative AI tools to develop ideas and content, make informed decisions, and improve the speed of daily work tasks.

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Google AI Essentials

Boost Your Productivity with AI Tools. New to AI? Learn from Google experts how AI can help you speed up daily tasks and spark new ideas.

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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:

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

• 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. 

Read more: Machine Learning vs. AI: Differences, Uses, and Benefits

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 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 just 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?

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

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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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Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Strategic Thinking, Artificial Neural Networks, Data Science, Data Ethics, Market Opportunities, Artificial Intelligence, Team Building, Business Ethics, Machine Learning, Needs Assessment, Engineering Management

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.

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Introduction to Statistics

Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By ...

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Average time: 14 hour(s)

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Skills you'll build:

Sampling (Statistics), Statistical Inference, Probability, Regression Analysis, Exploratory Data Analysis, Data Analysis, Probability Distribution, Statistical Analysis, Statistical Methods, Descriptive Statistics, Statistical Hypothesis Testing, Data Collection, Quantitative Research, Statistics, Statistical Modeling

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++.

Read more: Python or R for Data Analysis: Which Should I Learn?

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Python for Everybody

Learn to Program and Analyze Data with Python. Develop programs to gather, clean, analyze, and visualize data.

4.8

(216,354 ratings)

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Average time: 2 month(s)

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Skills you'll build:

Data Collection, Database Management, Web Services, Data Visualization, Database Design, Database Systems, SQL, Python Programming, Relational Databases, Data Structures, Programming Principles, Computer Programming, Data Modeling, Extensible Markup Language (XML), Network Protocols, Data Processing, Application Programming Interface (API), Restful API, Web Scraping, JSON, File Management, Data Manipulation, Development Environment, Data Import/Export, Object Oriented Programming (OOP), Data Visualization Software, Algorithms, Exploratory Data Analysis, Data Analysis, Unstructured Data, TCP/IP, Hypertext Markup Language (HTML), Data Capture, Data Access, Scripting Languages, Scripting, Computational Thinking

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. 

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Foundations of Data Structures and Algorithms

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Average time: 6 month(s)

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Skills you'll build:

Combinatorics, Applied Mathematics, Operations Research, Graph Theory, Design Strategies, Linear Algebra, Network Model, Computer Science, Computational Thinking, Theoretical Computer Science, Python Programming, Cryptography, Data Structures, Encryption, Programming Principles, Tree Maps, Data Encryption Standard, Public Key Cryptography Standards (PKCS), Mathematical Modeling, Algorithms, Analysis, Probability & Statistics, Big Data, Network Analysis, Advanced Mathematics, Arithmetic, Mathematical Theory & Analysis, IBM Cloud

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.

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IBM Data Science

Prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.

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Average time: 4 month(s)

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Skills you'll build:

Feature Engineering, Data Import/Export, Jupyter, Generative AI, Data Visualization, Predictive Modeling, Unsupervised Learning, SQL, Plotly, Data Wrangling, Exploratory Data Analysis, Dashboard, Supervised Learning, Data Visualization Software, Data Mining, Professional Networking, Data Literacy, Data Analysis, Matplotlib, Interactive Data Visualization, Pandas (Python Package), Relational Databases, Databases, Data Manipulation, Transaction Processing, Database Management, Stored Procedure, Query Languages, Database Design, Python Programming, Regression Analysis, Descriptive Statistics, Scikit Learn (Machine Learning Library), Statistical Modeling, Data Cleansing, Data Pipelines, NumPy, Data-Driven Decision-Making, R Programming, GitHub, Git (Version Control System), Machine Learning, Big Data, Other Programming Languages, Application Programming Interface (API), Data Science, Statistical Programming, Cloud Computing, Version Control, Data Synthesis, Data Presentation, Data Ethics, Natural Language Processing, Data Storytelling, Data Modeling, Predictive Analytics, Dimensionality Reduction, Classification And Regression Tree (CART), Decision Tree Learning, Applied Machine Learning, Object Oriented Programming (OOP), Data Structures, JSON, Web Scraping, Computer Programming, Automation, Programming Principles, Restful API, Scripting, Data Processing, Histogram, Scatter Plots, Box Plots, Seaborn, Geospatial Information and Technology, Heat Maps, Interviewing Skills, Applicant Tracking Systems, Portfolio Management, Company, Product, and Service Knowledge, Presentations, Job Analysis, Recruitment, Writing, Business Research, Problem Solving, Talent Sourcing, Communication, Professional Development, Deep Learning, Digital Transformation, Artificial Intelligence, Data Collection, Machine Learning Methods, Business Analysis, Data Quality, Stakeholder Engagement, Analytical Skills, Peer Review, User Feedback

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.

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Supervised Machine Learning: Regression and Classification

In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries ...

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NumPy, Machine Learning, Regression Analysis, Predictive Modeling, Python Programming, Feature Engineering, Classification And Regression Tree (CART), Artificial Intelligence, Statistical Modeling, Jupyter, Supervised Learning, Unsupervised Learning, Scikit Learn (Machine Learning Library)

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.

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Deep Learning

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Average time: 3 month(s)

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Skills you'll build:

Machine Learning, Computer Vision, Artificial Intelligence, Tensorflow, Linear Algebra, Applied Machine Learning, Python Programming, Debugging, PyTorch (Machine Learning Library), Supervised Learning, Analysis, Performance Tuning, Artificial Neural Networks, Keras (Neural Network Library), Machine Learning Algorithms, Natural Language Processing, Image Analysis, Large Language Modeling, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Data Quality, Calculus, Data Processing, Algorithms

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

Learn more: 9 Best Python Libraries for Machine Learning

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DeepLearning.AI TensorFlow Developer

Learn to build AI apps with Tensorflow. Build, train, and optimize deep neural networks and dive deep into Computer Vision, Natural Language Processing, and Time Series Analysis, along with best practices and hands-on experience in one of the most in-demand deep learning frameworks.

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Average time: 2 month(s)

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Machine Learning, Generative AI, Computer Vision, Tensorflow, Predictive Modeling, Applied Machine Learning, Forecasting, Supervised Learning, Time Series Analysis and Forecasting, Artificial Neural Networks, Text Mining, Keras (Neural Network Library), Natural Language Processing, Image Analysis, Data Processing, Artificial Intelligence and Machine Learning (AI/ML), Deep 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

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.

Month 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.

Month 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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Start your AI learning journey today

Your journey to a career in artificial intelligence can begin with a single step. Consider taking one of these AI courses on Coursera to start building your skills today:

For an overview of AI, try DeepLearning.AI’s AI For Everyone course 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.

To enhance your career with AI skill, enroll in the IBM AI Foundations for Everyone Specialization. Learn foundational AI concepts, familiarize yourself with AI services, and gain hands-on experience with several AI environments and applications.

For a comprehensive introduction to generative AI, consider taking Google Cloud's Introduction to Generative AI Learning Path Specialization. Here, you'll explore the foundations of large language models, their applications, and the ethical considerations crucial for AI development.

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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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Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Strategic Thinking, Artificial Neural Networks, Data Science, Data Ethics, Market Opportunities, Artificial Intelligence, Team Building, Business Ethics, Machine Learning, Needs Assessment, Engineering Management

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AI Foundations for Everyone

Unlock your potential with AI. Build job-ready AI skills to enhance your career. Everyone welcome. No prior AI background required.

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Automation, Content Creation, Machine Learning, ChatGPT, Generative AI, Business Process Automation, Artificial Intelligence, Prompt Engineering, Workflow Management, OpenAI, Natural Language Processing, Image Analysis, Large Language Modeling, Data Science, Application Deployment, Market Opportunities, Program Development, Customer Service, Deep Learning, IBM Cloud, Virtual Environment, WordPress, Decision Support Systems, Interaction Design, Customer experience improvement

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Average time: 1 month(s)

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Organizational Effectiveness, Machine Learning Methods, Generative AI, Artificial Intelligence, Governance, Prompt Engineering, Decision Making, Data Ethics, Accountability, Business Ethics, Compliance Training, Google Cloud Platform, Ethical Standards And Conduct, Large Language Modeling, Application Development, Corporate Strategy, Artificial Intelligence and Machine Learning (AI/ML)