Introduction to Artificial Intelligence (AI) (Coursera)

Offered by IBM,
Introduction to Artificial Intelligence (AI) (Coursera)

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.
This course is part of multiple programs:
This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:

What You Will Learn

  • Understand what is AI, its applications and use cases and how it is transforming our lives
  • Explain terms like Machine Learning, Deep Learning and Neural Networks
  • Describe several issues and ethical concerns surrounding AI
  • Articulate advice from experts about learning and starting a career in AI

Syllabus

WEEK 1
What is AI? Applications and Examples of AI
This week, you will learn what AI is. You will understand its applications and use cases and how it is transforming our lives.

WEEK 2
AI Concepts, Terminology, and Application Areas
This week, you will learn about basic AI concepts. You will understand how AI learns, and what some of its applications are.

WEEK 3
AI: Issues, Concerns and Ethical Considerations
This week, you will learn about issues and concerns surrounding AI, including - ethical considerations, bias, jobs, etc. - their impact on society. This information will help you to have an informed discussion on the costs and benefits of AI, and reassure decision makers about implementing an AI solution.

WEEK 4
The Future with AI, and AI in Action
This week, you will learn about the current thinking on the future with AI, as well as hear from experts about their advice to learn and start a career in AI. You will also demonstrate AI in action by utilizing Computer Vision to classify images.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Deep Learning Applications for Computer Vision (Coursera) Coursera
University of Colorado Boulder

Deep Learning Applications for Computer Vision (Coursera)

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics.

Oct 12th 2026
5-12 Weeks
Introduction to Genomic Technologies (Coursera) Coursera
Johns Hopkins University

Introduction to Genomic Technologies (Coursera)

This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed.

Oct 12th 2026
4 Weeks
Matrix Methods (Coursera) Coursera
University of Minnesota

Matrix Methods (Coursera)

Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction.

Oct 12th 2026
5-12 Weeks
Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera) Coursera
IBM

Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera)

An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research.

Oct 12th 2026
4 Weeks
Probabilistic Graphical Models 2: Inference (Coursera) Coursera
Stanford University

Probabilistic Graphical Models 2: Inference (Coursera)

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more.

Oct 12th 2026
5-12 Weeks
Recommender Systems (Coursera) Coursera
Sungkyunkwan University - SKKU

Recommender Systems (Coursera)

In this course you will: a) understand the basic concept of recommender systems; b) understand the Collaborative Filtering; c) understand the Recommender System with Deep Learning; d) understand the Further Issues of Recommender Systems. Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, conditional probability, and basic machine learning algorithms.

Oct 12th 2026
4 Weeks
Innovative Teaching with ChatGPT (Coursera) Coursera
Vanderbilt University

Innovative Teaching with ChatGPT (Coursera)

Despite what you may have heard, ChatGPT offers exciting possibilities for supporting innovative teaching and personalized education. This course provides practical techniques that any educator, from K-12 to higher education, can use to help support their teaching. No experience with ChatGPT, prompt engineering, or Generative AI is required.

Oct 12th 2026
1 Week
Prompt Engineering for Web Developers (Coursera) Coursera
Scrimba

Prompt Engineering for Web Developers (Coursera)

Not quite getting the results you want from ChatGPT? Wondering how you can use AI language models to your advantage? Then this course is for you! If you’ve spent any amount of time with AI language models like ChatGPT and Google Bard, you may have noticed the results can sometimes be, well, frustrating. When it comes to leveraging AI language models, your output is often only as good as your input. In other words, it’s all about learning how best to communicate your desired results. Effective prompt engineering is the secret sauce for getting the most out of AI.

Oct 12th 2026
3 Weeks
The AI Awakening: Implications for the Economy and Society (Coursera) Coursera
Stanford University

The AI Awakening: Implications for the Economy and Society (Coursera)

This course explores how the advances in artificial intelligence can and will transform our economy and society in the near future. You will hear insights from esteemed AI researchers and industry leaders in technology, economics, and business. The curriculum addresses the technological underpinnings of Generative AI, its profound implications for businesses and the broader economy, and the potential risks associated with AI-driven transformations in the workforce.

Oct 12th 2026
3 Weeks