Coursera

Introduction to Artificial Intelligence (AI) (Coursera)

Offered by IBM,

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

Coursera

Johns Hopkins University

The R Programming Environment (Coursera)

Statistics & Data Analysis Data Science

This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Whether you are part of a data science team or working individually within a community of developers, this course will give you the knowledge of R needed to make useful contributions in those settings.

Jun 15th 2026

4 Weeks

Programming Data Science R Language

Coursera

Johns Hopkins University

Advanced R Programming (Coursera)

Statistics & Data Analysis Data Science

This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions.

Jun 15th 2026

4 Weeks

Programming Object-Oriented Programming Data Science

Coursera

University of Washington

Machine Learning: Clustering & Retrieval (Coursera)

Statistics & Data Analysis Data Science

Case Studies: Finding Similar Documents. A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover?

Jun 15th 2026

5-12 Weeks

Machine Learning Clustering MapReduce

Coursera

University of Michigan

Generative AI Essentials: Overview and Impact (Coursera)

Robotics & Computer Vision

With the rise of generative artificial intelligence, there has been a growing demand to explore how to use these powerful tools not only in our work but also in our day-to-day lives. Generative AI Essentials: Overview and Impact introduces learners to large language models and generative AI tools, like ChatGPT. In this course, you’ll explore generative AI essentials, how to ethically use artificial intelligence, its implications for authorship, and what regulations for generative AI could look like.

Jun 19th 2026

1 Week

Artificial Intelligence AI Coursera Plus

Coursera

University of Washington

Machine Learning Foundations: A Case Study Approach (Coursera)

Statistics & Data Analysis Data Science

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies.

Jun 15th 2026

5-12 Weeks

Python Machine Learning Clustering

Coursera

University of Washington

Machine Learning: Regression (Coursera)

Statistics & Data Analysis Data Science

Case Study - Predicting Housing Prices. In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.

Jun 15th 2026

5-12 Weeks

Python Algorithms Machine Learning

Coursera

Johns Hopkins University

Managing Data Analysis (Coursera)

Management & Leadership Statistics & Data Analysis

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results.

Jun 15th 2026

1 Week

Strategy Communication Inference

Coursera

Google Cloud

Preparing for the Google Cloud Professional Data Engineer Exam (Coursera)

CS: Information & Technology Computer Science

From the course: "The best way to prepare for the exam is to be competent in the skills required of the job." This course uses a top-down approach to recognize knowledge and skills already known, and to surface information and skill areas for additional preparation. You can use this course to help create your own custom preparation plan. It helps you distinguish what you know from what you don't know. And it helps you develop and practice skills required of practitioners who perform this job.

Jun 20th 2026

5-12 Weeks

Machine Learning Data Engineer Google Cloud

Coursera

Wesleyan University

Machine Learning for Data Analysis (Coursera)

Statistics & Data Analysis Data Science

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering.

Jun 15th 2026

4 Weeks

Python Machine Learning Data Analysis

Coursera

Johns Hopkins University

Practical Machine Learning (Coursera)

Statistics & Data Analysis Data Science

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates.

Jun 15th 2026

4 Weeks

Algorithms Machine Learning Regression

Coursera

University of Minnesota

Nearest Neighbor Collaborative Filtering (Coursera)

Data Science

In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user.

Jun 15th 2026

4 Weeks

Algorithms Machine Learning Data Science

Coursera

University of Washington

Machine Learning: Classification (Coursera)

Statistics & Data Analysis Data Science

Case Studies: Analyzing Sentiment & Loan Default Prediction. In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank.

Jun 15th 2026

5-12 Weeks

Python Machine Learning Classification