Intro to Artificial Intelligence (Udacity)

Offered by Udacity,
Intro to Artificial Intelligence (Udacity)

This course will introduce you to the basics of AI. Topics include machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing. Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI.

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

Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.
Note: Parts of this course are featured in the Machine Learning Engineer Nanodegree and the Data Analyst Nanodegree programs. If you are interested in AI, be sure to check out those programs as well!

Course Syllabus

Lesson 1
Fundamentals of AI

  • Statistics, Uncertainty, and Bayes networks.
  • Machine learning.
  • Logic and planning.

Lesson 2
Applications of AI

  • Image processing and computer vision.
  • Robotics and robot motion planning.
  • Natural language processing and information retrieval.
Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Understanding Clinical Research: Behind the Statistics (Coursera) Coursera
University of Cape Town

Understanding Clinical Research: Behind the Statistics (Coursera)

If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place. You may be a clinical practitioner reading research articles to keep up-to-date with developments in your field or a medical student wondering how to approach your own research. Greater confidence in understanding statistical analysis and the results can benefit both working professionals and those undertaking research themselves.

Oct 26th 2026
5-12 Weeks
Introduction to Large Language Models with Google Cloud (Udacity) Udacity
Udacity,Google Cloud

Introduction to Large Language Models with Google Cloud (Udacity)

Learn how large language models can be utilized and how you can use prompt tuning to enhance LLM performance. This is an introductory level course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps. Most students will be able to complete this course in under an hour.

Self Paced
Self-Paced
Reinforcement Learning (Udacity) Udacity
Georgia Institute of Technology,Udacity

Reinforcement Learning (Udacity)

You should take this course if you have an interest in machine learning and the desire to engage with it from a theoretical perspective. Through a combination of classic papers and more recent work, you will explore automated decision-making from a computer-science perspective. You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning.

Self Paced
Self-Paced
The Unix Workbench (Coursera) Coursera
Johns Hopkins University

The Unix Workbench (Coursera)

Unix forms a foundation that is often very helpful for accomplishing other goals you might have for you and your computer, whether that goal is running a business, writing a book, curing disease, or creating the next great app. The means to these goals are sometimes carried out by writing software. Software can’t be mined out of the ground, nor can software seeds be planted in spring to harvest by autumn. Software isn’t produced in factories on an assembly line. Software is a hand-made, often bespoke good. If a software developer is an artisan, then Unix is their workbench.

Oct 26th 2026
4 Weeks
Secure and Private AI (Udacity) Udacity
Udacity,Facebook

Secure and Private AI (Udacity)

Learn how to extend PyTorch with the tools necessary to train AI models that preserve user privacy. This free course will introduce you to three cutting-edge technologies for privacy-preserving AI: Federated Learning, Differential Privacy, and Encrypted Computation. You will learn how to use the newest privacy-preserving technologies, such as OpenMined's PySyft. PySyft extends Deep Learning tools—such as PyTorch—with the cryptographic and distributed technologies necessary to safely and securely train AI models on distributed private data.

Self Paced
Self-Paced
Machine Learning (Udacity) Udacity
Georgia Institute of Technology,Udacity

Machine Learning (Udacity)

Supervised, Unsupervised & Reinforcement. Machine Learning is a graduate-level course covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences. The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff. In part two, you will learn about Unsupervised Learning. Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? Such answers can be found in this section!

Self Paced
Self-Paced
AWS DeepRacer (Udacity) Udacity
Udacity,AWS

AWS DeepRacer (Udacity)

Learn the fundamentals of machine learning and reinforcement learning in a fun and engaging way through autonomous driving with AWS DeepRacer. This course will prepare you to create, train, and fine-tune reinforcement learning models in the AWS DeepRacer 3D racing simulator. You will be able to utilize the car's tech specs, assembly, and calibration to train and deploy your racing model using AWS in both simulated and real-world tracks.

Self Paced
Self-Paced
Attention Mechanism with Google Cloud (Udacity) Udacity
Udacity,Google Cloud

Attention Mechanism with Google Cloud (Udacity)

Learn how the attention mechanism works and can be applied to machine translation. This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering.

Self Paced
Self-Paced
AWS Machine Learning Foundations Course (Udacity) Udacity
Udacity

AWS Machine Learning Foundations Course (Udacity)

Learn what machine learning is and the steps involved in building and evaluating models. Gain in demand skills needed at businesses working to solve challenges with AI. Learn the fundamentals of advanced machine learning areas such as computer vision, reinforcement learning, and generative AI. Get hands-on with machine learning using AWS AI Devices (i.e. AWS DeepRacer and AWS DeepComposer). Learn how to prepare, build, train, and deploy high-quality machine learning (ML) models quickly with Amazon SageMaker and learn object-oriented programming best practices.

Self Paced
Self-Paced
Model Building and Validation (Udacity) Udacity
Udacity

Model Building and Validation (Udacity)

Advanced Techniques for Analyzing Data. This course will teach you how to start from scratch in answering questions about the real world using data. Machine learning happens to be a small part of this process. The model building process involves setting up ways of collecting data, understanding and paying attention to what is important in the data to answer the questions you are asking, finding a statistical, mathematical or a simulation model to gain understanding and make predictions.

Self Paced
Self-Paced