Free AI Courses You Should Take in 2023

Free AI Courses You Should Take in 2023
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Here are some of the best free AI courses you should take in 2023, for your professional growth

One of the fastest-growing areas of technology is artificial intelligence, and many engineers are attempting to understand it to advance their careers.

1. Intro to Artificial Intelligence: Udacity

Time to Complete- 4 Months

Level-Intermediate

The cost of this course to learn artificial intelligence is nothing. You will discover the fundamentals of AI in this course, The use of artificial intelligence will also be covered.

2. AI For Everyone: Coursera

Time to Complete- 6 Hours

Level-Beginners

As a Free to Audit course, all of the material is available without charge, but you must pay for the certificate.

As implied by the course's name, "AI for Everyone," this course is appropriate for everyone interested in learning about artificial intelligence. Andrew Ng, a co-founder of Coursera and adjunct professor of computer science at Stanford University, is the instructor for this course. For those who are completely new to artificial intelligence, this course is an excellent starting point.

3. AI Fundamentals: Udacity

Time to Complete- 1 Month

Level-Beginner

The foundations of artificial intelligence can be learned in this course, which is suitable for beginners. You will master the fundamentals of AI and machine learning in this course, along with Azure.

The following section will teach you how to use Azure Cognitive Services to manage crucial computer vision workloads like object identification, picture classification, face detection, text analysis, and form processing. Also, you will discover how to use Azure Machine Learning to train and evaluate models. You will also learn about natural language processing in this course.

4. Reinforcement learning and game AI overview: Kaggle

Time to Complete- 4 hours

Level-Beginner

On Kaggle, you can access this course without charge. There are 4 lessons in this course. The first session will teach you how to build your own artificially intelligent robots. You will then learn how to develop game AI in the traditional manner.

In the final lesson, you will study how reinforcement learning may be utilized to develop intelligent agents free of heuristics. You can evaluate your agents' effectiveness by comparing them to those created by other users.

5. Artificial Intelligence for Robotics: Udacity

Time to Complete- 2 Months

Level-Advanced

An advanced course on artificial intelligence for robotics is being offered here. The head of Google and Stanford's autonomous driving teams will teach you how to program the main systems of a robotic car in this course.

This course will cover planning, search, probabilistic inference, localization, tracking, and control, with a particular emphasis on robots. Robot motion and perception are modeled using Python programming language and a few fundamental object-oriented ideas.

6. Artificial Intelligence and Machine Learning Made Simple: Udemy

Time to Complete-

Level-N/A

This course will teach you how to apply AI applications to your business in a non-technical manner. This course is over fairly quickly. You will study various machine learning systems, the basics of artificial intelligence, and the development of machine learning over time in this course.

7. Artificial Intelligence by Georgia Tech: Udacity

Time to Complete- 44Min

Level-Beginner

The principles of artificial intelligence are covered in-depth in this additional free course, along with the essential ideas of classical search, probability, machine learning, logic, and planning. You will learn how to use AI algorithms in this course to solve a variety of practical issues, such as navigating, recognizing sign language, and playing video games.

 8. Python-based introduction to artificial intelligence: edX

Time to Complete-7 Weeks

Level- Beginner

You will study artificial intelligence concepts and algorithms in this course, as well as the theories behind technologies like handwriting recognition, machine translation, and gaming engines.

You will work on practical projects throughout the course to become familiar with the theory underlying graph search algorithms, classification, optimization, reinforcement learning, and other areas.

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