19 Best Artificial Intelligence (AI) Courses for 2023

In the rapidly evolving world of technology, artificial intelligence (AI) has emerged as a driving force behind many groundbreaking innovations. As the demand for AI expertise grows, professionals and enthusiasts are seeking comprehensive educational resources to stay ahead of the curve. In this article, we explore the “best AI courses” available to learners, providing insights into their content, structure, and unique offerings to help you make an informed decision and embark on your AI learning journey.

My Favorite Online Artificial Intelligence Courses To Learn AI in 2023 Are:

1. AI for Everyone (Coursera – DeepLearning.AI)

The course teaches non-technical professionals about AI and its applications. It covers common AI terminology, the realistic capabilities of AI, identifying opportunities for AI, machine learning and data science projects, building an AI team and strategy, and navigating ethical discussions. Engineers can also benefit from the course to learn about the business aspects of AI.

2. Artificial Intelligence A-Z™ 2023: Build an AI with ChatGPT4 (Udemy)

This course teaches AI concepts and coding using Python, and includes merging AI with OpenAI Gym, optimizing AI, code templates, intuition tutorials, real-world solutions, and in-course support. It is designed for anyone interested in AI, machine learning, or deep learning, and covers beginner to expert AI skills. The course emphasizes developing a deep understanding of coding AI, rather than just memorizing theory, and includes real-world applications.

3. CS50’s Introduction to Artificial Intelligence with Python (Harvard University)

This course covers the concepts and algorithms of modern artificial intelligence and machine learning, including graph search algorithms, reinforcement learning, and optimization. Through hands-on projects and the use of Python programming, students learn how to design intelligent systems and apply AI in real-world problems. The course is designed for anyone interested in AI and machine learning, and provides theoretical frameworks and practical experience to future-proof their career. The course is created by the creators of CS50, one of the most popular computer science courses.

4. AI Fundamentals (DataCamp)

This course aims to demystify AI and cover topics such as machine learning, deep learning, and predictive analytics. It covers how machines learn, their limits, and how to use machine learning to solve real-world problems such as recognizing written digits, predicting customer churn, and analyzing tweets. The course is practical and designed to introduce learners to the world of AI in a gentle yet firm manner.

5. The Beginner’s Guide to Artificial Intelligence with Unity (Udemy)

This course covers building a genetic algorithm and neural network from scratch in C#. It also covers using the Unity ML-Agents plugin and Tensorflow to train game characters. Students will be able to apply their knowledge of machine learning to their own projects and integrate contemporary research ideas in the field. The course focuses on distilling the mathematics and statistics behind machine learning into working program code. Lastly, the course teaches using Proximal Policy Optimisation to train a neural network.

6. Introduction to Artificial Intelligence (AI) Coursera

This course is part of a program and offers a shareable career certificate upon completion. It covers four modules and teaches the basics of AI, including concepts such as machine learning, deep learning, and neural networks. The course also covers ethics and bias surrounding AI, as well as advice for starting a career in AI. A mini project is included to demonstrate AI in action. The course is designed for individuals with or without a technical background, and no programming or computer science expertise is required.

7. Artificial Intelligence Nanodegree (Udacity)

This program teaches the foundational AI algorithms used in various applications, from NASA’s Mars Rover to DeepMind’s AlphaGo Zero. The program is designed to be completed in three months and covers classical AI algorithms applied to common problem types. Students will learn about Bayes Networks, Hidden Markov Models, and other related concepts.

8. Google AI For Social Good (Google)

This guide offers resources for both novices and those seeking to uplevel their AI and machine learning (ML) skill set. It covers the types of problems organizations can solve with ML, identifying and preparing data sources, and guidelines for developing and utilizing ML responsibly. The guide includes an introduction to AI and ML, including the difference between traditional programming and ML, which allows programs to learn from examples rather than a list of instructions. It also includes ML terminology and interactive demos to demonstrate the capabilities of ML. The guide also includes information about applying AI to humanitarian and environmental challenges.

9. Artificial intelligence in Digital Marketing (Udemy)

This course covers concepts related to AI and machine learning in the context of digital marketing. It explains the difference between AI and machine learning and how to conduct SEO now that Google is an “AI first” company. Other topics covered include chatbots, programmatic advertising, big data, digital assistants, data science, SQL, and the future of internet marketing. The course is suitable for both new and advanced digital marketers, content creators interested in leveraging AI for content creation and curation, and lead-generation and email marketers looking to learn new technology.

10. Artificial Intelligence Professional Program (Stanford)

The Artificial Intelligence Professional Program is a fully online program that covers machine learning, natural language processing, reinforcement learning, and other modern AI techniques. Modeled after the Stanford Artificial Intelligence Graduate Certificate program, the AI Professional Program is designed for working professionals who want to explore AI topics at graduate-level depth with the flexibility of schedule and scope. Each 10-week course has a cohort of over 100 learners who collaborate, create study groups, and receive support from Stanford-affiliated Course Assistants. A short application is required before enrolling in the program to verify that applicants meet the prerequisite requirements.

11. Deep Reinforcement Learning: Hands-on AI Tutorial in Python (Udemy)

This course teaches the concepts and fundamentals of reinforcement learning, as well as the main algorithms including Q-Learning, SARSA, and Deep Q-Learning. Students will learn how to formulate a problem in the context of reinforcement learning and Markov Decision Processes (MDP). The course also includes hands-on experiments and real-world projects to apply the learned techniques. By the end of the course, students will be able to develop artificial intelligence applications using reinforcement learning.

12. Artificial Intelligence: Reinforcement Learning in Python (Udemy)

In this course, students will learn how to apply gradient-based supervised machine learning methods to reinforcement learning, understand reinforcement learning on a technical level, and understand the relationship between reinforcement learning and psychology. The course also covers the implementation of 17 different reinforcement learning algorithms. By the end of the course, students will have the skills and knowledge to implement and apply reinforcement learning algorithms to solve various problems.

13. IBM Applied AI Certification Course (Coursera)

In this course, students will learn the definition of AI and its applications and use cases, as well as terms such as machine learning, deep learning, and neural networks. The course also covers the creation of AI chatbots and virtual assistants without any programming and deploying them on a website. Students will also learn how to build AI-powered solutions using IBM Watson AI services, APIs, and Python with minimal coding. Additionally, the course covers computer vision techniques using Python, OpenCV, and Watson, and how to develop custom image classification models and deploy them to the cloud. By the end of the course, students will have the skills and knowledge to create and deploy various AI-powered solutions using different tools and techniques.

14. Modern Artificial Intelligence Masterclass: Build 6 Projects (Udemy)

In this course, students will learn how to deploy Emotion AI-based models using Tensorflow 2.0 Serving and use them to make inferences. The course also covers the concept of Explainable AI and how to uncover the blackbox nature of Artificial Neural Networks and visualize their hidden layers using the GradCam technique. Students will also learn how to develop Deep Learning models to automate and optimize the brain tumor detection processes at a hospital, using ResNets and ResUnet networks for healthcare applications. Additionally, the course covers building, training, and deploying AI models in business to predict customer defaults on credit cards using AWS SageMaker XGBoost algorithm, and optimizing XGBoost model parameters using hyperparameters optimization search. The course also covers applying AI in business applications by performing customer market segmentation to optimize marketing strategy. Finally, students will learn the underlying theory and mathematics behind the DeepDream algorithm for Art generation, and develop, train, and test State-of-the-art DeepDream algorithms to create AI-based art masterpieces using Keras API in TF 2.0. The course also covers developing ANNs models and training them in Google Colab while leveraging the power of GPUs and TPUs. By the end of the course, students will have the skills and knowledge to build and deploy various AI models for different applications, as well as optimize their performance using various techniques.

15. Elements of AI (MinnaLearn & University of Helsinki)

The Elements of AI is a free online course created by MinnaLearn and the University of Helsinki, designed to teach people about the basics of AI, what can and cannot be done with it, and how to start creating AI methods. The course is comprised of two parts: “Introduction to AI,” which requires no programming or complicated math, and “Building AI,” which covers the algorithms necessary to create AI methods and recommends basic Python programming skills. Over 950,000 people have taken the courses, which combine theory with practical exercises and can be completed at your own pace.

16. AI Application with Watson (edX)

The “AI Applications with Watson” course by IBM is a free online program that teaches learners how to use IBM Watson Discovery to build and program chatbots, extract insights from data sets, and apply natural language processing techniques using services such as Tone Analyzer and Personality Insights. The course is self-paced and takes about 2-4 hours per week for 3 weeks to complete. Learners need to have basic knowledge of object-oriented programming, Node.js, and IBM Watson Assistant, and should have an IBM Cloud account, Git, IBM Cloud command line tool, and a text or code editor. The course is in English and provides an opportunity to earn a skill badge.

17. Introduction to Artificial Intelligence (AI) for Managers (Udemy)

This course on Artificial Intelligence and Machine Learning for Managers aims to teach learners about AI and ML concepts in detail, as well as technologies with real-world examples and use cases. The course is created by a CIO. Learners will gain an understanding of what AI and ML are, their real-world use cases in consumer scenarios and industries, and how Industry 4.0 and Digital Transformation are centered around AI. The course will cover types of AI, including Vision, Text / Natural Language Processing (NLP), Analytics, and Interactive AI, and will also touch upon the role of Data Scientist and Machine Learning in Advanced Analytics. Additionally, learners will learn about Machine Learning Model development steps with examples, including python code snippets, ML Pipeline, and AI maturity challenges for organizations.

18. MicroMasters® Program in Artificial Intelligence by Columbia University (edX)

It’s great to know that Columbia University offers an online MicroMasters program on Artificial Intelligence on edX. This program provides learners with a rigorous, advanced, professional, and graduate-level foundation in AI. The courses included in the program are Animation and CGI Motion, Artificial Intelligence (AI), Machine Learning, and Robotics. Learners can either audit these courses for free or receive a verified certificate for a small fee. This is a great opportunity to gain expertise in one of the most fascinating and fastest-growing areas of computer science.

Best AI Courses & Machine Learning for Beginners to Learn in 2023

1. AI for Everyone (Coursera – DeepLearning.AI)

If you’re interested in learning about AI but don’t have a technical background, the AI for Everyone course on Coursera by DeepLearning.AI might be a good fit for you. This course covers various topics, including common AI terminology, the realistic capabilities of AI, identifying opportunities for AI, machine learning and data science projects, building an AI team and strategy, and navigating ethical discussions. Even engineers can benefit from this course by learning about the business aspects of AI.

Questions I Get Asked About AI Courses:

What AI means

AI meaning: Artificial Intelligence; computer systems performing tasks requiring human-like intelligence; includes problem-solving, learning, reasoning, perception, language understanding, interaction.

What should you learn inside the AI course?

An AI course covers a range of topics, including the history and branches of AI, foundational principles and algorithms, machine learning, deep learning, natural language processing, computer vision, robotics, AI ethics, practical applications, and relevant tools and libraries. Students can expect to gain hands-on experience through AI projects, learning about various techniques and frameworks used in the field.

Is artificial intelligence difficult to learn?

The difficulty of learning artificial intelligence depends on factors such as background knowledge, programming experience, motivation, learning resources, time commitment, and hands-on practice. It can be challenging, but with dedication and the right approach, it is achievable.

Can anyone learn AI?

Yes, anyone with dedication, access to resources, and a willingness to learn can acquire AI skills.

Which artificial intelligence course is the best?

The best AI course is ‘AI for Everyone’ offered on Coursera by DeepLearning.AI.

What is an artificial intelligence course?

An artificial intelligence course is a program that teaches the principles, techniques, and tools used in AI systems, covering topics such as machine learning, deep learning, natural language processing, and computer vision.

Edwize Summary – Best AI Courses

Drawing from my personal experience in AI, I found the best AI courses to be an invaluable resource in my ongoing learning journey. The diverse range of courses offered by prestigious institutions and leading online platforms allowed me to explore various aspects of artificial intelligence that aligned with my interests and goals.

As you embark on your own AI learning adventure, I am confident that this comprehensive guide will serve as a helpful starting point, empowering you to choose the perfect AI course to fuel your passion and elevate your career.

Lets all learn to play nicely together. This is the future, embrace it.