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Brendan Martin
Author: Brendan Martin
Founder of LearnDataSci

7 Best Artificial Intelligence Courses to Learn AI in 2023

Gartner, a technology research firm, projects that the artificial intelligence sector will grow just over 21% this year. With that growth comes a significant demand for AI engineers to help maintain, improve, and build new AI systems. The courses below are my top recommendations for online AI courses you can take today to begin your journey into the field.

What AI means

The thought of Artificial Intelligence often conjures images of human-like robots, like those from Westworld and Ex Machina. Although currently impossible, this highly advanced version of AI, more formally known as Artificial General Intelligence (AGI), is being heavily researched.

Notable developments in AI have been in Artificial Narrow Intelligence (ANI), or AI systems focused on narrow tasks, like autonomous driving, playing Starcraft, and debating humans.

Acclaimed machine learning professor Andrew Ng explains in his course AI for Everyone that AGI has made little progress compared to ANI. Essentially all online AI courses pertain to narrow intelligence, so most of this article will focus on recommending ANI courses.

What should you learn?

In Artificial Intelligence: A Modern Approach, one of the most widely assigned textbooks in college AI courses, the author describes the various narrow intelligence subtopics that could produce an AGI system when combined.

  • natural language processing to communicate successfully in a human language;
  • knowledge representation to store what it knows or hears;
  • automated reasoning to answer questions and to draw new conclusions;
  • machine learning to adapt to new circumstances and to detect and extrapolate patterns.
  • computer vision and speech recognition to perceive the world;
  • robotics to manipulate objects and move about.

— Russell, S. J. (2020). Artificial Intelligence: A Modern Approach. Pearson

It's worth noting that machine learning is a critical component in an AI curriculum, and there is a lot of crossover between AI courses and machine learning courses.

Many of the most important advances in AI as we know it today are due to developments in machine learning, specifically through deep learning and reinforcement learning. Because of their importance to AI, I've considered their inclusion in the curriculum more heavily when searching for and selecting the best courses.

Before we get into the course recommendations, we need to describe AI course prerequisites briefly.


Most AI courses assume you have basic knowledge of statistics, probability, linear algebra, calculus, and programming, and without this mathematics exposure, you'll find it challenging to understand many AI concepts. You don't need a graduate-level understanding, but AI is an advanced math and computer science subject, so comfort with these prerequisites is essential.

If you're uncomfortable with any of these subjects, the following are some of the top-rated courses that may benefit you:

If you have some familiarity with each, you may find it easier to take one of the AI courses listed below and reference these courses when something doesn't make sense. All of the prerequisite courses listed above, except for Codecademy, have entirely free videos.

Aim not to just sit and watch videos, but to solve as many problems in these subjects as possible.

The 7 Best AI Courses for 2023

RankTitle LinkPlatformRatingLevel
1AI For EveryoneCoursera4.8Beginner
2Artificial Intelligence NanodegreeUdacity4.8Beginner-Intermediate
3Professional Certificate in Computer Science for Artificial IntelligenceedX4.9Intermediate
4Deep Learning SpecializationCoursera4.9Intermediate
5Self-Driving Cars with DuckietownedX4.9Intermediate
6Natural Language Processing SpecializationCoursera4.6Intermediate
7Artificial IntelligenceOpenCourseWare4.8Intermediate

Course breakdowns

Course LinkEnroll

Best for:

AI newcomers desiring a broad, non-technical overview of the field


Taught by Andrew Ng, creator of the famous Stanford Machine Learning class, this course is the best non-technical introduction to AI.

This is a good fit for a comprehensive view of AI, what it can do, its misconceptions, and its benefits. Conversely, if you're interested in the technical aspects of implementing AI solutions, you're better off considering one of the other courses on this list.

Andrew Ng brilliantly explains the complexities of AI in simple, primarily non-technical terms, giving anyone the ability to converse with practitioners and speak intelligently about AI in its current state.


  • What is AI?
  • Building AI Projects
  • Building AI in Your Company
  • AI and Society

The rest of this article will recommend the best technical courses, that is, those requiring the aforementioned prerequisite knowledge in math and programming.

Enroll in AI For Everyone

Pricing3 months for $1017
Course LinkEnroll

Best for:

Anyone interested in learning a wide range of AI techniques from some of the top AI experts


Peter Norvig, the author of Artificial Intelligence: A Modern Approach, the most widely used AI textbook in universities, co-created this AI course. This course's curriculum follows a similar, but condensed, path to Norvig's textbook and forms a general overview of AI techniques.

The course features several example projects that'll test your new knowledge from each lesson, including building a sudoku solver, a forward planning agent, an adversarial game-playing agent, and part of speech tagging model. These projects will provide valuable portfolio pieces and proof of your newly acquired AI skills.


  • Introduction to Artificial Intelligence
  • Classical Search
  • Automated Planning
  • Optimization Problems
  • Adversarial Search
  • Fundamentals of Probabilistic Graphical Models

Overall, this course offers a strong foundation in Artificial Intelligence techniques. The content mirrors that of many intro AI courses offered at universities and is presented by two top minds in the industry.

Despite the positives, one central AI technique missing from this curriculum is machine learning. For that, check out the next course in this list.

Enroll in Artificial Intelligence Nanodegree

Course LinkEnroll

Best for:

Learners that yearn for a better computer science foundation


The CS50 computer science course from Harvard is one of the most popular CS online courses currently available. This two-part professional certificate from edX tracks Harvard's CS50 and CS50AI courses, allowing learners without the prerequisite CS knowledge to break into AI.

AI is computer science, so making sure you understand traditional CS concepts is critical to learning how to make intelligent systems. The professional certificate requires both courses to be completed, but if you already feel like your CS knowledge is adequate, jumping to the second course might be a better fit and save time.

Even though this course has sections on C and Python programming, I wouldn't consider it an introduction to programming. If you're not already comfortable with a programming language, you may find it challenging to keep up.


Course 1: Introduction to Computer Science

  • Intro to Computer Science
  • Programming with C
    • Data types, operators, conditional statements, loops, command line
    • Functions, variables, debugging, arrays, command-line arguments
  • Algorithms
    • Linear search, binary search, bubble sort, selection sort, recursion, merge sort
  • Memory
    • Hexadecimal, pointers, custom types, dynamic memory allocation, call stacks, file pointers
  • Data Structures
    • singly-linked lists, hash tables, tries
  • Programming with Python
  • Using SQL with Python
  • Web programming
    • Intro to the Internet, IP, TCP, HTTP, HTML, CSS, JavaScript, DOM
  • Flask web servers and Ajax

Course 2: Introduction to Artificial Intelligence with Python

  • Search - finding solutions to problems
  • Knowledge - representing information and drawing inferences from it
  • Uncertainty - using probability to deal with uncertain events
  • Optimization - finding the best way to solve a problem
  • Learning - using data to improve performance
  • Neural Networks - using brain-like structures to perform tasks
  • Language - processing human's natural language

I found the lessons to be an entertaining and insightful mix of on-stage presentations and code demonstrations. The lecturers are excellent teachers, but there's no hand-holding. You'll find this series challenging and demanding, something you'd expect from an actual college course.

Enroll in Professional Certificate in Computer Science for Artificial Intelligence

Course LinkEnroll

Best for:

Students with some experience who want to dive into the deep learning branch of AI


This Specialization by Andrew Ng takes a deep dive into deep learning, an advanced form of neural network.

Although deep learning is considered only one piece of AI, it's played a critical role in many of the most impressive AI achievements. This course is designed to provide a broad knowledge of recent developments in deep learning and contains insightful wisdom on building, training, and optimizing machine learning models.


Course 1: Neural Networks and Deep Learning

  • Introduction to Deep Learning
  • Basics of Neural Networks
  • Shallow Neural Networks
  • Deep Neural Networks

Course 2: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

  • Practical aspects of Deep Learning
  • Optimization Algorithms
  • Hyperparameter Tuning, Batch Normalization and programming frameworks

Course 3: Structuring Machine Learning Projects

  • ML production workflow
  • Error analysis procedures

Course 4: Convolution Neural Networks

  • Foundations of Convolutional Neural Networks
  • Deep Convolutional Models
  • Object Detection
  • Special Applications: Face Recognition and Neural Style Transfer

Course 5: Sequence Models

  • Recurrent Neural Networks
  • Natural Language Processing and Word Embeddings
  • Sequence Models and Attention Mechanism
  • Transformer Network

In the last two courses of this Specialization, you'll learn about computer vision and natural language processing, two important AI subtopics, making for a well-rounded introduction to the field. Deep learning is only one of many AI techniques, so you may want a broader overview of AI before enrolling in this course series.

Enroll in Deep Learning Specialization

PricingFree, $399 for materials
Course LinkEnroll

Best for:

Intermediate learners who have a strong interest in autonomous driving and enjoy hands-on learning


Autonomous driving is one of the most active AI fields, and this course uniquely tackles online learning for self-driving vehicles by pairing its content with a purchasable driving robot. For a $399 starter kit (found here), you'll receive a Duckiebot vehicle, road mat, cones, and signs to start live training your own models for autonomous driving.

The course itself digs deep into controlling your Duckiebot, such as how to drive in lanes, stop at intersections, and detect and avoid crashing into objects. All coding is done with Python and a machine learning framework, like PyTorch or Tensorflow.


  • Introduction to autonomous vehicles
  • Towards autonomy
  • Modeling and control
  • Robot vision
  • Object detection
  • State estimation and localization
  • Planning
  • Learning by reinforcement

Duckiebot uses an NVIDIA Jetson Nano, a small computer built for AI IoT applications, for which you'll learn how to program. Once the course is completed, you'll have knowledge of basic robotics, IoT, and reinforcement learning (eg, Q Learning), after which you can go on and apply your new skills to all sorts of IoT and robotics applications.

Enroll in Self-Driving Cars with Duckietown

Course LinkEnroll

Best for:

Those with some experience and a fascination with the NLP branch of AI


One core feature of an intelligent system is deciphering, analyzing, and providing insight into human language, a feat achieved with natural language processing (NLP). The entire goal of this Specialization is to give the tools and techniques needed to build NLP systems.

The content of this course is produced by the same team that created the Deep Learning Specialization mentioned above, and so it is incredibly well-designed and informative. The Specialization is split into courses that each focus on essential model types: Classification, Probabilistic, Sequence, and Attention. These model types resulted in significant improvements in NLP and formed the foundation of some of the best language models we have today.


Course 1: Classification and Vector Spaces

  • Sentiment Analysis with Logistic Regression
  • Sentiment Analysis with Naive Bayes
  • Vector Space Models
  • Machine Translation and Document Search

Course 2: Probabilistic Models

  • Autocorrect
  • Part of Speech Tagging and Hidden Markov Models
  • Autocomplete and Language Models
  • Word Embeddings and Neural Networks

Course 3: Sequence Models

  • Neural Networks for Sentiment Analysis
  • Recurrent Neural Networks for Language Modeling
  • LSTMs and Named Entity Recognition
  • Siamese Networks

Course 4: Attention Models

  • Neural Machine Translation
  • Text Summarization
  • Question Answering
  • Chatbot

While not a general introduction to AI, this Specialization will leave you with highly-valuable skills in a subset of AI. From here, you'll have the prerequisite knowledge to either start building your own startup around NLP or to find a career in the industry.

Enroll in Natural Language Processing Specialization

Course LinkEnroll

Best for:

Self-starters looking for a completely free, top-tier course


This is a free AI course from MIT OpenCourseWare, a platform that hosts many MIT courses complete with homework, exams, solutions, lecturer notes, and full lecture videos. This course is a perfect fit if you're a self-motivated learner and don't care about platform interactivity, auto-graded assignments, and certificates.

Since this is a live recorded university course, lessons are given in an MIT lecture hall by Patrick Henry Winston, a renowned MIT professor. The content in this class spans more broadly than any other course of found, touching on everything from basic AI algorithms, to machine learning, to probabilistic methods. AI is a fast-moving field, and since this course was recorded in 2010, it doesn't include some more recent developments. Despite that, the concepts presented are still relevant and form the foundation of AI today.


  • Reasoning
    • Goal trees
    • Problem solving
    • Rule-based expert systems
  • Search
    • Depth-first
    • Hill climbing
    • Beam
    • Optimal
    • Branch and bound
    • A*
    • Games
    • Minimax
    • Alpha-beta
  • Constraints
    • Interpreting line drawings
    • Search
    • Domain reduction
    • Visual object recognition
  • Learning
    • Nearest neighbors
    • Identification trees
    • Disorder
    • Neural nets and back propagation
    • Genetic algorithms
    • Sparse spaces
    • Phonology
    • Near misses
    • Felicity conditions
    • Support vector machines
    • Boosting
  • Representations
    • Classes
    • Trajectories
    • Transitions
  • Architectures
    • GPS
    • SOAR
    • Subsumption
    • Society of Mind
  • The AI business
  • Probabilistic inference
  • Model merging
  • Cross-modal coupling

The easiest way to watch the lectures for this course are through this YouTube playlist, but you'll still need to reference the OpenCourseWare page for notes, assignments, exams, and solutions.

Enroll in Artificial Intelligence

Final words

Learning AI from scratch can be daunting, but remember that no matter your background or level of education, you can learn anything with persistence.

If you've taken one of the courses above and want to share your experience or think there's a vital offering I missed, leave a comment down below!

Meet the Authors

Brendan Martin

Chief Editor at LearnDataSci and software engineer

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