7 Best Artificial Intelligence Courses to Learn AI in 2024 (2024)

Which course teaches AI the best?

Depending on your goals, you may want to:

• Learn how to use Generative AI, such as ChatGPT and DALL·E, in your applications
• Build a solid artificial intelligence foundation for the future
• Understand AI's impact on your career or business
• Build your own AI from scratch
• Use state-of-the-art AI research and algorithms

If you'd like a holistic view of what AI is, what it can do, and what the future holds, the best AI course you can take is AI For Everyone on Coursera.

For more technologically inclined readers, let's discuss what you need to learn. (TL;DR click here to jump to courses)

Artificial intelligence, like that from Westworld and Ex Machina, is a highly advanced version of AI known as Artificial General Intelligence (AGI). Although currently impossible, researchers are making progress–if this is the AI you're interested in, consider going for a Ph.D.

All of the AI we know and use is Artificial Narrow Intelligence (ANI), systems focused on narrow tasks. Tasks like autonomous driving, playing Starcraft, debating humans, and even ChatGPT are considered narrow intelligence.

Machine learning is a critical component in an AI curriculum, and there is a lot of crossover between AI and machine learning courses.

Many of the most critical advances in AI have been due to developments in machine learning, specifically through deep learning and reinforcement learning.

Although a particular topic listed above might capture your interest more, acquiring a core set of foundational skills is essential for effectively developing AI.

For those interested in large-language models

If you're looking for a course on how to utilize LLMs like ChatGPT in a project, all you really need are the following skills:

• Python Programming - Learn Python from Codecademy or any other top Python course
• Effective prompting - Enroll in the Prompt Engineering Specialization on Coursera
• Working with the ChatGPT API - Take Mastering OpenAI Python APIs on Udemy

To learn how LLMs work and how to build AI, continue reading 👇

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:

• Probability: Fat Chance: Probability from the Ground Up from Harvard
• Statistics: Fundamentals of Statistics from MIT
• Linear Algebra: Linear Algebra 18.06 from MIT
• Calculus: Single Variable Calculus and Multivariable Calculus from MIT
• Programming: Learn Python from Codecademy or any other top Python course

Solve as many problems in these subjects as possible, and you'll have a solid foundation for truly understanding AI.

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.

Also, all prerequisite courses listed above, except for Codecademy, have free videos.

#1 AI For Everyone

Best for:

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

Overview

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.

Syllabus:

• 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

#2 Artificial Intelligence Nanodegree

Best for:

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

Overview

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, forming 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.

Syllabus:

• 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 many university intro AI courses 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

#3 Professional Certificate in Computer Science for Artificial Intelligence

Best for:

Learners that yearn for a better computer science foundation

Overview

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 understanding 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.

Syllabus:

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

The lessons were an entertaining and insightful mix of on-stage presentations and code demonstrations. The lecturers are excellent teachers, but there's no hand-holding. This series is challenging and demanding, something you'd expect from an actual college course.

Enroll in Professional Certificate in Computer Science for Artificial Intelligence

#4 Deep Learning Specialization

Best for:

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

Overview

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.

Syllabus:

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

#5 Self-Driving Cars with Duckietown

Best for:

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

Overview

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.

Syllabus:

• 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 (e.g., 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

#6 Natural Language Processing Specialization

Best for:

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

Overview

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, so it is incredibly well-designed and informative. The Specialization is split into courses focusing 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.

Syllabus:

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 crucial skills in a subset of AI. From here, you'll have the prerequisite knowledge to start building your own startup around NLP or find a career in the industry.

Enroll in Natural Language Processing Specialization

#7 Artificial Intelligence

Best for:

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

Overview

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 of this class is more comprehensive than any other course I've encountered, covering a wide range of topics including basic AI algorithms, machine learning, and 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.

Syllabus:

• 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 backpropagation
  • 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 is through this YouTube playlist, but you'll still need to reference the OpenCourseWare page for notes, assignments, exams, and solutions.

Enroll in Artificial Intelligence

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 I missed a critical offering, comment below!