According to Gartner, a renowned technology research firm, the artificial intelligence industry is expected to grow by over 21% this year. This growth signifies a substantial demand for AI engineers who can maintain, enhance, and create new AI systems. Below are my top picks for online AI courses that you can enroll in today to embark on your journey into this field.
Understanding Artificial Intelligence:
When we think of artificial intelligence, we often envision humanoid robots akin to those depicted in movies like Westworld and Ex Machina. While achieving this highly advanced form of AI, known as Artificial General Intelligence (AGI), remains a distant goal, significant research efforts are dedicated to its development.
Current AI advancements:
Notable advancements have been made in the field of artificial narrow intelligence (ANI). Artificial Neural Networks (ANI) are AI systems designed to perform particular tasks, such driving on its own, playing sophisticated games like Starcraft, and having conversations with people.
Insights from Andrew Ng:
Renowned machine learning professor Andrew Ng, in his course ‘AI for Everyone,’ explains that progress in AGI has been limited compared to ANI. Consequently, the focus of most online AI courses is on narrow intelligence. Therefore, the majority of the courses recommended in this article concentrate on ANI, addressing specific and specialized AI applications.
In the book ‘Artificial Intelligence: A Modern Approach,’ considered a staple in college AI courses, the author discusses diverse subtopics within narrow intelligence. These subtopics, when integrated, have the potential to create an Artificial General Intelligence (AGI) system. Therefore, aspiring AI professionals should focus on mastering these specific areas to gain comprehensive knowledge in the field.
In the pursuit of creating an Artificial General Intelligence (AGI) system, it is crucial to focus on mastering the following key subtopics within narrow intelligence:
Natural Language Processing: Proficiency in understanding and utilizing human languages is essential for effective communication.
Knowledge Representation: The ability to store acquired knowledge and information is fundamental for building a comprehensive intelligence system.
Automated Reasoning: Developing the capability to answer questions and draw new conclusions through logical reasoning enhances problem-solving abilities.
Machine Learning: Mastering the art of adapting to new situations and recognizing patterns is vital. Machine learning enables AI systems to evolve and respond to changing circumstances.
Computer Vision and Speech Recognition: These skills allow AI systems to perceive and interpret the world visually and aurally, providing a deeper understanding of the environment.
Robotics: Proficiency in robotics enables the manipulation of objects and movement within physical spaces, translating virtual intelligence into practical actions.
By focusing on these areas, AI enthusiasts can lay the groundwork for the development of a comprehensive artificial general intelligence system.
— In the book ‘Artificial Intelligence: A Modern Approach’ by Russell (2020), the significance of machine learning as a pivotal element in AI education is emphasized. There exists a significant overlap between AI and machine learning courses, indicating their intertwined nature.
Machine learning, especially through deep learning and reinforcement learning, has played a central role in shaping contemporary AI. Given their critical importance, these areas have been given substantial weight in the curriculum selection process, reflecting their impact on the field.
Before delving into the course suggestions, it is essential to provide a brief overview of the prerequisites for AI courses.
Prerequisites:
For individuals aspiring to enroll in AI courses, a foundational understanding of certain subjects is necessary. These prerequisites typically include basic knowledge in statistics, probability, linear algebra, calculus, and programming. While a graduate-level understanding is not mandatory, AI is an advanced field that heavily relies on mathematics and computer science concepts. Therefore, having a comfortable grasp of these prerequisites is crucial for comprehending AI concepts and theories.
If you find yourself lacking confidence in any of these subjects, the following list comprises top-rated courses that can aid in strengthening your foundation.
Here are some recommended courses to enhance your foundational knowledge in the prerequisite subjects for AI:
Probability: “Fat Chance: Probability from the Ground Up” from Harvard University
Statistics: “Fundamentals of Statistics” from MIT (Massachusetts Institute of Technology).
Linear Algebra: “Linear Algebra 18.06” from MIT.
Calculus: “Single Variable Calculus” and “Multivariable Calculus” from MIT.
Programming: Consider taking a Python programming course from platforms like Codecademy, or explore other highly-rated Python courses available online.
These courses will provide you with the necessary background to confidently approach advanced topics in artificial intelligence.
Having a foundational understanding of the prerequisite courses mentioned above will significantly ease your comprehension of the following AI courses. While all the prerequisite courses, except for Codecademy, offer free video resources, it is essential to actively engage with the material. Rather than passively watching videos, focus on solving a variety of problems in these subjects. This hands-on approach will reinforce your understanding and prepare you effectively for the challenges presented in AI courses.
The 7 Best AI Courses for 2024
| Rank | Title Link | Platform | Rating | Level |
|---|---|---|---|---|
| 1 | AI For Everyone | Coursera | 4.8 | Beginner |
| 2 | Artificial Intelligence Nanodegree | Udacity | 4.8 | Beginner-Intermediate |
| 3 | Professional Certificate in Computer Science for Artificial Intelligence | edX | 4.9 | Intermediate |
| 4 | Deep Learning Specialization | Coursera | 4.9 | Intermediate |
| 5 | Self-Driving Cars with Duckietown | edX | 4.9 | Intermediate |
| 6 | Natural Language Processing Specialization | Coursera | 4.6 | Intermediate |
| 7 | Artificial Intelligence | OpenCourseWare | 4.8 | Intermediate |
Course Overview:
1. AI For Everyone
Rating: 4.8 Pricing: Free-$49.99/month
Level: Beginner Course Link: Enroll
Best for: individuals new to AI seeking a comprehensive, non-technical introduction to the field.
Overview: Instructed by Andrew Ng, renowned for creating the Stanford Machine Learning class, this course offers a broad, non-technical overview of AI. It is ideal for those looking to gain a comprehensive understanding of what AI entails, its common misconceptions, and its practical applications. If you are interested in the technical implementation of AI solutions, other courses on this list might be more suitable. Andrew Ng simplifies the complexities of AI using accessible language, enabling participants to engage with practitioners and discuss AI knowledgeably.
Syllabus:
- What is AI?
- Building AI Projects
- Building AI in Your Company
- AI and Society
Please note: The remaining recommendations in this article focus on technical courses that require prior knowledge in mathematics and programming. If you’re ready to begin your AI journey with a non-technical approach, you can enroll in AI For Everyone.“
2. Artificial Intelligence Nanodegree
| Course Name | Artificial Intelligence Nanodegree |
|---|---|
| Best for | Individuals interested in acquiring a broad spectrum of AI techniques from leading experts in the field. |
| Overview | Co-created by Peter Norvig, the author of the widely used AI textbook ‘Artificial Intelligence: A Modern Approach,’ this course provides a condensed yet comprehensive overview of AI techniques. The curriculum closely aligns with Norvig’s textbook and covers key topics in AI. Participants will engage in practical projects, including creating a sudoku solver, a forward planning agent, an adversarial game-playing agent, and a part of speech tagging model. These projects serve as valuable portfolio pieces, showcasing your proficiency in AI. |
| Syllabus | 1. Introduction to Artificial Intelligence 2. Classical Search 3. Automated Planning 4. Optimization Problems 5. Adversarial Search 6. Fundamentals of Probabilistic Graphical Models |
| Note | While this course offers a strong foundation in various AI techniques, it does not cover machine learning. For machine learning expertise, consider exploring the next course recommended in this list. |
| Enrollment Link | Enroll in the Artificial Intelligence Nanodegree |
3. Professional Certificate in Computer Science for Artificial Intelligence
| Course Name | Professional Certificate in Computer Science for Artificial Intelligence |
|---|---|
| Rating | 4.9 |
| Pricing | Free-$348 |
| Level | Intermediate |
| Course Link | Enroll |
| Best for | Learners aiming to establish a solid computer science foundation and delve into AI without prior CS knowledge. |
| Overview | This two-part professional certificate offered by edX tracks Harvard’s CS50 and CS50AI courses, bridging the gap for learners lacking prerequisite CS knowledge. The course emphasizes the importance of understanding fundamental CS concepts to grasp AI principles. It covers programming in C and Python, but prior programming familiarity is recommended. The certificate demands completion of both courses, ensuring a comprehensive understanding of AI concepts. The course content includes topics ranging from algorithms and data structures to neural networks and natural language processing. The format includes engaging on-stage presentations and code demonstrations, delivered by excellent lecturers. However, the course expects active engagement without extensive hand-holding, mirroring the rigor of a college-level course. |
| 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 | |
| Syllabus (Continued) | 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 course offers engaging on-stage presentations and code demonstrations, delivering challenging and insightful content, characteristic of an actual college course. |
Ready to enhance your computer science foundation and explore AI? Enroll in the Professional Certificate in Computer Science for Artificial Intelligence.
4. Deep Learning Specialization
| Course Name | Deep Learning Specialization |
|---|---|
| Rating | 4.9 |
| Pricing | Free-$49.99/month |
| Level | Intermediate |
| Course Link | Enroll |
| Best for | Students with some experience looking to explore deep learning within the AI domain. |
| Overview | This Specialization, led by Andrew Ng, delves deeply into advanced neural networks, specifically focusing on the realm of deep learning. While deep learning is a subset of AI, it has played a pivotal role in significant AI advancements. The course offers comprehensive insights into recent developments in deep learning, providing valuable knowledge on constructing, 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 | |
| Syllabus (Continued) | 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 | |
| Syllabus (Continued) | Course 3: Structuring Machine Learning Projects |
| – ML production workflow | |
| – Error analysis procedures | |
| Syllabus (Continued) | Course 4: Convolution Neural Networks |
| – Foundations of Convolutional Neural Networks | |
| – Deep Convolutional Models | |
| – Object Detection | |
| – Special Applications: Face Recognition and Neural Style Transfer | |
| Syllabus (Continued) | Course 5: Sequence Models |
| – Recurrent Neural Networks | |
| – Natural Language Processing and Word Embeddings | |
| – Sequence Models and Attention Mechanism | |
| – Transformer Network | |
| Note | The Specialization covers computer vision and natural language processing, crucial AI subtopics. For a broader AI overview, it’s recommended to explore fundamental AI concepts before enrolling in this deep learning series. |
5. Self-Driving Cars with Duckietown
| Course Name | Self-Driving Cars with Duckietown |
|---|---|
| Rating | 4.9 |
| Pricing | Free, $399 for materials |
| Level | Intermediate |
| Course Link | Enroll |
| Best for | Intermediate learners passionate about autonomous driving and hands-on learning. |
| Overview | This course stands out by combining online learning for self-driving vehicles with a purchasable driving robot kit priced at $399. The kit includes a Duckiebot vehicle, road mat, cones, and signs, enabling hands-on training in autonomous driving models. The course extensively covers controlling your Duckiebot, including lane driving, intersection navigation, and object detection, all using Python and machine learning frameworks like PyTorch or TensorFlow. The syllabus covers various aspects of autonomous vehicles, including modeling, control, robot vision, object detection, state estimation, localization, planning, and reinforcement learning using an NVIDIA Jetson Nano, a specialized AI IoT computer. By course completion, you’ll gain expertise in robotics, IoT, and reinforcement learning (e.g., Q Learning), equipping you to apply these skills in diverse IoT and robotics applications. |
| Syllabus | – Introduction to autonomous vehicles |
| – Towards autonomy | |
| – Modeling and control | |
| – Robot vision | |
| – Object detection | |
| – State estimation and localization | |
| – Planning | |
| – Learning by reinforcement | |
| Note | The course offers a unique hands-on approach to autonomous driving, providing practical experience in building and programming self-driving vehicles. The course material is paired with a Duckiebot starter kit, enhancing the learning experience. |
Ready to dive into the world of self-driving cars? Enroll in Self-Driving Cars with Duckietown.
6. Natural Language Processing Specialization
| Course Name | Natural Language Processing Specialization |
|---|---|
| Rating | 4.6 |
| Pricing | Free-$49.99 |
| Level | Intermediate |
| Course Link | Enroll |
| Best for | Individuals with some experience and a keen interest in the NLP branch of AI. |
| Overview | Natural Language Processing (NLP) is a fundamental aspect of intelligent systems, enabling the analysis and interpretation of human language. This Specialization is designed to equip learners with the tools and techniques necessary to build advanced NLP systems. Created by the team behind the Deep Learning Specialization, this course series is well-structured and informative. It is divided into courses focusing on essential NLP model types: Classification, Probabilistic, Sequence, and Attention. These models have significantly enhanced NLP, forming the basis for some of the most advanced language models 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 | |
| Syllabus (Continued) | Course 2: Probabilistic Models |
| – Autocorrect | |
| – Part of Speech Tagging and Hidden Markov Models | |
| – Autocomplete and Language Models | |
| – Word Embeddings and Neural Networks | |
| Syllabus (Continued) | Course 3: Sequence Models |
| – Neural Networks for Sentiment Analysis | |
| – Recurrent Neural Networks for Language Modeling | |
| – LSTMs and Named Entity Recognition | |
| – Siamese Networks | |
| Syllabus (Continued) | Course 4: Attention Models |
| – Neural Machine Translation | |
| – Text Summarization | |
| – Question Answering | |
| – Chatbot | |
| Note | While not a general introduction to AI, this Specialization provides valuable expertise in the NLP domain. Graduates are equipped with the skills necessary to build startup ventures centered around NLP or pursue careers within the industry. |
Ready to enhance your NLP skills? Enroll in the Natural Language Processing Specialization.
| Course Name | Artificial Intelligence |
|---|---|
| Rating | 4.8 |
| Pricing | Free |
| Level | Intermediate |
| Course Link | Enroll |
| Best for | Self-starters seeking a high-quality, entirely free AI course. |
| Overview | This free AI course is offered by MIT OpenCourseWare, providing access to MIT courses with comprehensive materials including homework, exams, solutions, lecture notes, and full lecture videos. Ideal for self-motivated learners, this course is presented by Patrick Henry Winston, an esteemed MIT professor, in a live recorded university setting. Although lacking platform interactivity, auto-graded assignments, and certificates, the course covers a broad range of AI topics. It encompasses basic AI algorithms, machine learning, and probabilistic methods. Recorded in 2010, some recent developments are not included, but the foundational concepts remain relevant and form the basis of AI today. |
| Syllabus | – Reasoning |
| – Goal trees | |
| – Problem solving | |
| – Rule-based expert systems | |
| – Search | |
| – Games | |
| – Constraints | |
| – Interpreting line drawings | |
| – Learning | |
| – Representations | |
| – Architectures | |
| – The AI business | |
| – Probabilistic inference | |
| – Model merging | |
| – Cross-modal coupling | |
| Note | The course, while lacking recent developments, offers a comprehensive foundation in AI. Lectures are available through a YouTube playlist, with additional materials provided on the OpenCourseWare page, including notes, assignments, exams, and solutions. Suitable for learners who prefer self-paced, content-rich courses without interactivity features. |
Ready to dive into the world of AI with MIT’s esteemed course? Enroll in Artificial Intelligence.
Last remarks
It can be intimidating to study AI from scratch, but keep in mind that anyone can learn anything with perseverance, regardless of background or educational attainment.
Please leave a comment below if you’ve taken any of the aforementioned courses and would like to share your experience or if you believe there is an important offering that I overlooked!
