best ai and machine learning courses
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Top 10 Best AI and Machine Learning Courses of 2024

Tired of feeling like an amateur in a world dominated by smart machines? Well, buckle up, because we’re about to take you on an electrifying journey into the realm of artificial intelligence (AI) and machine learning (ML). Whether you’re a tech enthusiast or a professional craving the latest AI breakthroughs, this guide will help you choose a course to accelerate your learning and propel you toward success in this cutting-edge field.

What makes the best AI and machine learning courses?

A graohic showing a brain and neurons connecting, which draws a parallel to how AI and machine learning works.

The course you end up choosing should provide an easily understandable and up-to-date curriculum that covers both theoretical concepts and practical applications. It should delve into various subfields such as supervised and unsupervised learning, deep learning, natural language processing, computer vision, as well as prompting among others (you can also read more about those in our articles about foundation models and large language models).

You’ll also have to keep in mind your background in the field when choosing the course. Novices and people from a non-tech background should always check if the courses are beginner friendly and if they provide a solid foundation while gradually introducing complex topics. Also, the course should offer you a certificate upon completion that guarantees you mastered the knowledge and the skills covered in the course,

The delivery format of the course is another crucial aspect to consider, and the choice will depend on your preference. While textual materials can be valuable for some people, video-based courses often offer a more engaging learning experience, allowing learners to visualize concepts and demonstrations. The instructor of the course is another key factor. Is this person someone reputable in the field? Do they have the necessary qualifications to teach you these things? Down below, we break down some of the most popular courses and pick out the best ones for your needs!

Text-based courses offer you a more flexible and self-paced learning experience, allowing you to review materials at your own speed and refer back to specific sections easily. Additionally, text courses provide an opportunity to engage deeply with the content, as you’ll need to actively read, comprehend, and internalize the information.

Video-based courses provide a dynamic and visually stimulating learning environment. Videos enable you to see demonstrations, visualizations, and real-life examples, which can enhance understanding and retention of complex concepts. Moreover, video courses offer the benefit of observing instructors’ body language, tone, and gestures, adding a personal touch to the learning experience.

All of the courses we reviewed are based on the mixed format approach, with video-delivered lessons followed by text instructions and explanations, giving you the best of both worlds.

Best AI and Machine Learning Courses

Here is a list of the best AI and machine learning courses.

Introduction to AI course by Coursera


Introduction to Artificial Intelligence (AI) is a course offered by Coursera that provides a great introduction to the world of AI, covering its concepts, applications, and ethical considerations. The course is part of the larger IBM Applied AI Professional Certificate. This beginner-friendly course is designed for individuals with or without a technical background and does not require ANY programming or computer science expertise. The course is delivered through a combination of video lectures, readings, quizzes, and assessments, allowing learners to study at their own pace and on a flexible schedule, but should take you approximately 9 hours to complete. The instructor, Rav Ahuja, is a Global Program Director at IBM.

Key topics covered

  • Some of the topics covered in this Coursera course are the fundamentals of AI, its applications and use cases, and ethical considerations surrounding AI.
  • Completing the course, you will gain insights into concepts like machine learning, deep learning, and neural networks.
  • The course also provides advice from industry experts on learning and starting a career in AI.
  • Additionally, during the course you will have the opportunity to engage in a mini-project, simulating real-world projects, to demonstrate AI in action.


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  • 7-day free trial
  • $44 per month to continue learning after the trial ends

Bottom line

Upon successfully completing the course, learners will receive a shareable certificate, which can be added to their LinkedIn profile or included in their resume or CV. This certification validates their understanding and competence in the field of AI and can enhance their career prospects in industries where AI is becoming increasingly relevant.

Machine learning & AI courses by Google Cloud

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Apart from the impressive AND free AI and Machine Learning with Google Cloud Youtube playlist, and the beginner-friendly Machine Learning Crash Course, Google offers a bundle of courses on machine learning and artificial intelligence titled Machine Learning Engineer Learning Path. The objectives of the Path are to equip learners with the latest technology implementations, in-depth data modeling, and deep learning techniques. By choosing these courses, you’ll gain access to industry-leading instructors and a wealth of resources provided by Google Cloud, ensuring a high-quality learning experience. These courses are delivered through a combination of text-based resources and video-based instruction, providing a flexible and engaging learning experience.

Key topics covered

  • Topics covered include Vertex AI, BigQuery, TensorFlow, Cloud Vision, Natural Language API, and more.
  • Learners will delve into foundational data, ML, and AI tasks in Google Cloud, as well as advanced concepts such as machine learning operations (MLOps), ML pipelines, and building and deploying machine learning solutions on Vertex AI.
  • The courses also emphasize hands-on experience in areas like computer vision fundamentals, natural language processing, recommendation systems, and production machine learning systems, enabling learners to gain practical skills required for job roles such as Data Scientist, Machine Learning Engineer, or Contact Center Engineer.


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  • Monthly subscription for all Google Cloud courses is $29, with a free 30-day trial
  • Annual subscription is $299

Bottom line

Learners can trust that the courses are taught by experienced professionals who possess in-depth knowledge of the subject matter. Upon successful completion of the courses, you can earn skill badges and even pursue certification as a Professional Machine Learning Engineer, further validating your expertise in the field.

AI for Everyone course by DeepLearning

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The course “AI for Everyone” is an introductory 6-hour course designed to help individuals understand the concepts and applications of artificial intelligence (AI) regardless of their technical background. It is delivered through a combination of text-based content and video lectures. The course is led by Andrew Ng, a renowned figure in the field of AI and the founder of DeepLearning.AI, who co-founded Google Brain and Coursera and also led AI research at Baidu. As the lecturer is a prominent figure within the Coursera Community, the course is delivered via the Coursera platform.

Key Topics Covered

  • Key features of the course include exploring the workflow of machine learning and data science projects, understanding AI terminology and strategy, and gaining insights into the impact of AI on society.
  • The course covers topics such as machine learning, deep learning, and the role of AI in various industries.
  • It also includes case studies and examples to illustrate the capabilities and limitations of AI.


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  • Free for audit only
  • $44 to obtain a certificate

Bottom line

What sets this course apart is its focus on providing a non-technical perspective on AI, allowing participants to grasp the fundamentals and implications of AI technology without the need for extensive technical knowledge. The combination of Andrew Ng’s expertise, the course’s practical approach, and the opportunity to earn a certification makes it an attractive choice for anyone seeking a solid foundation in AI. The program is delivered through a combination of live virtual sessions and on-campus interactions, allowing participants to engage with renowned MIT faculty experts and fellow peers from around the world.

MIT’s Professional Certificate Program in Machine Learning & Artificial Intelligence

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The Professional Certificate Program in Machine Learning & Artificial Intelligence offered by MIT Professional Education is a comprehensive and immersive learning experience designed for professionals seeking to enhance their knowledge and skills in this rapidly evolving field. The program equips participants with the best practices and actionable knowledge necessary to excel in the AI revolution. The program is intended for professionals with at least three years of experience and technical background in areas like computer science, statistics, physics, or electrical engineering. It is also suitable for individuals whose work involves data analysis or those seeking to gain a deeper understanding of machine learning and artificial intelligence. The program is delivered via live video and on the MIT campus.

Key topics covered

  • The Professional Certificate Program in Machine Learning & Artificial Intelligence covers topics such as natural language processing, predictive analytics, deep learning, and algorithmic methods.
  • Participants will gain a solid foundation in machine learning concepts, including probability, statistics, classification, regression, and optimization.
  • The program explores advanced topics such as deep learning architectures and algorithms used in reinforcement learning.
  • Additionally, participants will learn about the application of AI in computational design and manufacturing, as well as strategies for effective AI system development and deployment.


  • Not disclosed

Bottom line

This is an excellent choice for professionals seeking to stay ahead in the AI-powered future. With its comprehensive curriculum, distinguished faculty, interactive delivery format, and industry-recognized certification, this program provides a unique opportunity to acquire the skills and knowledge needed to excel in the field of machine learning and artificial intelligence. Furthermore, as program alumni, individuals receive continuing education units (CEUs), a 15% discount on future MIT Professional Education courses, membership in the exclusive MIT Professional Education LinkedIn group, and a complimentary one-year subscription to the MIT Technology Review.

Machine Learning for Everybody: A Youtube course

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With over 1.4 million views on Youtube, freeCodeCamp’s course on Machine learning, developed by Kylie Ying, covers the basics of machine learning and demonstrates how to implement various concepts using TensorFlow. The course is entirely video based and almost 4 hours long.

Key topics covered

  • The course includes hands-on exercises and provides code and resources for supervised learning (classification and regression) and unsupervised learning.
  • Participants will learn about features, training models, and techniques such as K-Nearest Neighbors, Naive Bayes, Logistic Regression, Support Vector Machines, Neural Networks, and more.
  • The course also explores practical implementations using TensorFlow and covers topics like linear regression, K-means clustering, and principal component analysis.


  • Free

Bottom line

This four-hour course is a great choice for anyone wanting to understand the computer science basics of machine learning. Judging from the numerous comments of praise, you will leave the course with a solid grasp of the machine learning algorithm.

Artificial Intelligence Foundations: A LinkedIn course

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The course “Artificial Intelligence Foundations: Machine Learning” is an introductory course that delves into the fascinating field of machine learning, which is a branch of artificial intelligence. This course is designed to provide learners with an understanding of the machine learning lifecycle and the steps involved in building systems that can learn from data and make decisions autonomously. This course is intended for beginners who are interested in understanding the fundamentals of machine learning. It does not assume prior knowledge in the field, making it accessible to a wide audience.

The course is delivered through a combination of text-based content and video lectures, allowing learners to grasp the concepts effectively. It covers a wide range of topics, including supervised, unsupervised, and reinforcement learning methods. Throughout the course, instructor Kesha Williams emphasizes the importance of sourcing and preparing data, as well as selecting the most suitable learning algorithm for specific projects.

Key topics covered

  • The course covers key topics such as supervised, unsupervised, and reinforcement learning methods. It also emphasizes the importance of data sourcing and preparation, as well as selecting the most suitable learning algorithm for specific projects.
  • Furthermore, the course explores evaluating model performance using standard metrics and guides learners in building a machine-learning pipeline for streamlining the development process.
  • Key features of this course include chapter quizzes to reinforce learning, access to the course materials on tablets and phones for convenient learning on the go, and a certificate of completion upon finishing the course successfully. The certificate can be shared on LinkedIn and other platforms to showcase the learner’s skills and enhance their professional profile.


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  • Free 1-month trial
  • $20 if you only buy this course
  • Annual and Monthly payment plans for LinkedIn earning

Bottom line

This course stands out as an excellent choice for individuals seeking to understand artificial intelligence and machine learning and build a solid foundation in this exciting field. No data science or knowledge of programming languages necessary!

Deep Learning course by Lightning AI

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Deep Learning Fundamentals is a video course that teaches machine learning and deep learning using a modern open-source stack. Led by Sebastian Raschka, a renowned author and professor, the course consists of 10 units with bite-sized videos, quizzes, and exercises. It covers essential concepts of deep learning, how to design experiments using PyTorch, and how to write efficient code with PyTorch Lightning. The course is intended for individuals who have some programming experience in Python and want to build classifiers for various types of data and optimize model performance.

Key topics covered

  • Through 10 units, participants will delve into the core concepts of deep learning, including machine learning fundamentals, designing deep learning experiments with PyTorch, and writing efficient code using PyTorch Lightning.
  • The course explores various techniques and tools for building classifiers for different types of data such as tables, images, and text.
  • Participants will also learn how to optimize model performance, fine-tune models effectively, and gain practical insights into organizing code, logging, and evaluating models.
  • Additionally, the course provides an introduction to computer vision, natural language processing, and large language models, along with techniques for speeding up model training.


  • Free

Bottom line

This in-depth course is a great choice for everyone wanting to delve deeper into machine learning and showcase their knowledge, as the course offers certification in the form of an optional course completion badge, which can be shared on LinkedIn.

Data Science: Machine Learning course by EDX

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The “Data Science: Machine Learning” course offered by HarvardX is an 8-week program designed to teach participants about the popular methodologies and algorithms used in machine learning. Through this course, learners will gain an understanding of machine learning concepts and principles, with a specific focus on building a movie recommendation system. The course is delivered online and can be accessed through self-paced learning, allowing participants to progress at their own speed. The course features video lectures, text materials, and practical exercises to enhance learning.

The instructor of the course is Rafael Irizarry, a Professor of Biostatistics at Harvard University.

Key topics covered

  • The course dives into the concept of training data and how to use it to uncover predictive relationships.
  • Participants will gain practical knowledge in building a movie recommendation system, a popular application of machine learning.
  • Additionally, the course explores important concepts such as overtraining and regularization, equipping learners with techniques to avoid common pitfalls.
  • By the end of the course, participants will have a solid understanding of machine learning principles, algorithms, and their practical implementation, providing them with valuable skills for data analysis and predictive modeling.


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  • Free to audit
  • $100 to attain a certificate

Bottom line

The course is intended for individuals interested in data science, particularly those who want to gain expertise in machine learning. It is suitable for beginners who have a basic understanding of data analysis and statistics. The course is part of the Professional Certificate Program in Data Science offered by HarvardX, making it an excellent choice for individuals looking to develop their skills in this field.

Machine Learning and AI Fundamentals course by Codecademy

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The “Machine Learning and AI Fundamentals” skill path offered by Codecademy focuses on essential concepts such as Python programming, probability, linear algebra, statistics, matplotlib, pandas, and more. Through a carefully curated curriculum comprising nine units, 35 lessons, 25 projects, and 26 quizzes, learners will develop a strong foundation in data literacy, programming, mathematics, statistics, and visualization.

One of the key advantages of the skill path is its hands-on approach, allowing learners to actively write code in Codecademy’s interactive platform.

Key topics covered

  • The skill path begins by introducing learners to the foundations of Machine Learning and AI Engineering, followed by an exploration of data literacy principles.
  • As they progress, learners dive into Python fundamentals, gaining practical skills to tackle real-world challenges and analyze data related to U.S. medical insurance costs through a portfolio project.
  • Additionally, the curriculum covers advanced topics like Python Pandas for ML/AI Engineers, exploratory data analysis in Python, and the mathematics and statistics underlying machine learning models.
  • The platform also provides AI-driven recommendations, guiding learners on what to review to reinforce their knowledge and stay on track. Practice projects and auto-graded quizzes further enhance the learning experience, while a certificate of completion recognizes the learner’s achievement upon finishing the course.


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  • Basic Plan is free
  • Plus Plan is $17.49 per month
  • Pro Plan is $27.47 per month

Bottom line

Reviews from learners testify to the effectiveness of Codecademy’s learning model, appreciating its interactive nature and the flexibility it offers in terms of study schedules. Whether you aim to upgrade your skill set, differentiate yourself in your career, or gain practical knowledge in Machine Learning and AI, this course will equip you with the necessary tools and expertise.

Understanding Machine Learning course by DataCamp

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DataCamp’s Understanding Machine Learning course is designed for beginners seeking an introduction to the core concepts of machine learning. This course provides an overview without the need for coding, making it accessible to individuals from diverse backgrounds.

Key topics covered

  • The course covers essential topics such as the definition of machine learning and its distinction from data science and artificial intelligence.
  • Learners will delve into the vocabulary specific to machine learning and gain a solid understanding of its practical applications.
  • Through hands-on exercises, participants will cement their knowledge and develop an appreciation for how machine learning powers technologies like self-driving cars and personalized recommendations on platforms such as Amazon.
  • Additionally, the course explores the underlying principles of machine learning, including its workflow for building models. Learners will gain insights into supervised and unsupervised learning techniques and understand their respective use cases.
  • The course also touches upon deep learning, focusing on popular applications such as computer vision and natural language processing (NLP). Furthermore, learners will gain an awareness of the limitations and potential biases associated with machine learning.


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  • Basic Plan is free
  • Premium Plan is $25 per month
  • Teams Plan is $25 per user per month
  • Enterprise Plan is custom-priced

Bottom line

With a 4.6+ rating and positive reviews from numerous learners, this course has garnered praise for its ability to provide a strong foundational understanding of machine learning. By completing this course, learners will be equipped with the essential knowledge to embark on their journey in the field of machine learning.

What are the best artificial intelligence and machine learning courses?

When it comes to the best artificial intelligence and machine learning courses, the ideal choice may vary depending on whether you come from a technical or non-technical background, as well as your level of experience.

For beginners with limited technical knowledge, the “DeepLearning AI for everyone” course is highly recommended. This course provides a great introduction to AI and deep learning concepts, making it accessible for those starting from scratch, regardless of their technical background.

On the other hand, for individuals with a technical background and some programming experience, “MIT’s Professional Certificate Program in Machine Learning & Artificial Intelligence” is an excellent option. This program delves deeper into advanced machine learning and AI techniques, catering to those with prior technical knowledge and experience.

What are the best artificial intelligence and machine learning certifications available online? 

A graphics showcasing an online graduate with their diploma and certificate.

When it comes to the best artificial intelligence and machine learning certifications available online, the choice should align with your background and level of experience in the field.

For experienced professionals looking to enhance their credentials, the “Google Cloud Professional Data Engineer” certification is highly regarded. This certification validates skills in designing and building data processing systems using AI and ML technologies.

On the other hand, for individuals with a non-technical background who seek recognition in the field, the “Artificial Intelligence Foundations: A LinkedIn course” is a valuable choice. This course provides a solid foundation in AI concepts, allowing non-technical professionals to establish themselves in the AI and ML domain without requiring extensive technical knowledge.

What is the current state of the AI and machine learning job market?

A person typing into a laptop.

The current state of the AI and Machine Learning job market is highly promising. According to recent reports, the global AI market was valued at around $62.35 billion in 2020, and it is projected to grow at a compound annual growth rate (CAGR) of nearly 40% until 2026. The demand for AI professionals has been skyrocketing, with job postings in this field increasing by over 300% in the past five years.

The United States leads in AI job postings, accounting for approximately 50% of the total demand. The demand for AI professionals extends beyond the technology sector, as industries such as legal, education, medicine, finance, and manufacturing are increasingly adopting AI technologies.

The average salary for AI professionals is significantly higher than in other IT fields, and roles such as machine learning engineers and data scientists with AI expertise are highly sought after. The AI job market is expected to continue growing rapidly, offering a wide range of career opportunities for those with AI skills.

Is coding knowledge required for AI and machine learning?

A computer with some code on the screen.

Yes, coding is essential for AI and machine learning. However, it is important to note that individuals from non-technical backgrounds can still pursue a career in Artificial Intelligence and Machine Learning. Non-technical AI roles do exist and are discussed below.

Machine learning models are implemented through coding, and understanding how to write code allows programmers to grasp the functioning of algorithms and optimize them effectively. While languages like C++, Java, and Python are commonly used, knowledge of languages like R, Lisp, and Prolog becomes important for specific machine-learning tasks.

Although prior knowledge of languages like HTML and CSS is not necessary, starting with relevant languages like Python is recommended. While the ultimate goal of machine learning is to enable computers to learn autonomously, foundational skills in probability, statistics, linear algebra, and calculus are required to understand the fundamentals and work with data matrices and vector operations.

While it’s crucial to understand the underlying concepts, learning to write code can come later in the learning journey, with a focus on supervised learning, unsupervised learning, reinforcement learning, and neural networks.

I come from a non-technical background. Is it possible to become an AI and machine learning professional? What is the route?

A graphic representing networking.

It is absolutely possible for individuals from non-technical backgrounds to pursue a career in Artificial Intelligence and Machine Learning. The route to becoming an AI and Machine Learning professional involves focusing on strategy and leadership rather than hands-on coding. Non-technical AI roles, such as enterprise sales of AI/ML products and services or AI/ML product management, provide opportunities for non-technical professionals to contribute to the field.

To make this transition, mastering LinkedIn for networking, gaining AI/ML business fundamentals, and excelling in the non-technical AI interview process are crucial steps. By following this route, non-technical professionals can successfully enter the world of AI and Machine Learning.

Final thoughts

Choosing the best AI and ML course is not merely about gaining knowledge and skills; it’s about making a profound decision that shapes your future. In a rapidly evolving field like AI, where advancements are constant, the right course should empower you to adapt and thrive. When making your decision, consider your background, learning preferences, and career goals to find the course that best suits your needs.

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