According to the International Monetary Fund (IMF), artificial intelligence (AI) is set to affect nearly 40% of all jobs. With only a small percentage of workers with skills in this field, the rush to learn the ins-and-outs of AI is ever more important. Here are top free courses to master AI.

While there are an increasing number of full-fledged AI degree programs, including within business schools, some students may be looking for a simpler or self-paced route. Several top tech firms and universities offer free online courses that serve as an introduction to AI technologies.

The AI revolution has set the tech world on fire during the past years, especially after 2022. And the trend is not stopping anytime soon.

According to the World Economic Forum's Future of Jobs Report, 50% of all work tasks will be handled by machines by 2025. In the words of Professor Klaus Schwab WEF's Founder and Executive Chairman:

"We have the tools at our disposal. The bounty of technological innovation which defines our current era can be leveraged to unleash human potential.”

The AI revolution will create 97 million jobs worldwide, but it will also destroy almost as many, leaving some communities at risk. This shift in the nature of jobs, as predicted in the report, will worsen the current percentage of inequality.

The report also indicates that millions of workers would need to be re-skilled to adapt to this shift. Governments, on the other hand, would have to ensure "stronger safety nets" for displaced workers. 

“AI is providing people with on-demand learning anywhere they are at any time of day on any day,” says Jared Curham, a professor of work and organisational studies at MIT’s Sloan School of Management. 

As digital transformation driven by AI progresses, both executives and employees are considering its impact on jobs and the skills required to remain competitive. This is where the importance of AI-related upskilling and reskilling becomes crucial.

Key ways to mastering AI

Mastering AI is critical in today’s world - for all of us, our families, our businesses and our society. We do this looking at the importance of taking a structured approach that combines theoretical knowledge with practical application. 

Here are some of the best ways to learn, understand, and master AI, along with 8 free courses to help you on your journey:

Best Ways to Learn, understand and master AI

  1. Build a Strong Foundation: Start with the basics of programming, mathematics, and statistics. These are crucial for understanding AI algorithms and concepts. 

Programming languages like Python and R are commonly used in AI because they provide powerful libraries for machine learning and data processing. For instance, learning Python's NumPy and Pandas libraries will help you work with large datasets, while libraries like TensorFlow or PyTorch allow you to implement AI algorithms. 

A good grasp of mathematics, especially linear algebra and calculus, is crucial for understanding how algorithms like gradient descent work in training machine learning models. Similarly, statistics help in making sense of data, which is at the core of AI.

Example: Start by taking courses on Python programming and learning how basic statistics are applied in data analysis, such as calculating probabilities or understanding distributions. This will prepare you for the more advanced AI topics that rely heavily on these foundations.

  1. Focus on Core AI Concepts: Learn about machine learning, deep learning, neural networks, and natural language processing. These form the backbone of most AI applications.

Machine learning involves teaching systems to learn from data and make decisions, while deep learning uses neural networks to handle more complex data, such as images or text. NLP helps in understanding and processing human language, which is essential for applications like chatbots or speech recognition.

Example: You could begin by learning supervised and unsupervised learning techniques, such as regression or clustering, and then move on to deep learning frameworks like convolutional neural networks (CNNs) for image classification or recurrent neural networks (RNNs) for text analysis.

  1. Hands-on Practice: Apply your knowledge through projects and coding exercises. This helps reinforce theoretical concepts and builds practical skills.

By experimenting with real-world datasets, you can learn how to clean data, apply algorithms, and tune models. For instance, using platforms like Kaggle or GitHub, you can participate in AI challenges or contribute to open-source AI projects, where you get a chance to work on data classification, regression models, or even NLP projects.

Example: Create a machine learning project where you predict housing prices based on historical data, using a regression model. Alternatively, work on a sentiment analysis project using NLP techniques to classify positive and negative reviews from a dataset of customer feedback.

  1. Stay Updated: AI is a rapidly evolving field. Keep up with the latest developments by following AI publications, attending webinars, and participating in online communities.

Example: Subscribe to reputable AI journals such as arXiv for research papers or follow AI-focused platforms like OpenAI’s blog to understand the latest advancements in generative AI models. Engaging in discussions on platforms like Reddit's AI forums or Stack Overflow can help clarify doubts and learn from other practitioners' experiences.

  1. Specialise: After gaining a broad understanding, focus on specific areas of AI, such as computer vision, robotics, or generative AI, depending on the interest.

Specialisation allows us to focus on specific AI applications and dive deeper into advanced techniques. For instance, computer vision focuses on teaching machines to interpret visual data and is used in facial recognition or autonomous driving. Generative AI, on the other hand, deals with AI that can generate new content, such as creating realistic images or writing text.

Example: If you choose to specialise in computer vision, you could work with libraries like OpenCV to build image classification models. If generative AI interests you, you could explore GANs (Generative Adversarial Networks) and use them to create new, synthetic images based on real-world data.

List of free courses to master AI

AI For Everyone by DeepLearning.AI (Coursera)

  • About: AI For Everyone by DeepLearning.AI on Coursera is an introductory course designed for anyone interested in understanding artificial intelligence, regardless of their technical background. 
  • Taught by Andrew Ng, a prominent figure in AI, the course breaks down complex AI concepts into easily digestible lessons, making it accessible to business leaders, professionals, and non-technical individuals. 
  • Topics: It covers essential topics such as what AI can and cannot do - AI terminology, and how it’s applied in industries today. 
  • Focus: With a focus on real-world applications, the course helps learners grasp the potential of AI to transform businesses and society, providing them with the knowledge needed to collaborate effectively with AI teams. 
  • Duration: The course is structured to take around six hours to complete, offering flexibility for learners to progress at their own pace.

Google AI Courses

  • Google offers a comprehensive upskilling program through five distinct courses designed to help learners master generative AI from the ground up. 
  • The courses start with an introduction to AI and machine learning principles, progressing to more advanced topics that provide a solid understanding of AI applications in real-world scenarios. 
  • One of the key offerings, "Google AI for Everyone," is a self-paced course that takes about four weeks to complete and is taught by Laurence Moroney, Google’s lead AI advocate. 
  • The curriculum covers essential AI concepts, making it accessible for both technical and non-technical audiences. Learners are equipped with practical knowledge and tools to apply AI in various sectors. 
  • https://lnkd.in/eW5k4DVz

Microsoft AI Course 

  • About: Microsoft AI for Beginners is a comprehensive, free course that introduces learners to the fundamentals of artificial intelligence. 
  • Topics: Starting with the basics, the course gradually explores advanced topics like neural networks and deep learning, offering a solid foundation in machine learning concepts and the ethical considerations surrounding AI. 
  • Course Design: Designed for beginners, it provides hands-on experience with Microsoft Azure’s AI tools, allowing participants to apply their knowledge in practical scenarios. 
  • Availability: The course is available offline through Docsify, making it accessible to learners who may not have consistent internet access, further enhancing its flexibility. https://lnkd.in/eKJ9qmEQ

     

Introduction to AI with Python

  • Harvard University’s Introduction to Artificial Intelligence with Python is a 7-week, intensive course that delves into the foundational concepts and algorithms of AI. 
  • Taught by renowned computer scientist David J. Malan, this course is designed to introduce learners to the technologies behind AI, such as machine learning libraries and game-playing engines. 
  • Over 10-30 hours per week, participants engage in hands-on activities that explore core AI principles, including building AI algorithms and understanding how they are applied in real-world scenarios.  https://lnkd.in/g4Sbb3nQ

     

Prompt Engineering for ChatGPT

  • Vanderbilt University’s Prompt Engineering for ChatGPT is a 6-module course designed for beginners who want to master the art of writing effective prompts for ChatGPT. 
  • The course starts by teaching basic prompting techniques and gradually progresses to more advanced strategies, helping learners understand how to better communicate with and manipulate AI responses. 
  • By the end of the course, participants will have the skills to craft precise prompts, enhancing their ability to leverage ChatGPT for various tasks.  https://lnkd.in/d-rCb-AM

ChatGPT Prompt Engineering for Devs

  • Offered in collaboration between OpenAI and DeepLearning.AI, the ChatGPT Prompt Engineering for Devs course is taught by Isa Fulford and Andrew Ng. This course is specifically designed for developers who want to enhance their ability to create effective prompts for ChatGPT. 
  • It begins with best practices for prompt engineering and leads participants through hands-on exercises to improve their skills. 
  • By the end of the course, learners will have a deeper understanding of how to design prompts that maximise the efficiency and relevance of AI responses, a valuable skill in various AI-driven applications.  https://lnkd.in/gtGc5Znp

AWS Skill Builder

  • The AWS Skill Builder platform offers over 100 free AI courses and resources, making it a valuable learning hub for those interested in machine learning, generative AI, and foundational models. 
  • With nearly 7 hours of free content covering AI basics, AWS Skill Builder provides a well-rounded introduction to AI concepts. 
  • These courses not only cover machine learning principles but also offer practical insights into using AWS’s powerful AI tools. 

IBM AI Foundations for Everyone

  • IBM’s AI Foundations for Everyone is a specialisation that includes three courses focused on AI basics, IBM Watson, and chatbot development. Designed for learners of all backgrounds, this course takes approximately three months to complete at a pace of two hours per week. 
  • It offers a certificate upon completion, giving learners not only foundational knowledge of AI but also a recognised credential. 
  • With a focus on practical applications, particularly with IBM’s AI tools, this course is ideal for anyone looking to develop a comprehensive understanding of AI.

Big Data, Artificial Intelligence, and Ethics

  • In the Big Data, Artificial Intelligence, and Ethics course from the University of California, Davis, learners are introduced to the vast opportunities and challenges posed by big data and AI.
  • Spanning four modules, this course covers the fundamentals of big data, its applications, and how tools like IBM's Watson are used in AI applications. 
  • The course also emphasises the ethical implications of AI, helping learners understand both the potential and limitations of these technologies. It’s a great starting point for those interested in the intersection of big data and AI.  https://lnkd.in/gVEf3Dvm

AI Applications and Prompt Engineering

  • The AI Applications and Prompt Engineering course, available on edX, provides an introduction to prompt engineering and explores the practical applications of AI. 
  • Learners begin by understanding basic AI concepts and move on to building their own AI-driven applications. 
  • This course is designed to help participants develop skills in prompt creation, which is essential for working effectively with AI tools like chatbots and automated systems. 
  • By the end of the course, learners will have gained practical experience in building and deploying AI applications.  https://lnkd.in/g2P9U_Bs

Elements of AI by University of Helsinki and Reaktor

  • The Elements of AI course, created by the University of Helsinki and Reaktor, is designed for a broad audience with no prior experience in AI or programming. 
  • This user-friendly, self-paced course covers the basics of AI, breaking down complex concepts into simple, digestible lessons. 
  • With interactive elements, learners can engage with the material and gain a solid understanding of AI without needing advanced mathematical or coding knowledge. 
  • This course is ideal for anyone looking to grasp the fundamentals of AI in a flexible, accessible format.

Additional Tips for Mastering AI

  • Set Clear Learning Goals: Define specific outcomes to achieve in your AI learning journey.

Without a well-defined plan, learning AI can become overwhelming. Beginners should start by identifying short-term goals, such as learning the basics of machine learning algorithms or mastering AI frameworks like TensorFlow or PyTorch. Long-term goals may include building a chatbot, becoming proficient in natural language processing (NLP), or understanding deep learning networks.

  • Build Projects: Apply knowledge to real-world problems through hands-on projects.

Starting with smaller projects, such as building a simple regression model to predict housing prices or creating a sentiment analysis tool for social media data, is recommended. As skills progress, they can take on more complex projects like developing recommendation systems or training deep learning models for medical image classification.

  • Join AI Communities: Engage with other learners and professionals to share knowledge and stay motivated.

Platforms like Reddit, Stack Overflow, or AI-specific groups provide valuable resources and mentorship opportunities. Regular participation in such communities allows learners to stay updated on trends and best practices, while also receiving feedback and motivation from others.

  • Read Research Papers: Familiarise with cutting-edge AI research to deepen understanding.

Websites like arXiv.org and Google Scholar offer access to the latest research in areas like deep learning and reinforcement learning. By reading papers from AI leaders, learners can better understand emerging methodologies and improve their theoretical knowledge.

  • Participate in AI Competitions: Participating in AI competitions, such as those on Kaggle, provides a unique opportunity to apply skills to real-world problems while competing with others. These competitions challenge participants to work with real datasets, solve complex problems, and meet deadlines, providing invaluable experience in AI development. 

Remember, mastering AI is a continuous process. The field is constantly evolving, so commit to lifelong learning and stay curious about new developments and applications of AI technology.

Frequently Asked Questions

Q. Why is mastering AI important for the future?

According to reports by the International Monetary Fund (IMF) and World Economic Forum, AI is expected to impact nearly 40% of jobs and handle 50% of work tasks by 2025. Mastering AI will help individuals stay competitive in the workforce and prepare for the creation of new jobs driven by AI technologies.

Q. What are the best ways to start learning AI?

To start learning AI, it’s essential to build a strong foundation in programming, mathematics, and statistics. From there, you can focus on core AI concepts like machine learning, deep learning, and natural language processing. Hands-on practice with real-world projects and staying updated with the latest AI developments will also help reinforce your knowledge.

Q. What are the key concepts to focus on when learning AI?

The key AI concepts include:

  • Machine learning (supervised and unsupervised learning)
  • Deep learning and neural networks
  • Natural language processing (NLP)
  • AI ethics and responsible AI These topics form the backbone of most AI applications

Q. How can I apply my AI knowledge in real-world projects?

You can apply your AI knowledge by building projects that solve real-world problems. For example, create machine learning models to predict housing prices or develop a sentiment analysis tool to classify social media reviews. Platforms like Kaggle provide datasets and competitions to help you practice and improve your skills.

Q. Why is it important to stay updated with AI developments?

AI is a rapidly evolving field. Staying updated through AI publications, research papers, and online communities will help you keep up with the latest innovations, tools, and applications, ensuring your skills remain relevant.

Q. What are some advanced areas of AI to specialise in?

Once you have a foundational understanding, you can specialise in areas such as:

  • Computer Vision: Used in facial recognition and autonomous vehicles.
  • Robotics: Focused on building intelligent robots for automation.
  • Generative AI: Involves AI models that generate new content, such as text or images.

Q. How can AI competitions help me improve my skills?

AI competitions, like those hosted on Kaggle, provide an opportunity to work on real-world datasets and solve complex problems. They help sharpen your problem-solving skills, improve your understanding of AI, and give you valuable experience that can be added to your portfolio.

Q. Is AI mastery a continuous process?

Yes, mastering AI is a continuous journey. The field is constantly evolving with new tools, models, and methodologies. Lifelong learning and curiosity about AI developments are essential to staying proficient in this rapidly changing field.