Google, Microsoft, Nvidia, Linkedin and IBM Offer AI Courses for Free (2024)

The double-edged sword of AI (artificial intelligence) lies in its novelty. While early adopters have made significant strides, the entire ecosystem is in a formative stage, demanding a continuous learning curve for those seeking to catch up.

This represents a massive opportunity as well as a pitfall for those who are on the outside looking in. We like to monitor different courses in the space for our own education as well as for resources to share with others to include them as part of the discussion and learning. 

We've compiled this list as a set of reliable resources to do just that.

Google offers 10 AI courses for free

Google's new initiative involves a collection of free courses aimed at giving people the knowledge and skills needed to excel in the new world of generative AI.

Below is a list of the ten courses being offered:

  1. Introduction to Generative AI: This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps. This course is estimated to take approximately 45 minutes to complete:
  2. Introduction to Large Language Models: This introductory-level microlearning course explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps. This course is estimated to take approximately 45 minutes to complete.
  3. Introduction to Responsible AI: This is an introductory-level microlearning course to explain responsible AI, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 7 AI principles.
  4. Generative AI Fundamentals: Earn a skill badge by completing the Introduction to Generative AI, Introduction to Large Language Models and Introduction to Responsible AI courses. By passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI.
  5. Introduction to Image Generation: This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models have become popular in research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.
  6. Encoder-Decoder Architecture: This course gives you a synopsis of the encoder-decoder architecture, a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you'll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
  7. Attention Mechanism: This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works and how it can improve the performance of various machine-learning tasks, including machine translation, text summarization, and question-answering.
  8. Transformer Models and BERT Model: This course introduces the Transformer architecture and the Bidirectional Encoder Representations from the Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.
  9. Create Image Captioning Models: This course teaches you how to create an image captioning model using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your image captioning models and use them to generate captions for images.
  10. Introduction to Generative AI Studio: This course introduces Generative AI Studio, a product on Vertex AI that helps you prototype and customize generative AI models so you can use their capabilities in your applications. In this course, you learn what Generative AI Studio is, its features and options, and how to use it by walking through demos of the product. In the end, you will have a quiz to test your knowledge.
Google's commitment to nurturing the next generation of AI pioneers is commendable. Aspiring learners can access these courses without incurring any costs.

Upon completion, Google Cloud rewards you with a 'Completion Badge,' a testament to your newfound mastery of Generative AI. In a world driven by innovation, these courses offer a gateway to an exciting and fulfilling journey in the realm of Generative AI. So, embrace the opportunity, embark on this educational voyage, and become a part of the generative AI revolution.

You can sign up for these courses here.

Microsoft & Linkedin released 15 Professional Certificates in Generative Al

No prerequisites or fees required.

  1. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐀𝐥: An overview of AI tools for project managers, executives, and students starting their AI career:
  2. 𝐖𝐡𝐚𝐭 𝐈𝐬 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐥?: Learn about the basics, history, working principles, and ethical implications of Generative AI:
  3. 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐥: The Evolution of Thoughtful Online Search: Explore distinctions between search and reasoning engines, mastering thoughtful search strategies in Generative AI:
  4. 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐧𝐢𝐧𝐠 𝐘𝐨𝐮𝐫 𝐖𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐁𝐢𝐧𝐠 𝐂𝐡𝐚𝐭: Utilize Microsoft Bing Chat to automate and streamline tasks effectively:
  5. 𝐄𝐭𝐡𝐢𝐜𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐀𝐠𝐞 𝐨𝐟 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐥: Address ethical concerns in deploying Generative AI, understanding the ethical analysis framework:
  6. 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐀𝐳𝐮𝐫𝐞 𝐀𝐈 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬 Learn how to use Azure Machine Learning to create and publish models without writing code:
  7. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 This course is a great way to learn the basics and use cases of Machine Learning:
  8. 𝐀𝐈 𝐟𝐨𝐫 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 - By Microsoft:
  9. 𝐀𝐈 𝐟𝐨𝐫 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞:
  10. 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧:
  11. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬:
  12. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐥𝐞 𝐀𝐈:
  13. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐈𝐦𝐚𝐠𝐞 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧:
  14. 𝐂𝐫𝐞𝐚𝐭𝐞 𝐈𝐦𝐚𝐠𝐞 𝐂𝐚𝐩𝐭𝐢𝐨𝐧𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬:
  15. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐒𝐭𝐮𝐝𝐢𝐨:

IBM Free AI Courses

  1. Generative AI:
  2. IBM AI Engineering:
  3. IBM Applied AI Professional Certificate:
  4. Generative AI for Data Scientists Specialization:
  5. IBM AI Engineering Professional Certificate:
  6. IBM Machine Learning Professional Certificate:

Nvidia Free AI Courses

𝟭. Live Training to accelerate your career in AI -
𝟮. Generative AI Explained -
𝟯. Augmenting LLMs using RAG -
𝟰. Building Video AI Applications -
𝟱. Getting Started with AI on Jetson Nano -
𝟲. Digital Fingerprinting with Morpheus -
𝟳. An Even Easier Introduction to CUDE -
𝟴. Building RAG Agents with LLMs -
𝟵. Building A Brain in 10 Minutes -
𝟭𝟬. Assemble a Simple Robot in Isaac Sim -
𝟭𝟭. Accelerate Data Science Workflows -



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