We come to our new blog ‘Free AI Courses by Google: Learn Generative AI with Google’. The field of Artificial Intelligence (AI) has seen rapid development over the past five years, with Generative AI (GAI) at the forefront of this evolution. In fact, the Generative AI market is projected to reach $36 billion by 2028, a significant increase from $3.7 billion in 2023.
Generative AI has had a profound impact on various industries, including healthcare, marketing, fashion, and entertainment. AI generators such as AI image generators and AI video generators have demonstrated the potential to replace manual human tasks. However, mastering this field requires a specialized AI skillset.
To facilitate learning for AI enthusiasts, Google has introduced 10 free courses on Generative AI. Before delving into the details of these courses, let’s briefly understand what Generative AI entails.
Table of Content
What is Generative AI and Why is it Important to Learn?
Generative AI is a specialized domain of AI that focuses on building models capable of generating new and realistic content, such as images, text, audio, or videos, using existing data samples.
Prominent examples of Generative AI models include ChatGPT and DALL-E, which have found real-world applications. ChatGPT is integrated into Bing’s search engine, while the Edge browser incorporates DALL-E.
Staying up-to-date with Generative AI technology is crucial for several reasons:
- Enhances business productivity, cost-effectiveness, and efficiency.
- Encourages experimentation and creativity.
- Facilitates human-AI collaboration and augments human capabilities.
- Enables innovative problem-solving strategies.
Now, let’s explore how Google is aiding learners in studying Generative AI through their 10-course learning path.
10 Course Learning Path for Generative AI: Free AI Courses by Google
Introduction to Generative AI
Course difficulty: Beginner-level
Completion time: Approximately 45 minutes
Prerequisites: None
Website: https://www.cloudskillsboost.google/course_templates/536
Key takeaways:
- This is the courses on Generative AI Offered by Google offers Understanding of Generative Artificial Intelligence, its functioning, applications, and how it differs from standard machine learning techniques.
- Introduction to Google tools for creating personalized Generative AI applications.
- Overview of Generative AI model types: unimodal and multimodal. Unimodal systems accept only one input type, while multimodal systems can process multiple input types.
Introduction to Large Language Models
Course difficulty: Beginner-level
Completion time: Approximately 45 minutes
Prerequisites: None
Website: https://www.cloudskillsboost.google/course_templates/539
Key takeaways:
- Exploration of Large Language Models (LLMs), which are AI models trained on extensive textual data.
- Insight into the use of LLMs for sentiment analysis.
- Understanding prompt tuning, a process that refines the prompts given to a language model to achieve desired outputs.
- Overview of Google’s development tools for Generative AI.
Introduction to Responsible AI
Course difficulty: Beginner-level
Completion time: Approximately 1 day (Quiz/lab completion at your own pace)
Prerequisites: None
Website: https://www.cloudskillsboost.google/course_templates/554
Key takeaways:
- This Free AI Courses by Google offers Introduction to Responsible Artificial Intelligence, its importance, and how Google implements this technology in their products.
- Overview of Google’s 7 Responsible AI principles.
Generative AI Fundamentals
Course difficulty: Beginner-level
Completion time: Approximately 1 day (Quiz/lab completion at your own pace)
Prerequisites: None
Website: https://www.cloudskillsboost.google/course_templates/556
Key takeaways:
- This Free AI Courses by Google offers Consolidation of content covered in the previous three courses.
- Final quiz to assess understanding of fundamental Generative AI concepts.
Introduction to Image Generation
Course difficulty: Beginner-level
Completion time: Approximately 1 day (Quiz/lab completion at your own pace)
Prerequisites: Knowledge of machine learning (ML), deep learning (DL), convolutional neural networks (CNNs), and Python programming.
Website: https://www.cloudskillsboost.google/course_templates/541
Key takeaways:
- Exploration of diffusion models, their functioning, and implementation.
- Understanding of unconditioned diffusion models.
- Advancements in text-to-image diffusion models.
- Training and deployment of these models using Vertex AI, Google’s fully managed ML platform.
Encoder-Decoder Architecture
Course difficulty: Intermediate-level
Completion time: Approximately 1 day (Quiz/lab completion at your own pace)
Prerequisites: Knowledge of Python programming and TensorFlow.
Website: https://www.cloudskillsboost.google/course_templates/543
Key takeaways:
- Comprehensive understanding of the key components of the encoder-decoder architecture.
- Application of the encoder-decoder architecture for training models and generating text.
- Hands-on lab where you will code in TensorFlow, a popular ML development platform, to build production-grade models.
Attention Mechanism
Course difficulty: Intermediate-level
Completion time: Approximately 45 minutes
Prerequisites: Knowledge of ML, DL, Natural Language Processing (NLP), Computer Vision (CV), and Python programming.
Website: https://www.cloudskillsboost.google/course_templates/537
Key takeaways:
- Introduction to the concept of attention mechanism, a powerful approach enabling language models to focus on specific input sequence segments for better contextual understanding.
- Insight into the functioning and applications of the attention mechanism in ML models.
Transformer Models & BERT Models
Course difficulty: Beginner-level
Completion time: Approximately 45 minutes
Prerequisites: Intermediate knowledge of ML, understanding of word embeddings and attention mechanism, and experience with Python and TensorFlow.
Website: https://www.cloudskillsboost.google/course_templates/538
Key takeaways:
- Learning about the Transformer architecture and the construction of the Bidirectional Encoder Representation from Transformers (BERT) model.
- Exploration of different Natural Language Processing (NLP) tasks for which BERT models are utilized.
Create Image Captioning Models
Course difficulty: Intermediate-level
Completion time: Approximately 1 day (Quiz/lab completion at your own pace)
Prerequisites: Knowledge of ML, DL, NLP, CV, and Python programming.
Website: https://www.cloudskillsboost.google/course_templates/542
Key takeaways:
- Identification of the components of an image captioning model.
- Building and evaluating a model for image captioning.
- Creation of custom captioning models for photos and generating captions using them.
Introduction to Generative AI Studio
Course difficulty: Introductory-level
Completion time: Approximately 1 day (Quiz/lab completion at your own pace)
Prerequisites: None
Website: https://www.cloudskillsboost.google/course_templates/552
Key takeaways:
- This Courses on Generative AI Offered by Google offers Understanding the purpose of Generative AI Studio, a product within Google’s Vertex AI.
- Coverage of the options and properties of Generative AI Studio.
- Hands-on lab where you can leverage this tool.
Upon completion of these ten free courses, learners will gain a comprehensive understanding of Generative AI and its practical applications. Armed with this knowledge, learners can contribute to the advancement of Generative AI and develop innovative products that have a positive impact on society.
Comparison of Courses on Generative AI Offered by Google:
Here’s a table highlighting the key points and a comparison of the above courses on Generative AI offered by Google:
| Course | Difficulty Level | Completion Time | Prerequisites | Key Takeaways |
|---|---|---|---|---|
| Introduction to Generative AI | Beginner | ~45 minutes | None | – Understanding of Generative AI and its applications.<br>- Introduction to Google tools for creating Generative AI apps.<br>- Differentiating between unimodal and multimodal Generative AI model types. |
| Introduction to Large Language Models | Beginner | ~45 minutes | None | – Exploring Large Language Models (LLMs) and their applications.<br>- Understanding prompt tuning and its role in refining language models.<br>- Overview of Google’s development tools for Generative AI. |
| Introduction to Responsible AI | Beginner | ~1 day | None | – Introduction to Responsible AI and its importance.<br>- Familiarity with Google’s 7 Responsible AI principles. |
| Generative AI Fundamentals | Beginner | ~1 day | None | – Consolidation of content covered in previous courses.<br>- Assessment of fundamental concepts in Generative AI through a final quiz. |
| Introduction to Image Generation | Beginner | ~1 day | Knowledge of ML, DL, CNNs, Python programming | – Understanding diffusion models and their implementation.<br>- Exploring unconditioned diffusion models.<br>- Training and deploying diffusion models on Google’s Vertex AI platform. |
| Encoder-Decoder Architecture | Intermediate | ~1 day | Knowledge of Python programming, TensorFlow | – Understanding key components of encoder-decoder architecture.<br>- Application of encoder-decoder architecture for training and generating text.<br>- Practical experience coding in TensorFlow to build production-grade models. |
| Attention Mechanism | Intermediate | ~45 minutes | Knowledge of ML, DL, NLP, CV, Python programming | – Introduction to attention mechanism and its role in understanding contextual information.<br>- Applications and functioning of attention mechanism in ML models. |
| Transformer Models & BERT Models | Beginner | ~45 minutes | Intermediate knowledge of ML, word embeddings, attention mechanism, experience with Python and TensorFlow | – Understanding the Transformer architecture and building BERT models using Transformers.<br>- Exploring various NLP tasks that utilize BERT models. |
| Create Image Captioning Models | Intermediate | ~1 day | Knowledge of ML, DL, NLP, CV, Python programming | – Identifying elements of an image captioning model.<br>- Building and evaluating image captioning models.<br>- Creating custom captioning models for photos. |
| Introduction to Generative AI Studio | Introductory | ~1 day | None | – Familiarity with Generative AI Studio, a Vertex AI product by Google.<br>- Understanding options and properties of Generative AI Studio.<br>- Practical utilization of Generative AI Studio through a hands-on lab. |
Please note that the information in the table provides a summary of key points and a general comparison. It is advisable to review the individual course descriptions and requirements for more detailed information.
Conclusion: Free AI Courses by Google
In conclusion, the emergence of Generative AI (GAI) has revolutionized the Artificial Intelligence landscape, presenting immense opportunities for innovation and automation across various industries. With the Generative AI market projected to reach $36 billion by 2028, it is evident that this field holds significant potential for growth and impact.
Google’s initiative to offer 10 free courses on Generative AI is a commendable effort to democratize access to this specialized knowledge. These courses cover a wide range of topics, from the fundamentals of Generative AI to advanced techniques such as transformer models and image captioning. By completing these courses, learners can acquire a comprehensive understanding of Generative AI and its practical applications.
The implications of mastering Generative AI are far-reaching. It empowers businesses to enhance productivity, cost-effectiveness, and efficiency, while fostering creativity and enabling novel problem-solving strategies. Furthermore, Generative AI facilitates human-AI collaboration, augmenting human capabilities and opening doors to new possibilities.
By equipping themselves with Generative AI skills, learners can position themselves at the forefront of technological advancements, contributing to the development of innovative products and solutions that positively impact society.
In conclusion, Google’s commitment to empowering AI enthusiasts with knowledge in Generative AI through these courses demonstrates their dedication to fostering a vibrant and inclusive AI ecosystem. As the field of Generative AI continues to evolve, continuous learning and exploration will be essential to stay at the forefront of this exciting domain.
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