10 Free AI Courses by Google

Introduction

Learning about AI is crucial in today’s world. With new AI technologies emerging regularly, it’s a hot topic everywhere. As technology evolves quickly, having a good grasp of artificial intelligence and machine learning is essential for staying competitive in your career.

Imagine a future where understanding AI and machine learning isn’t just useful but necessary for job success. With technology advancing rapidly, it’s time to learn the skills that will make you stand out in the job market.

Good news! Google is here to help. They offer a range of free AI courses suitable for everyone, whether you’re new to coding or an experienced programmer looking to improve. These courses cover various topics, from the basics of machine learning to advanced natural language processing.

These courses are designed to help you enhance your AI skills and knowledge. If you’re struggling to find the right course, this article is for you. Keep reading to learn more!

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Benefits of Taking Google AI Courses

Google AI courses offer many advantages, divided into two main categories: career benefits and knowledge gains.

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Career Benefits

• Skill development: Google’s free AI courses can prepare you with the skills and knowledge needed to start a career in Artificial Intelligence (AI) or Machine Learning (ML). These skills are in high demand in today’s job market, offering numerous career opportunities in the healthcare, finance, and technology sectors.

• Increased employability: Getting a certificate from Google AI, even for completing a free course, can make you more appealing to employers. It shows that you’re eager to learn valuable skills in demand.

• Staying ahead of the curve: AI is changing fast. You can keep up with the newest advancements and trends by taking Google AI courses, ensuring you have the right knowledge for the job market.

Knowledge-based Benefits

• Improved understanding of AI: Even if AI isn’t your career goal, doing Google AI courses can improve your understanding of how AI functions and affects different parts of our lives. This knowledge can be useful in many personal and professional situations.

• Critical thinking and problem-solving skills: Many AI courses include hands-on practice and real-world use of ideas. This can help you build important skills like critical thinking and problem-solving, which are useful in any job.

• Enhanced decision-making: Knowing what AI can and can’t do helps you make smarter choices in different parts of your life and work.

Remember, the advantages you get from Google AI courses will vary based on what you want to achieve and your chosen course. However, the benefits mentioned earlier show why these courses are valuable for anyone keen on understanding AI and how it’s used.

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Course 1: Introduction to Large Language Models

This short course teaches about large language models (LLM), explaining what they are, how they can be used, and techniques for improving their performance. It also shows you how to use Google tools to make your own Generation AI applications.

Once you finish the course, you’ll get a badge you can show off on your profile page. Displaying your badges can help you show off the skills you’ve learned and boost your career in cloud computing by proving your expertise to others!

Objective of Course

1. Define what Large Language Models (LLMs) are.

2. Provide an overview of Use Cases for Large Language Models (LLMs).

3. Elaborate on the concept of Prompt Tuning.

4. Explain the development tools in Google’s Gen AI toolkit.

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Course 2: Introduction to Generative AI

This short course teaches about Generative AI, explaining what it is, how it’s used, and how it’s different from traditional machine learning methods. It also shows you how to use Google Tools to make your own Generative AI applications.

After finishing the course, you’ll get a badge you can display on your profile page. Collecting badges can help you show off the skills you’ve learned and advance your career in cloud computing by demonstrating your expertise!

Objective of Course

1. Define Generative AI.

2. Elaborate on the functioning of Generative AI.

3. Outline various types of Generative AI models.

4. Discuss the applications of Generative AI.

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Course 3: Introduction to Responsible AI

This short course is a beginner’s guide to understanding responsible AI, its importance, and how Google uses it in its products. It also explains Google’s 7 AI principles.

After finishing the course, you’ll receive a badge. You can display all your badges on your profile page. Showing off your skills can help you stand out in the cloud industry and demonstrate what you’ve learned!

Objective of Course

1. Gain insight into the rationale behind Google’s implementation of AI principles.

2. Identify the imperative for fostering a responsible AI approach within organizational practices.

3. Acknowledge the consequential impact of decisions made throughout all project phases on responsible AI.

4. Recognize the flexibility for organizations to tailor AI designs to align with their unique business requirements and values.

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Course 4: Generative AI Fundamentals

Earn a skill badge by completing the courses Introduction to Generative AI, Introduction to Large Language Models, and Introduction to Responsible AI. Passing the final quiz shows you understand basic concepts in generative AI.

Google Cloud issues skill badges to recognize your expertise in their products. To celebrate your achievements, publicize your profile and share the badge on social media.

After finishing the courses, you’ll get the badge. Keep track of all your badges on your profile page. Showcasing your skills can boost your career in the cloud industry!

Objective of Course

1. Successfully finish the course named “Introduction to Generative AI.”

2. Complete the course “Introduction to Large Language Models (LLM).”

3. Successfully finish the course named “Introduction to Responsible AI.”

4. Achieve a passing score on the “Generative AI Fundamentals” quiz to earn the skill badge.

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Course 5: Introduction to Generative AI Studio

This course introduces you to Generative AI Studio on Vertex AI, helping you prototype and customize generative AI models for your applications. You will learn about Generative AI Studio, explore its features, and navigate the product through hands-on demonstrations. A quiz at the end will test your understanding.

After completing the course, you’ll earn a badge to display on your profile page with your other badges. Highlighting these skills can boost your cloud career!

Objective of Course

1. Provide an overview of the functionalities of Generative AI Studio.

2. Detail the available options within Generative AI Studio.

3. Investigate the language tool features offered by Generative AI Studio.

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Course 6: Create Image Captioning Models

This course teaches you to build an image captioning model using deep learning techniques. You’ll explore key components like the encoder and decoder and learn how to effectively train and evaluate your model. By the end of the course, you’ll be able to create your image captioning models to generate descriptive captions for various images.

Upon completing the course, you’ll earn a badge on your profile page, showcasing the valuable skills you’ve acquired. Boost your cloud computing career by demonstrating your expertise to the world!

Objective of Course

1. Gain insight into the various elements comprising an image captioning model.

2. Acquire the skills to train and assess the performance of an image captioning model.

3. Develop personalized image captioning models.

4. Apply your custom image captioning models to produce captions for images.

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Course 7: Transformer Models and BERT Model

This course introduces the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You will learn about the key elements of the Transformer architecture, including the self-attention mechanism and how it underpins the BERT model. You will also explore various BERT applications, such as text classification, question answering, and natural language inference.

The course takes about 45 minutes to complete. After finishing, you’ll earn a badge on your profile page. Show off your skills to the world and boost your cloud career!

Objective of Course

1. Learn about the main components of the Transformer architecture.

2. Acquire knowledge on constructing a BERT model with Transformers.

3. Apply BERT to address various challenges of natural language processing (NLP).

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Course 8: Attention Mechanism

This course teaches you about the attention mechanism, a powerful technique that helps neural networks focus on specific parts of an input sequence. Understand how attention works and its applications in improving tasks like machine translation, text summarization, and question answering.

The course takes about 45 minutes to complete.

After finishing, you’ll earn a badge on your profile page. Showcasing your badges highlights your skills and can easily boost your cloud career. Let the world see the expertise you’ve developed!

Objective of Course

1. Gain a comprehensive understanding of the concept of attention and its functioning.

2. Acquire knowledge on applying attention mechanisms in machine translation.

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Course 9: Encoder-Decoder Architecture

This course covers the encoder-decoder architecture, a powerful machine learning framework for tasks like machine translation, text summarization, and question answering. You will learn about the key elements of the encoder-decoder setup and how to train and deploy these models. In a practical lab session, you’ll use TensorFlow to create a basic encoder-decoder model for generating poetry from scratch.

After completing the course, you’ll earn a badge to display on your profile page. Showcasing your badges highlights your new skills and can boost your cloud career!

Objective of Course

1. Comprehend the fundamental elements of the encoder-decoder architecture.

2. Acquire the skills to train and produce text from a model employing the encoder-decoder architecture.

3. Develop the ability to create your encoder-decoder model using Keras.

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Course 10: Introduction to Image Generation

This course introduces diffusion models, a type of machine learning model inspired by physics principles like thermodynamics and known for their success in image generation. Recently, these models have become popular in both research and industry, forming the basis for many advanced image-generation tools on Google Cloud. The course covers both the theory behind diffusion models and practical training and deployment techniques on Vertex AI.

After completing the course, you’ll earn a badge to display on your profile page. Showcasing your badges is an excellent way to highlight the valuable skills you’ve gained and boost your cloud career.

Objective of Course

1. Understanding the mechanics of diffusion models

2. Exploring practical applications of diffusion models

3. Examining diffusion models without constraints

4. Investigating progress in diffusion models, particularly in the context of text-to-image conversion.

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Conclusion

Google’s free AI courses offer a fantastic opportunity to learn and advance your artificial intelligence and machine learning skills. These courses cover a wide range of topics, from the basics to advanced applications, providing valuable knowledge that can help you stay competitive in today’s job market. By completing these courses, you can earn certificates and badges demonstrating your expertise to potential employers, enhancing your career prospects in various industries.

Take advantage of these resources to expand your understanding of AI and ML and showcase your achievements on your profile page. Start your learning journey today and stay ahead in the rapidly evolving field of artificial intelligence.

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FAQs

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