Introduction

Nowadays, learning about AI is the ask of the hour. With new AI technologies coming out regularly, everyone is buzzing about AI. As technology advances rapidly, a solid understanding of artificial intelligence and machine learning is crucial for staying competitive in the job market.

Picture this: a world where understanding artificial intelligence and machine learning is not just a skill but a necessity for career competitiveness. As technology progresses, it’s time to equip yourself with the knowledge that will set you apart in the job market.

Guess what? The tech powerhouse, Google, has your back! They’ve curated a collection of free AI courses that cater to everyone, whether you’re a coding novice or an experienced programmer looking to level up. These courses are like a virtual playground, offering diverse topics, from the basics of machine learning to the intricacies of natural language processing.

These courses cover various topics, from machine learning to natural language processing, and are designed to help individuals enhance their skills and knowledge in AI. If you are struggling to find one, this article is for you.

Read on to know more!

Benefits of Taking Google AI Courses

There are several potential benefits to taking Google AI courses, which can be broadly categorized into career-oriented and knowledge-based advantages:

Career Benefits

• Skill development: Free AI courses by Google can equip you with the skills and knowledge necessary to enter the field of Artificial Intelligence (AI) or Machine Learning (ML). These skills are highly sought-after in today’s job market, opening doors to various career opportunities across healthcare, finance, and technology industries.
• Increased employability: A recognized credential from Google AI, even a free course completion certificate, can make you a more attractive candidate to potential employers. It demonstrates your interest and initiative in learning in-demand skills.
• Staying ahead of the curve: AI is a rapidly evolving field. Taking Google AI courses can help you stay updated on the latest advancements and trends, ensuring you possess relevant knowledge for the job market.

Knowledge-based Benefits

• Improved understanding of AI: Even if you are not aiming for a career in AI, taking Google AI courses can enhance your general understanding of how AI works and its impact on various aspects of our lives. This knowledge can be valuable in various personal and professional contexts.
• Critical thinking and problem-solving skills: Many AI courses involve hands-on learning and practical application of concepts. This can help you develop critical thinking and problem-solving skills, valuable assets in any field.
• Enhanced decision-making: By understanding AI’s capabilities and limitations, you can make more informed decisions in various personal and professional situations.

It’s important to note that the benefits you gain from Google AI courses will depend on your goals and chosen course. However, the potential benefits listed above highlight the value proposition of these courses for anyone interested in learning more about AI and its applications.

Also read: A Complete Python Tutorial to Learn Data Science from Scratch.

Course 1: Introduction to Large Language Models

This micro-learning course introduces Large Language Models (LLM), delving into their definitions, potential applications, and prompt tuning techniques to optimize LLM performance. It also guides you using Google tools to create your own Generation AI applications.

You’ll be eligible to receive the displayed badge upon completing the course. Keep track of all your earned badges on your profile page. Showcase the skills you’ve acquired and elevate your cloud career by demonstrating your expertise to the world!

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.

Course 2: Introduction to Generative AI

This microlearning course introduces Generative AI, offering insights into its definition, applications, and distinctions from conventional machine learning approaches. Additionally, it delves into using Google Tools to create your own Generative AI applications.

Upon completing this course, you’ll be eligible to receive the badge showcased here! Keep track of all your earned badges on your profile page. Elevate your cloud career by showcasing the skills you’ve cultivated!

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.

Course 3: Introduction to Responsible AI

This microlearning course serves as an entry-level guide designed to clarify the concept of responsible AI, its significance, and Google’s approach to implementing responsible AI in its products. The course also provides an overview of Google’s 7 AI principles.

Upon completing this course, you’ll be awarded the displayed badge. Track and showcase all your earned badges on your profile page. Enhance your standing in the cloud industry by showcasing the skills you’ve acquired!

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.

Course 4: Generative AI Fundamentals

Achieve a skill badge by successfully finishing the courses Introduction to Generative AI, Introduction to Large Language Models, and Introduction to Responsible AI. Upon passing the final quiz, you’ll showcase your grasp of fundamental concepts in generative AI.

A skill badge issued by Google Cloud acknowledges your proficiency in their products and services. Make your profile public and add the badge to your social media to share your accomplishments.

Upon course completion, you’ll receive the displayed badge. Track all your earned badges on your profile page. Enhance your cloud career by highlighting the skills you’ve honed to the world!

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.

Also, the GenAI Pinnacle Program, a groundbreaking initiative is reshaping the landscape of AI education. This avant-garde course sets the stage for unparalleled learning and development in artificial intelligence, ensuring you stay at the forefront of this dynamic field. Explore this today!

Course 5: Introduction to Generative AI Studio

This course will familiarize you with Generative AI Studio, a tool on Vertex AI designed to assist you in prototyping and customizing generative AI models for integration into your applications. Throughout the course, you will gain an understanding of what Generative AI Studio entails, explore its features and functionalities, and learn how to navigate through the product via hands-on demonstrations. A quiz will be provided after the course to assess your comprehension.

You will be awarded a badge upon successful completion, which you can showcase on your profile page alongside other earned badges. Elevate your cloud career by highlighting the skills you’ve honed through this course!

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.

Course 6: Create Image Captioning Models

In this course, you will gain the skills to construct an image captioning model through deep learning techniques. You’ll explore key elements like the encoder and decoder and learn how to train and assess your model effectively. By the course’s conclusion, you can craft your own image captioning models to generate descriptive captions for various images.

You’ll be eligible to earn the displayed badge upon completing the course. Keep track of all your earned badges on your profile page, showcasing the valuable skills you’ve acquired. Elevate 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.

Course 7: Transformer Models and BERT Model

This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from the Transformers (BERT) model. You’ll gain insights into the key elements of the Transformer architecture, including the self-attention mechanism, and understand how it forms the basis of the BERT model. Additionally, you’ll explore various applications of BERT, such as text classification, question answering, and natural language inference.

Completing this course is expected to take around 45 minutes. Once finished, you’ll be eligible to earn the displayed badge. Keep track of all your earned badges on your profile page. Showcase your developed skills to the world and enhance your cloud career!

Objective of Course

1. Gain insight into the key elements of the Transformer architecture.
2. Acquire knowledge on constructing a BERT model with Transformers.
3. Apply BERT to address various natural language processing (NLP) challenges.

Course 8: Attention Mechanism

In this course, you’ll explore the attention mechanism—an influential technique empowering neural networks to concentrate on particular segments of an input sequence. Gain insights into how attention operates and discover its applications in enhancing the efficiency of diverse machine learning tasks such as machine translation, text summarization, and question answering.

Expect to invest around 45 minutes in completing this course.

Upon successful completion, you’ll be eligible for the displayed badge. Highlight the badges you’ve earned on your profile page to showcase your acquired skills and boost your cloud career easily. Let the world recognize the expertise you’ve cultivated!

Objective of Course

1. Gain a comprehensive understanding of the concept of attention and its functioning.
2. Acquire knowledge on applying attention mechanisms in the context of machine translation.

Moreover, in this journey of continuous learning, the Certified AI & ML BlackBelt PlusProgram emerges as a beacon, offering a tailor-made approach to mastering the intricacies of Artificial Intelligence and Machine Learning.

Imagine a learning experience where every lesson is meticulously designed to align with your career goals. The PlusProgram not only provides an in-depth understanding of AI and ML but also caters to your unique aspirations, ensuring a curriculum that propels you towards success.

Course 9: Encoder-Decoder Architecture

This course provides an overview of the encoder-decoder architecture, a robust machine learning framework widely used for sequence-to-sequence tasks like machine translation, text summarization, and question answering. You will gain insights into the key elements of the encoder-decoder setup and the skills to train and deploy these models. In the practical lab session, you’ll use TensorFlow to create a basic implementation of the encoder-decoder architecture for generating poetry from scratch.

Upon completing this course, you can earn the displayed badge. Track all your earned badges on your profile page and enhance your cloud career by showcasing your newly acquired skills to the world!

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.

Course 10: Introduction to Image Generation

This course will familiarize you with diffusion models, a group of machine-learning models that have recently shown promise in image generation. Principles from physics, particularly thermodynamics, inspire these models. Over the past few years, diffusion models have gained popularity in research and industry, serving as the foundation for many cutting-edge image generation models and tools available on Google Cloud. The course covers the theoretical aspects of diffusion models and practical training and deployment techniques on Vertex AI.

Upon completing this course, you’ll have the opportunity to earn a badge, which will be showcased on your profile page. Displaying the badges you’ve earned is a great way to boost your cloud career, highlighting the valuable skills you’ve acquired throughout the course.

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.

Also read: Top 7 Generative AI Courses to Do in 2024

Course 11: Responsible AI: Applying AI Principles with Google Cloud

As the adoption of enterprise Artificial Intelligence (AI) and Machine Learning (ML) grows, implementing them responsibly becomes increasingly important. However, translating the concept of responsible AI into practical application poses a challenge for many. If you’re eager to understand how to integrate responsible AI practices within your organization effectively, this course is designed for you.

Throughout this course, you will gain insights into Google Cloud’s current practices, valuable best practices, and lessons learned. These will serve as a comprehensive framework for establishing your responsible AI approach.

Upon completing this course, you can earn the displayed badge, showcasing your achievement. Keep track of all your earned badges on your profile page and enhance your cloud career by demonstrating the skills you’ve acquired to the world!

Objective of Course

1. Elaborate on the rationale behind responsible AI in a business context
2. Recognize ethical concerns related to AI by employing effective issue-spotting techniques
3. Illustrate the implementation of Google’s AI Principles and utilize insights gained from their experiences
4. Apply a framework for integrating responsible AI practices within your organization
5. Explore subsequent actions to advance your commitment to responsible AI implementation

Miscellaneous Learning Options by Google

Explore technical training options if you’re an application developer, database engineer, integration engineer, or security engineer:

1. The Arcade: Gain hands-on knowledge in the Google Cloud environment through generative AI labs in this gamified experience. Earn points for Google Cloud swag.
   
   Link: Arcade by Google.
   
2. Gen AI Bootcamp: Access on-demand workshops covering foundational to advanced topics. Beginners can follow step-by-step, while experienced developers can dive into advanced sessions.
   
   Link: Gen AI Bootcamp.
   
3. What is Codey? Learn in 60 seconds: Discover how Codey helps you bring your coding ideas to life in just one minute, making coding accessible even for beginners.
   
   Link: Codey.

Conclusion

In conclusion, free AI courses by Google offer a valuable opportunity for individuals to enhance their skills and knowledge in artificial intelligence. Whether you’re a beginner looking to learn the basics or an experienced coder seeking to expand your expertise, these courses provide a wealth of valuable information that can help you succeed in the ever-evolving world of AI. Take advantage of these free resources and start your AI education journey today!