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Top 6 Google Free AI Online Courses to Master Artificial Enroll Now in 2024

April 26, 2024

This article explores the top 6 Google Free AI Online courses that will equip you with the knowledge and skills to navigate the exciting world of AI, regardless of your background. Whether you’re a complete beginner or have some existing knowledge, there’s a perfect course for you!

About the Google

Google is an American multinational corporation and technology company focusing on online advertising, search engine technology, cloud computing, computer software, quantum computing, e-commerce, consumer electronics, and artificial intelligence (AI). It has been referred to as “the most powerful company in the world” and is one of the world’s most valuable brands due to its market dominance, data collection, and technological advantages in the field of AI. Google’s parent company, Alphabet Inc. is one of the five Big Tech companies, alongside Amazon, Apple, Meta, and Microsoft.

Google was founded on September 4, 1998, by American computer scientists Larry Page and Sergey Brin while they were PhD students at Stanford University in California. Together, they own about 14% of its publicly listed shares and control 56% of its stockholder voting power through super-voting stock. The company went public via an initial public offering (IPO) in 2004. In 2015, Google was reorganized as a wholly owned subsidiary of Alphabet Inc. Google is Alphabet’s largest subsidiary and is a holding company for Alphabet’s internet properties and interests. Sundar Pichai was appointed CEO of Google on October 24, 2015, replacing Larry Page, who became the CEO of Alphabet. On December 3, 2019, Pichai also became the CEO of Alphabet.

Eligibility Criteria

Any College Student with any stream.

Here are the Top 6 Google Free AI Online Courses

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.

When you complete this course, you can earn the badge displayed here! View all the badges you have earned by visiting your profile page. Boost your cloud career by showing the world the skills you have developed!

2. Introduction to Responsible AI

This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it’s important, and how Google implements responsible AI in their products. It also introduces Google‘s 7 AI principles.

3. Transformer Models and BERT Model

This course introduces you to 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.

4. Introduction to Large Language Models

This is an introductory-level micro-learning course that 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.

5. Encoder-Decoder Architecture

This course gives you a synopsis of the encoder-decoder architecture, which is 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.

6. 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 be used to improve the performance of a variety of machine-learning tasks, including machine translation, text summarization, and question answering.

This course is estimated to take approximately 45 minutes to complete.