The Age of Generative AI
Are you fascinated by the power of artificial intelligence to create unique and realistic content? Look no further! In this article, we present a curated list of the top generative AI courses that will ignite your creativity and expand your skills. Generative AI skills have become increasingly important in today’s rapidly evolving technological landscape. The potential for generative AI to revolutionize many different businesses and creative fields is enormous as artificial intelligence technology develops. Design, entertainment, and marketing advances may result from the ability to create fresh, realistic content, such as images, music, and text.
We will talk about some of the top generative AI courses in this article that you may take online to increase your technical knowledge. Whether you’re an engineer, a professional coder or just someone who is curious about the potential of artificial intelligence technology, these courses will offer you an exploration and thorough understanding of generative AI. From understanding the principles of generative models to creating breathtaking artwork and lifelike literature, these courses will equip you with the abilities and information required to fully realize the potential of generative AI.
Top Generative AI Courses
Here are some of the best generative AI courses available online that can take your technical skills to the next level.
ChatGPT Prompt Engineering for Developers
Offered by DeepLearning.AI in collaboration with OpenAI, this course reflects the latest understanding of best practices for using prompts for the latest LLM models. This course, ChatGPT Prompt Engineering for Developers, teaches students how to create new, robust apps quickly using a large language model (LLM). This short course of 1-hour duration is taught by Isa Fulford from OpenAI and Andrew Ng from DeepLearning.AI. The course will describe how LLM APIs can be used in applications for a variety of tasks, including summarizing, inferring, transforming, and expanding. The limited-time free version of ChatGPT Prompt Engineering for Developers is user-friendly for beginners. Only a fundamental knowledge of Python is required.
Generative Adversarial Networks (GANs) Specialization
The Generative Adversarial Networks (GANs) Specialization is offered by DeepLearning AI on Coursera. It offers a fascinating introduction to image generation using GANs, outlining a journey from basic concepts to complex techniques using a simple methodology. Additionally, it discusses social aspects such as privacy protection, bias in ML, and how to detect it. Students will be able to develop a thorough theoretical basis and acquire practical GAN experience. In addition, they will assess a number of advanced GANs and train their own model in PyTorch. This Specialization is suitable for levels of learners, even those without prior familiarity with advanced math and machine learning research.
ChatGPT for Beginners: The Ultimate Use Cases For Everyone
Through this Udemy course, students will learn how to use ChatGPT’s power to automate tasks, make money, and develop their skills. From novice to expert users, this course, ChatGPT for Beginners: The Ultimate Use Cases For Everyone, is created for people and companies of all skill levels. Students will discover how ChatGPT works and how to use it to boost output, cut down on wait times, and streamline processes throughout the course. Additionally, students will gain practical experience utilizing ChatGPT to create realistic content while learning how to set up and customize ChatGPT to suit their individual needs. Apart from an introduction to ChatGPT and its capabilities, this course also includes tips and best practices for effectively using ChatGPT. One can even get advice on how to integrate ChatGPT into their business or personal workflow.
The Fundamentals of ChatGPT
The experts at Digital Partner have developed The Fundamentals of ChatGPT course to help learners take advantage of this important new technology, ChatGPT, as it begins to change the world. The course defines the role of OpenAI in promoting AI technology globally and explains how ChatGPT works step by step, along with highlighting some of the major shortcomings of chatbots. The course presents various case studies and examples of developers interacting with ChatGPT as they test its capabilities, which include writing, mathematics, coding, and more. The course also compares the standard ChatGPT and ChatGPT Plus, which charges a monthly subscription fee to use. Instructors of the course will provide strategies that they can use to develop and customize their own GPT platform.
Building Systems with the ChatGPT API
This one-hour course, taught by Isa Fulford of OpenAI and Andrew Ng of DeepLearning.AI, builds on the lessons taught in the popular ChatGPT Prompt Engineering for Developers, though it is not a prerequisite. In Building Systems With The ChatGPT API, one can learn how to automate complex workflows using chain calls to a large language model. Learners will build chains of prompts that interact with the completions of prior prompts as well as systems where Python code interacts with both completions and new prompts. They will also create a customer service chatbot using all the techniques from this course.
Most importantly, they will learn how to apply these skills to practical scenarios, including classifying user queries to a chat agent’s response, evaluating user queries for safety, and processing tasks for chain-of-thought, multi-step reasoning.
LangChain for LLM Application Development
LangChain for LLM Application Development, a one-hour course instructed by the creator of LangChain, Harrison Chase, as well as Andrew Ng, will vastly expand the possibilities for leveraging powerful language models, where students can now create incredibly robust applications in a matter of hours. In LangChain for LLM Application Development, learners will gain essential skills in expanding the use cases and capabilities of language models in application development using the LangChain framework. At the end of the course, they will have a model that can serve as a starting point for their own exploration of diffusion models for applications. Users will learn about calling LLMs, providing prompts, parsing the response, creating sequences of operations, and applying LLMs to their proprietary data and use case requirements.
How Diffusion Models Work
How Diffusion Models Work, a one-hour course by DeepLearning.AI and taught by Sharon Zhou, will expand one’s generative AI capabilities to include building, training, and optimizing diffusion models. In this course, users will gain a deep familiarity with the diffusion process and the models which carry it out. In this course, learners will explore the cutting-edge world of diffusion-based generative AI and create their own diffusion model from scratch. They will gain deep familiarity with the diffusion process and the models driving it, going beyond pre-built models and APIs. This course will help one acquire practical coding skills by working through labs on sampling, training diffusion models, building neural networks for noise prediction, and adding context for personalized image generation.
Introduction to Generative AI
Introduction to Generative AI is a beginner-level microlearning course provided by Google that seeks to define and explain what Generative AI is, which is, in short, a type of artificial intelligence technology that can create many types of material, including text, imagery, audio, and synthetic data. The course will examine the technology’s applications and how they differ from conventional machine learning techniques. It also covers Google Tools to help students develop their own Generative AI apps. The course will explain generative AI model types and their applications of the same. This course is estimated to take approximately 45 minutes to complete. Users can earn a badge when they complete this course.
Introduction to Large Language Models
Offered by Google, Introduction to Large Language Models is an introductory-level microlearning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how one can use prompt tuning to enhance LLM performance. Large Language Models (LLMs) are foundational machine learning models that make use of deep learning algorithms to process and understand natural language. Learners will learn how these models are trained on vast amounts of text data to learn patterns and entity relationships in the language. The course also covers Google tools to help users develop their own generative AI apps. This course is also estimated to take approximately 45 minutes to complete.
Introduction to Responsible AI
Introduction to Responsible AI is an introductory-level microlearning course aimed at explaining what responsible AI is, why it’s important, and how Google implements responsible AI in its products. Responsible AI is the practice of designing, developing, and deploying AI with the purpose of empowering employees and organizations and having an equitable influence on consumers and society. This enables businesses to build trust and confidently scale AI. The course also introduces Google’s 7 AI principles. These principles are: Be socially beneficial, Be built and tested for safety, Avoid creating or reinforcing unfair bias, Be accountable to people, Uphold high standards of scientific excellence, Incorporate privacy design principles, and Be made available for uses that align with these principles.
Introduction to Image Generation
Offered by Google, this Introduction to Image Generation 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 both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces learners to the theory behind diffusion models and how to train and deploy them on Vertex AI.
Encoder-Decoder Architecture
Encoder-Decoder Architecture by Google Skills Boost gives learners a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as text summarization, machine translation, and question answering. Students will learn about the key components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, they will code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
Attention Mechanism
Attention Mechanism course will introduce learners to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. Attention Mechanism is an attempt to implement the action of selectively concentrating on fewer relevant things while ignoring the others in deep neural networks. One may understand how the attention mechanism functions and how it can be applied to enhance a number of machine learning tasks, such as question-answering text summarization, and machine translation. It should take you about 45 minutes to finish this course.
Transformer Models and BERT Model
Transformer Models and BERT Model, offered by Google, gives an introduction to the Transformer architecture and the Bidirectional Encoder Representations from the Transformers (BERT) model. A neural network called a transformer model follows relationships in sequential input, such as the words in this sentence, to learn context and subsequent meaning, whereas BERT is an open-source ML framework for natural language processing. The self-attention mechanism, for example, and how it is used to construct the BERT model will be thoroughly explained to students as important parts of the Transformer architecture. Additionally, they will become familiar with the various tasks that BERT is capable of performing, including text classification, natural language inference, and question answering. It should take you 45 minutes to complete this course, on average.
Create Image Captioning Models
This short course by Google, Create Image Captioning Models, teaches how to create an image captioning model by using deep learning. Imagine captioning is the process of developing a written summary of a picture. Both computer vision and natural language processing are used to generate the captions. Learners will understand the various parts of an image captioning model, such as the encoder and decoder, as well as how to train and test your model. They will be able to develop their own image captioning models by the conclusion of the course and use them to produce captions for photos.
Introduction to Generative AI Studio
As the name Introduction to Generative AI Studio suggests, this course by Google 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. Generative AI Studio is a Google Cloud console tool for rapidly prototyping and testing generative AI models. Learners will test sample prompts, design their own prompts, and customize foundation models to handle tasks that meet their application’s needs. In this course, they will also 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, learners will have a hands-on lab to apply what they learned and a quiz to test your knowledge.
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
DeepLearning.AI provides this course titled Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning. This course, which is a part of their upcoming Machine Learning in Tensorflow Specialisation, will cover TensorFlow, a well-known open-source machine learning framework. The most fundamental theories of deep learning and machine learning are covered in this specialization by Andrew Ng. It also shows you how to apply those principles using TensorFlow so that students may begin creating and using scalable models to solve real-world issues. To gain a deeper grasp of how neural networks operate, this course is recommended.
