As technology changes at the speed of light, tech teams come under great pressure to stay ahead of the curve by continuously learning and adapting to these developments. One such area is the generative AI. Generative AI, which includes technologies like GPT-4 and other advanced neural networks, has revolutionized how we create content, design systems, and solve complex problems. The technology can generate human-like text, create realistic images and videos, and even compose music. The real question arises: where to start? Lucky for engineering leadership, we have created the list of perfect courses in generative AI for the tech team.
Top generative AI courses
There are a lot of options available for L&D teams to choose from the vast library of courses offered at DevPath. Amidst the lot, here are the best-selling generative AI courses that L&D teams can incorporate into their tech team training for upskilling:
Generative AI: From Theory to Product Launch
Unleashing the Power of AI with OpenAI’s GPT-3
Modern Generative AI with ChatGPT and OpenAI Models
Using OpenAI API for Natural Language Processing in Python
Generative AI: From Theory to Product Launch
The course “Generative AI: From Theory to Product Launch” provides a comprehensive overview of GenAI and large language models (LLMs) such as DALL·E and GPT-2, along with how GenAI systems are built and operated. Spanning over 8 weeks, tech teams will learn about data preparation, model selection, training techniques, and evaluation methods, which aid them in creating high-quality, innovative products. The course offers an opportunity to learn about the underlying algorithms of generative AI, such as transformers and generative adversarial networks (GANs), experiment with different AI models, and then gain the confidence to integrate these solutions into existing workflows. For example, in software development, generative AI can assist in code generation, debugging, and optimization, thereby speeding up the development cycle and reducing errors. One significant application is in software development, where generative models can automate code generation, significantly speeding up the development cycle and reducing the likelihood of human error.
The course also touches upon transformer models and their variations. Originally designed for natural language processing (NLP), transformer models utilize self-attention mechanisms to understand and generate complex data with remarkable accuracy. Then, there are the vision transformers (ViT) that adapt the transformer model for image processing. So, instead of treating images as a whole, ViTs break them down into smaller patches, treating each patch as a token similar to words in a sentence. This approach allows the model to process and analyze visual data with precision in image classification and object detection. These models can generate descriptive captions for images, translate spoken language into written text, and even create rich, interactive AI systems that can respond to both visual and auditory inputs. Additionally, there is also some historical context and the progression of techniques of generative AI which allows tech teams to gain a perspective on the strengths and limitations of different approaches. When tech teams are proficient in what generative AI can achieve, they can use its capabilities for tasks such as streamlining operations, troubleshooting issues, and refining models for better performance.
Generative AI: From Theory to Product Launch
Generative AI (GenAI) is an exciting new frontier of technology that opens up seemingly endless creative possibilities. This course provides a glimpse of generative models’ capability by showcasing some of their most impressive applications. It will empower you to leverage GenAI and large language models (LLMs) like DALL·E and GPT-2. You’ll learn about the evolution of machine translation systems, from the early 1950s to the current state-of-the-art generative models. You’ll learn about the building blocks of Transformer networks, including CNNs, RNNs, etc. This will be supplemented by an overview of the components of a GenAI system. Next, you’ll learn about transformer models and their variations: Vision Transformers (ViT) and multimodal transformers. You’ll explore the state-of-the-art models for text, image, and video generation models through the practical exercises. You’ll dive deep into the impact of GenAI across fields and industries, fueling the development and launch of GenAI-based products.
Unleashing the Power of AI with OpenAI’s GPT-3
The course begins with the foundational knowledge of GPT-3, how it works, and its capabilities and potential. Tech teams will learn about diverse applications of GPT-3 and its use cases across industries such as healthcare, finance, and customer service. This section will enlighten tech teams on how GPT-3 can streamline processes, enhance productivity, and drive innovation within technical projects. As tech teams progress in the course, they will gain hands-on experience with the OpenAI API so that tech teams can leverage GPT-3 effectively in their projects. Through practical exercises, the course will guide them step-by-step on using the API with Python, Go, and Java, along with best practices for integrating GPT-3 into existing systems and workflows.
Moreover, as tech teams learn about the importance of prompt design and effective prompting techniques, they will be able to maximize the utility of GPT-3 in various scenarios. Tech teams will get to examine and analyze how leading companies like GitHub, Algolia, and Microsoft’s Azure use GPT-3 to drive success. Detailed case studies will provide insights into the applications of GPT-3 in these prominent companies, highlighting key takeaways and best practices from their experiences. This will allow tech teams a deeper understanding of GPT-3 strategies and practices to enhance project performance. Lastly, a crucial area that tech teams will touch upon is the ethical implications of using GPT-3. With AI taking over the tech industry, it is imperative for tech teams to understand the AI bias, countermeasures, and environmental impact. The course assesses the environmental footprint of large language models and discusses sustainable practices so tech teams are aware of the fair and responsible use of GPT-3.
Unleashing the Power of AI with OpenAI’s GPT-3
In this course, you’ll start a transformative learning journey exploring the applications of GPT-3 in AI and gaining hands-on experience with this powerful language model. You’ll start the course by understanding the basics of GPT-3 and its applications across domains and then learn to use the OpenAI API with Python, Go, and Java. Next, you’ll dive into prompting techniques with GPT-3, exploring essential topics to understand how to use GPT-3 for next-gen startups with real-world use cases and applications. You’ll explore its role in prominent companies like GitHub, Algolia, and Microsoft’s Azure. Lastly, you will navigate the ethical considerations of GPT-3, addressing issues like AI bias, anti-bias countermeasures, and the environmental impact of LLMs. After completing this course, you’ll gain a deep understanding of GPT-3. Whether you are an aspiring developer, entrepreneur, or professional transitioning to AI-focused roles, this course equips you with the skills to advance your career.
Modern Generative AI with ChatGPT and OpenAI Models
The course begins with an introductory module on generative AI, OpenAI, and ChatGPT. This module aims to familiarize tech teams with the basics of generative AI, explaining what it is and why it is significant in the tech industry. It also covers the background of OpenAI, its contributions to AI research, ChatGPT, and its wide range of applications. The detailed overview helps tech teams understand the potential and limitations of using AI in complex projects. Next up, the course explores how ChatGPT can enhance productivity in code generation by writing, debugging, and optimizing code. By understanding diverse use cases, tech teams can identify specific areas in their projects where ChatGPT can be integrated to boost efficiency and innovation. The course then emphasizes the significance of prompt engineering and explains its critical role in working with AI models. It provides real-world examples of effective, prompt design to illustrate best practices and discusses ethical considerations, stressing the importance of responsible AI usage. This knowledge will enable tech teams to create precise and effective prompts, ensuring they can harness the full potential of ChatGPT in their projects. The final module discusses strategies to identify and reduce bias in AI outputs.
Furthermore, it provides a step-by-step guide on integrating OpenAI’s model APIs into existing workflows. This comprehensive technical guidance ensures that tech teams can implement and manage AI models effectively, guaranteeing smooth integration and optimal performance in their projects. The course also suggests further learning resources for continued education and provides information on joining OpenAI’s community and accessing support.
Modern Generative AI with ChatGPT and OpenAI Models
OpenAI aims to leverage artificial intelligence (AI) for the benefit of humanity. With the launch of ChatGPT, it has introduced a large-scale application of generative AI to create new content using machine learning algorithms. OpenAI offers a set of pretrained, ready-to-use models that can be consumed by the general public. This course starts with a basic introduction to generative AI, OpenAI, and ChatGPT, along with their potential use cases. It pitches the significance of prompt engineering with some practical and ethical considerations. You’ll explore various application domains where ChatGPT can enhance productivity, including marketing, research, and code generation. After completing this course, you’ll have the knowledge to explore the generative AI field and start using ChatGPT and OpenAI’s model APIs in your own projects while keeping some technical considerations in mind, including prompt design and bias mitigation.
Using OpenAI API for Natural Language Processing in Python
In this course, tech teams will learn about the GPT-3 model’s architecture, training methodology, and the vast range of NLP tasks it can perform. The module sets the stage for understanding why GPT-3 is a game-changer in the field of artificial intelligence and NLP. Tech teams get to understand how to set up access to the OpenAI API, including managing authentication, navigating the basic setup, and understanding the available API documentation and resources. As participants conclude this module, they take on learnings on text completion endpoint and how this can be used for automated content generation, code completion, and more. This course also introduces tech teams to using the OpenAI API for text classification tasks, including sentiment analysis and topic categorization which allows them to see how classification can be applied to enhance search engine functionalities and improve content management systems.
Tech teams will also learn best practices for handling large datasets, ensuring data privacy and security, and scaling NLP operations efficiently while simultaneously maintaining high performance and security standards. The course concludes with a step-by-step guide to start your own project using the OpenAI API. Tech teams integrate the API with existing systems, troubleshoot common issues, and optimize their implementations for maximum efficiency. By the end of this course, tech teams will have a thorough understanding of how to utilize the OpenAI API for various NLP tasks.
Using OpenAI API for Natural Language Processing in Python
As consumers rely more and more on search engines and technical software programs to answer their questions, the demand for effective and scalable natural language processing has gone immensely up. OpenAI provides access to the GPT model, which can perform several operations for NLP-related tasks such as summarization, classification, text completion, text insertion, and more. In this course, you’ll learn about the various endpoints of the OpenAI API and how they can be used to accomplish certain NLP tasks. You’ll also look at examples of each endpoint to show how they work. By the time you’re done with this course, you’ll be able to work on your own projects using the OpenAI API.
Final word
The advent of generative AI marks the beginning of a new technology era. It is transforming how software developers write high-quality code with fewer bugs, thereby enhancing overall effectiveness and efficiency. These courses are designed for tech professionals interested in making the most of the power of generative AI in software development. Whether it is web developers, mobile app developers, full-stack developers, or DevOps professionals, these top generative AI courses build a strong conceptual framework and provide grounds for applications.
Enroll now and embark on a transformative learning journey to become a GenAI master with these courses offered at DevPath!
Free Resources
