Generative AI is a rapidly evolving field with a plethora of fascinating applications, from creating realistic images and videos to generating human-like text and beyond. As the technology advances, the demand for skilled professionals who can harness the power of generative AI is growing exponentially. However, navigating the myriad of tutorials and courses available can be overwhelming, especially when trying to acquire these critical skills quickly.
To help you on your journey, we have curated a list of some of the highest-quality courses from respected providers such as DeepLearning.ai, Google Cloud, AWS, IBM, and more. These courses are designed with a strong practical focus, ensuring that you gain real-world skills needed to build applications powered by large language models (LLMs). The best part? Most of these courses are available for free, making it easier than ever to dive into the world of generative AI.
In this article, we provide an overview of these top courses, highlighting their key features and content to help you find the best fit for your learning needs. Whether you’re a beginner just starting out or an advanced developer looking to deepen your expertise, there’s something here for everyone.
Here are the courses we cover:
- Generative AI for Everyone by DeepLearning.ai
- Introduction to Generative AI by Google Cloud
- Generative AI: Introduction and Applications by IBM
- ChatGPT Promt Engineering for Developers by OpenAI and DeepLearning.ai
- LangChain for LLM Application Development by LangChain and DeepLearning.ai
- LangChain: Chat with Your Data by LangChain and DeepLearning.ai
- Open Source Models with Hugging Face by Hugging Face and DeepLearning.ai
- Building LLM Powered Apps by Weights & Biases
- Generative AI with Large Language Models by AWS and DeepLearning.ai
- LLM University by Cohere
- Amazon Bedrock & AWS Generative AI by AWS
- Finetuning Large Language Models by Lamini and DeepLearning.ai
- Reinforcement Learning from Human Feedback by Google Cloud and DeepLearning.ai
- Generative AI for Software Development by DeepLearning.ai
- Generative AI for Developers by Google Cloud
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Top Generative AI Courses with Practical Focus
Now let’s have an overview of some of the top generative AI courses available today. These courses are designed to equip you with practical skills and knowledge to excel in the field of generative AI.
1. Generative AI for Everyone by DeepLearning.ai
Level: Beginner
Duration: 3 hours
Cost: Free
Instructor: Andrew Ng, founder of DeepLearning.ai, co-founder of Google Brain and Coursera
Audience: This course is tailored for anyone keen on understanding the applications, impacts, and foundational technologies of generative AI. No prior coding skills or AI knowledge are required, making it accessible to a broad audience.
Content:
- Introduction to Generative AI: An overview of what generative AI is and its capabilities.
- Applications and Limitations: Insights into what generative AI can and cannot do, helping learners set realistic expectations.
- Practical Uses: Guidance on integrating generative AI into various personal or business contexts.
- Debunking Myths: Addressing common misconceptions about generative AI and promoting a clear understanding.
- Best Practices: Strategies for effective learning and evaluating the potential usefulness of generative AI in different scenarios.
This concise yet comprehensive course offers a foundational understanding of generative AI, making it an excellent starting point for anyone looking to delve into this transformative technology.
2. Introduction to Generative AI by Google Cloud
Level: Beginner
Duration: Specialization with 4 courses (approximately 4 hours total)
Cost: Free
Instructor: Google Cloud Training Team
Audience: This course is ideal for individuals looking to deepen their understanding of generative AI and large language models. While it is beginner-friendly, a basic grasp of AI concepts will help learners fully absorb the material.
Content:
- Generative AI Fundamentals: Defining generative AI and explaining its underlying mechanisms.
- Applications of Generative AI: Exploring various real-world applications and use cases of generative AI.
- Large Language Models: Defining LLMs, their functionalities, and practical use cases.
- Prompt Tuning: An overview of prompt tuning and its significance in optimizing AI outputs.
- Google’s Gen AI Development Tools: Insight into the tools provided by Google for developing generative AI applications.
- Responsible AI Practices: Discussion on responsible AI practices and how Google implements its AI Principles to ensure ethical AI development.
While the course does have a notable focus on Google’s AI practices and tools, it remains a robust introduction to generative AI and LLMs, providing valuable knowledge and insights for anyone interested in the field.
3. Generative AI: Introduction and Applications by IBM
Level: Beginner
Duration: 6 hours
Cost: Free
Instructor: Rav Ahuja, Chief Curriculum Officer and Global Program Director at IBM Skills Network
Audience: This course is perfect for those seeking to understand generative AI with a strong emphasis on practical applications and real-world use cases. It is well-suited for individuals interested in learning about generative AI models and tools across various media formats, including text, code, image, audio, and video.
Content:
- Generative vs. Discriminative AI: Understanding the fundamental differences between generative and discriminative AI.
- Capabilities and Use Cases: Insight into the abilities of generative AI and its practical applications in the real world.
- Sector-Specific Applications: Exploration of how generative AI is applied across different industries and sectors.
- Generative AI Models and Tools: Detailed examination of common generative AI models and tools used for generating text, code, images, audio, and video.
This comprehensive course provides a broad understanding of generative AI, emphasizing its real-world applications and diverse use cases, making it an excellent resource for beginners aiming to grasp the practical aspects of this technology.
4. ChatGPT Promt Engineering for Developers by OpenAI and DeepLearning.ai
Level: Beginner
Duration: 1 hour
Cost: Free
Instructors: Isa Fulford, Member of Technical Staff at OpenAI, and Andrew Ng, founder of DeepLearning.ai, co-founder of Google Brain and Coursera
Audience: This course is designed for developers who are beginning to build applications based on large language models. Basic Python coding skills are recommended to fully benefit from the course content.
Content:
- Introduction into LLMs: An overview of how large language models work.
- Best Practices for Prompt Engineering: Guidance on creating effective prompts for various tasks.
- Using LLM APIs: Practical examples of using LLM APIs in applications for tasks such as:
- Summarizing: Condensing user reviews for brevity.
- Inferring: Performing sentiment classification and topic extraction.
- Transforming Text: Executing tasks like translation, spelling, and grammar correction.
- Expanding Text: Automatically generating content such as emails.
- Effective Prompt Writing: Two key principles for writing effective prompts and systematic approaches to engineering good prompts.
- Building a Custom Chatbot: Step-by-step instructions on building a custom chatbot.
- Hands-on Experience: Numerous examples and interactive exercises in a Jupyter notebook environment to practice prompt engineering.
This succinct course provides developers with the essential skills and knowledge to harness the power of LLMs in their applications, emphasizing practical examples and hands-on experience to ensure a solid understanding of prompt engineering.
5. LangChain for LLM Application Development by LangChain and DeepLearning.ai
Level: Beginner
Duration: 1 hour
Cost: Free
Instructors: Harrison Chase, co-founder and CEO at LangChain, and Andrew Ng, founder of DeepLearning.ai, co-founder of Google Brain and Coursera
Audience: This beginner-friendly course is designed for developers who want to learn how to expand the use cases and capabilities of language models in application development using the LangChain framework. Basic Python knowledge is recommended to maximize the course benefits.
Content:
- Models, Prompts, and Parsers: Learn how to call LLMs, provide effective prompts, and parse the responses.
- Memories for LLMs: Understand how to use memories to store conversations and manage limited context space, enhancing the functionality of your applications.
- Chains: Create sequences of operations to build more complex workflows and capabilities within your applications.
- Question Answering over Documents: Apply LLMs to your proprietary data and specific use case requirements, making your applications more versatile and powerful.
- Agents: Explore the emerging development of LLMs as reasoning agents, opening up new possibilities for advanced application functionalities.
This concise course equips developers with the skills to significantly expand the use cases and capabilities of language models using the LangChain framework, enabling the creation of robust and sophisticated applications in a short amount of time.
6. LangChain: Chat with Your Data by LangChain and DeepLearning.ai
Level: Beginner
Duration: 1 hour
Cost: Free
Instructor: Harrison Chase, co-founder and CEO at LangChain
Audience: This course is aimed at developers who want to learn how to build practical applications that interact with data using LangChain and LLMs. Developers should be familiar with Python.
Content:
- Retrieval Augmented Generation (RAG): Learn how to retrieve contextual documents from external datasets.
- Chatbot Development: Build a chatbot that answers questions based on your documents.
- Document Loading: Explore over 80 loaders to access various data sources, including audio and video.
- Document Splitting: Understand best practices for data splitting.
- Vector Stores and Embeddings: Discover embeddings and vector store integrations in LangChain.
- Advanced Retrieval: Master techniques for accessing and indexing data to retrieve relevant information.
- Question Answering: Create a one-pass question-answering solution.
This concise course provides developers with the skills to effectively use language models and LangChain, enabling the creation of powerful applications using their own data.
7. Open Source Models with Hugging Face by Hugging Face and DeepLearning.ai
Level: Beginner
Duration: 1 hour
Cost: Free
Instructors: Maria Khalusova, Marc Sun, and Younes Belkada from the Hugging Face technical team
Audience: This course is for anyone looking to quickly and easily build AI applications using open-source models.
Content:
- Model Selection: Choose open-source models from the Hugging Face Hub for NLP, audio, image, and multimodal tasks.
- Transformers Library: Learn to use the transformers library to create a chatbot capable of multi-turn conversations.
- NLP Tasks: Translate between languages, summarize documents, and measure text similarity for search and retrieval.
- Audio Tasks: Convert audio to text with Automatic Speech Recognition (ASR) and text to audio with Text-to-Speech (TTS).
- Multimodal Tasks: Generate audio narrations for images by combining object detection and text-to-speech models.
This course provides the essential building blocks to combine into pipelines, enabling you to develop AI-enabled applications using Hugging Face’s open-source models.
8. Building LLM-Powered Apps by Weights & Biases
Level: Intermediate
Duration: 2 hours of video content
Cost: Free
Instructors: Shreya Rajpal, creator of Guardrails AI; Anton Troynikov, co-founder of Chroma; Shahram Anver, co-creator of Rebuff
Audience: This course is designed for developers looking to build LLM applications. Intermediate Python experience is required, but no prior machine learning skills are needed.
Content:
- Fundamentals of AI-Powered Applications: Learn the basics of APIs, chains, and prompt engineering for building AI applications.
- Hands-On Application Development: Follow a step-by-step guide to build your own app, using a support automation bot for a software company as an example.
- Enhancing Your LLM App: Discover methods for improving your LLM-powered app through experimentation and evaluation.
This course equips developers with the necessary skills to create and optimize LLM applications, providing practical insights and hands-on experience.
9. Generative AI with Large Language Models by AWS and DeepLearning.ai
Level: Intermediate
Duration: 16 hours
Cost: Free
Instructors: Chris Fregly and Shelbee Eigenbrode, Principal Solutions Architects for Generative AI at Amazon Web Services (AWS), Antje Barth, Principal Developer Advocate for Generative AI at AWS, and Mike Chambers, Developer Advocate for Generative AI at AWS.
Audience: This course is for developers who want to understand the fundamentals of generative AI and how to deploy it in real-world applications. Intermediate Python coding skills and a basic understanding of machine learning concepts, such as supervised and unsupervised learning, loss functions, and data splitting, are required.
Content:
- Generative AI Lifecycle: Learn the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection to performance evaluation and deployment.
- Transformer Architecture: Gain a detailed understanding of the transformer architecture powering LLMs, including their training process and how fine-tuning adapts them to specific use cases.
- Empirical Scaling Laws: Optimize the model’s objective function by balancing dataset size, compute budget, and inference requirements using empirical scaling laws.
- Advanced Techniques: Apply state-of-the-art methods for training, tuning, inference, and deployment to maximize model performance within project constraints.
- Business Implications: Explore the challenges and opportunities generative AI presents for businesses through insights from industry researchers and practitioners.
This comprehensive course provides developers with the knowledge and tools to effectively deploy generative AI in real-world applications, emphasizing practical techniques and industry insights.
Level: Intermediate to Advanced
Duration: 8 modules consisting of 42 articles, with content available in both video and text formats
Cost: Free
Instructors: Cohere team
Audience: This course is designed for developers and technical professionals who want to quickly and efficiently start building LLM applications.
Content:
- Key Concepts of Large Language Models: Gain a deep understanding of the fundamental concepts behind LLMs.
- Text Representation and Generation: Learn the principles of text representation and how LLMs generate text.
- Deployment: Discover how to deploy LLM applications using various tools.
- Semantic Search: Explore how semantic search works.
- Prompt Engineering: Understand the techniques of prompt engineering.
- Retrieval-Augmented Generation (RAG): Learn how to implement RAG in your applications.
- Tool Use: Get hands-on experience with various tools essential for LLM development.
This comprehensive course provides a thorough grounding in both basic and advanced concepts, enabling developers to understand the inner workings of LLMs and build sophisticated applications.
11. Amazon Bedrock & AWS Generative AI by AWS
Level: Beginner to Advanced
Duration: 11 hours
Cost: $19.99
Instructor: Rahul Trisal, AWS Community Builder in the Serverless Category and Senior AWS Architect with over 15 years of experience in AWS Cloud Strategy, Architecture, and Migration
Audience: This course is aimed at developers who want to build LLM applications using AWS infrastructure. Basic AWS knowledge is recommended, but the course includes a refresher on Python, AWS Lambda, and API Gateway for those who need it.
Content:
- Introduction to AI/ML: Basic overview of AI/ML concepts.
- Generative AI Fundamentals: Learn how generative AI works and explore foundation models in depth.
- Amazon Bedrock: Detailed console walkthrough, architecture, pricing, and inference parameters.
- Use Cases: Seven practical applications including design, text summarization, chatbots, code generation, and more.
- GenAI Project Lifecycle: Comprehensive guide on defining use cases, choosing a foundation model, prompt engineering, and fine-tuning models.
This course provides a thorough introduction to building LLM applications on AWS, covering both foundational concepts and practical implementations to equip developers with the necessary skills and knowledge.
12. Finetuning Large Language Models by Lamini and DeepLearning.ai
Level: Intermediate
Duration: 1 hour
Cost: Free
Instructor: Sharon Zhou, Co-Founder and CEO of Lamini
Audience: This course is designed for learners who want to understand the techniques and applications of finetuning large language models. Familiarity with Python and a deep learning framework such as PyTorch is recommended.
Content:
- Application of Finetuning: Learn when and why to apply finetuning on LLMs.
- Data Preparation: Understand how to prepare your data for finetuning.
- Training and Evaluation: Gain hands-on experience training and evaluating an LLM on your data.
Upon completion, learners will be equipped with the skills to effectively finetune LLMs, enhancing their ability to tailor models to specific applications and datasets.
13. Reinforcement Learning from Human Feedback by Google Cloud and DeepLearning.ai
Level: Intermediate
Duration: 1 hour
Cost: Free
Instructor: Nikita Namjoshi, Developer Advocate at Google Cloud
Audience: This course is for anyone with intermediate Python knowledge interested in learning about using the Reinforcement Learning from Human Feedback (RLHF) technique.
Content:
- Conceptual Understanding of RLHF: Gain insights into the RLHF training process.
- Datasets Exploration: Learn about the “preference” and “prompt” datasets used in RLHF training.
- Practical Application: Use the open-source Google Cloud Pipeline Components Library to fine-tune the Llama 2 model with RLHF.
- Model Assessment: Compare the tuned LLM against the original base model by evaluating loss curves and using the “Side-by-Side (SxS)” method.
This course equips learners with the conceptual and practical skills needed to apply RLHF for tuning LLMs, enhancing their understanding and capabilities in this advanced technique.
14. Generative AI for Software Development by DeepLearning.ai
Level: Intermediate
Duration: 3 courses (around 15 hours), starting on Sep 25, 2024
Cost: Free
Instructor: Laurence Moroney, Chief AI Scientist at VisionWorks Studios and former AI lead at Google
Audience: This course is designed for software developers who want to explore how to use LLMs to improve their efficiency and optimize their code quality.
Content:
- Understanding LLMs: Learn how large language models work to effectively leverage them in your development process.
- Pair-Coding with LLMs: Modify data structures for production and handle big data scales efficiently with the assistance of an LLM.
- Software Testing with LLMs: Use LLMs to identify bugs, create edge case tests, and update code to correct errors, enhancing your software testing processes.
- Database Implementation and Design: Build a local database from scratch and partner with an LLM to optimize software design for efficient and secure data access.
This comprehensive course equips software developers with the knowledge and skills to integrate LLMs into their workflow, enhancing productivity and code quality.
15. Generative AI for Developers by Google Cloud
Level: Intermediate to Advanced
Duration: 11 courses (about 19 hours in total)
Cost: Free
Instructor: Google Cloud team
Audience: This Generative AI Learning Path is tailored for App Developers, Machine Learning Engineers, and Data Scientists. It’s recommended to complete the Introduction to Generative AI learning path before starting this course.
Content:
- Generative AI Applications: Explore various applications, including image generation, image captioning, and text generation.
- Gen AI Model Architectures: Dive deep into model architectures such as the attention mechanism, encoder-decoder architecture, and transformer models.
- Vertex AI Studio: Learn how to use Vertex AI Studio for developing and deploying generative AI models.
- Responsible AI for Developers: Understand the principles of responsible AI and how to implement them in your projects.
- Machine Learning Operations (MLOps) for Generative AI: Gain insights into MLOps practices tailored for generative AI workflows.
Although the course emphasizes Google Cloud infrastructure and practices, it offers a comprehensive understanding of how generative AI works and how to apply these models in real-world scenarios.
Elevate Your Development Skills with Generative AI Courses
As generative AI continues to revolutionize the tech landscape, developers must equip themselves with the latest skills to stay competitive. The courses outlined in this article provide targeted, practical training in generative AI, helping you build sophisticated LLM-powered applications. Featuring instruction from esteemed providers such as DeepLearning.ai, Google Cloud, AWS, and IBM, these courses ensure you gain the expertise needed to thrive in this fast-evolving field.
Whether you’re a beginner ready to start your journey or an experienced developer seeking to enhance your capabilities, these courses offer a clear pathway to mastering generative AI. Embrace these learning opportunities and take your development skills to the next level with confidence and competence.
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