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Rav Ahuja

Generative AI modeling is an in-demand skill for AI model development. Employers now expect generative AI skills to be on an AI engineer’s resume. This hands-on course, which is also part of the IBM AI Applied Professional Certificate, will help you build the generative AI skills you need to stand out as an AI developer.

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Generative AI modeling is an in-demand skill for AI model development. Employers now expect generative AI skills to be on an AI engineer’s resume. This hands-on course, which is also part of the IBM AI Applied Professional Certificate, will help you build the generative AI skills you need to stand out as an AI developer.

Throughout the course, you’ll get valuable practical experience working on guided projects that provide step-by-step instructions for building generative AI-powered applications. As part of this, you’ll work with Python and related libraries like Flask and Gradio, plus you’ll use frameworks such as Langchain. The course includes learning elements such as videos and readings to help you understand the models, frameworks, and technologies used in the projects.

You’ll also dive into building intelligent chatbots and apps using popular large language models (LLMs) such as GPT3 and Llama 2 hosted on platforms like IBM watsonx and Hugging Face. You'll explore retrieval-augmented generation (RAG) technology to enhance LLMs by incorporating external information beyond their training data. You’ll be able to build voice-enabled chatbots and apps using IBM Watson ® Speech Libraries for Embed.

To get the most out of this course, it is essential that you have a basic understanding of the Python programming language. It is also of benefit if you are familiar with HTML, CSS, and JavaScript, though this is not a requirement. This course is ideal for tech professionals who have some experience with Python and are ready to build the highly sought-after generative AI skills required to be an AI engineer or AI developer. If that’s you… enroll today and build job-ready gen AI skills in 6 weeks.

What's inside

Learning objectives

  • Job-ready generative ai app development skills in 6 weeks, supported by practical experience and an industry-recognized credential.
  • How to integrate and enhance large language models (llms) using rag technology to build intelligent apps and chatbots.
  • How to use python libraries like flask and gradio to create web applications that interact with generative ai models.
  • How to use different frameworks and ai technologies to build ai-powered applications.
  • How to build generative ai-powered applications and chatbots using generative ai models, python, and related frameworks.

Syllabus

Module 1: Image Captioning with Generative AI
• Video: Course Introduction
• Reading: Course Overview
• Reading: Helpful Tips for Course Completion
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches current, industry relevant Generative AI skills, which are in-demand for AI Developers
Taught by an experienced instructor from the industry
Develops practical, hands-on skills for building Generative AI-powered applications
Part of a larger, more comprehensive IBM AI Applied Professional Certificate program
Requires only basic Python knowledge, making it accessible to learners with varied backgrounds
Covers the latest advancements in Generative AI, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) technology

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Reviews summary

Hands-on gen ai app development with python

According to students, this program offers a highly practical and hands-on approach to developing Generative AI applications using Python. Learners can expect to build job-ready skills through guided projects that integrate industry-standard tools and frameworks like Flask, Gradio, and Langchain, alongside popular LLMs such as GPT3 and Llama 2. The curriculum emphasizes practical application, covering a range of topics from image captioning to voice assistants and RAG-enhanced chatbots. While requiring a basic understanding of Python, it provides a solid foundation for tech professionals aiming to advance their careers in AI development, leveraging IBM's expertise and platforms like watsonx.ai for relevant and modern skill acquisition.
Requires a basic understanding of Python for optimal learning.
"I understand that a basic understanding of the Python programming language is essential."
"This course is ideal for me as a tech professional with some existing Python experience."
"I should ensure my Python fundamentals are solid to get the most out of the labs."
Leverages IBM's platforms and is part of their professional certificate.
"I appreciate that this course is part of the IBM AI Applied Professional Certificate."
"Working with IBM watsonx.ai and Watson Speech Libraries provides valuable exposure."
"The publisher's reputation adds credibility to the learning experience."
Explores a wide range of practical generative AI applications.
"I'm excited about building various applications, from image captioning to voice assistants."
"The modules seem to cover distinct and practical use cases like a Generative AI-Powered Meeting Assistant."
"I expect to build different chatbots, including one that summarizes private data with RAG."
Equips learners with in-demand skills for AI development careers.
"I anticipate gaining job-ready generative AI app development skills in just six weeks."
"This course is designed to help me stand out as an AI developer."
"I'm here to build highly sought-after generative AI skills required for an AI engineering role."
Utilizes modern and relevant Gen AI tools and frameworks.
"I will work with industry-standard libraries like Flask, Gradio, and Langchain."
"The coverage of popular LLMs such as GPT3 and Llama 2 is highly relevant."
"I'm keen to use IBM watsonx and Hugging Face platforms for deployment and models."
Focuses on hands-on building of Gen AI applications.
"I appreciate the valuable practical experience gained from the guided projects."
"The hands-on coding and project work are central to building my skills."
"I'm looking forward to building real Generative AI applications step-by-step."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Developing Generative AI Applications with Python with these activities:
Review 'Deep Learning with Python'
Review the core concepts of deep learning before the course begins
Show steps
  • Read through the first two chapters
  • Complete the first three exercises
  • Summarize the key concepts in your own words
Review Python
Refresh your foundational knowledge of Python to ensure you're prepared for this course.
Browse courses on Python
Show steps
  • Review Python fundamentals (variables, data types, control flow)
  • Practice writing simple Python scripts
Join a Study Group
Connect with other learners to discuss course concepts and share ideas
Browse courses on Generative AI
Show steps
  • Find a study group or create your own
  • Meet regularly to discuss the course material
  • Work together on projects and assignments
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Review the basics of Python programming
Review the basics of Python programming to ensure a solid foundation for the course.
Browse courses on Python Programming
Show steps
  • Go over basic Python syntax and data types.
  • Practice writing simple Python programs.
Follow Tutorials on Large Language Models (LLMs)
Enhance your understanding of LLMs and their capabilities
Browse courses on Generative AI
Show steps
  • Search for tutorials on LLMs from credible sources
  • Follow the tutorials step-by-step
  • Replicate the examples provided in the tutorials
Practice Using Generative AI Libraries
Gain proficiency in using the libraries covered in the course
Browse courses on Generative AI
Show steps
  • Install the required libraries
  • Complete the practice exercises provided in the course materials
  • Experiment with different library functions and parameters
Create a chatbot using a generative AI model
Create a chatbot using a generative AI model to gain hands-on experience in applying AI techniques.
Browse courses on Generative AI
Show steps
  • Choose a generative AI model and set up the necessary infrastructure.
  • Train the model on a relevant dataset.
  • Design and implement the chatbot interface.
  • Test and evaluate the chatbot's performance.
Build a Generative AI Chatbot
Apply your skills to create a practical application of generative AI
Browse courses on Generative AI
Show steps
  • Design the chatbot's functionality and user interface
  • Choose a generative AI model and integrate it into your chatbot
  • Train the chatbot on a relevant dataset
  • Test and refine the chatbot's performance
Create a Generative AI Application
Apply your learned skills to build a real-world AI application
Browse courses on Generative AI
Show steps
  • Choose a project idea
  • Design the application architecture
  • Implement the application using Python and generative AI models
  • Deploy the application to a cloud platform or host it on your own server
Contribute to Open Source Generative AI Projects
Gain practical experience and contribute to the field of generative AI
Browse courses on Generative AI
Show steps
  • Find open-source generative AI projects on platforms like GitHub or GitLab
  • Review the project documentation and codebase
  • Identify ways you can contribute, such as fixing bugs, adding features, or improving documentation
  • Submit your contributions to the project

Career center

Learners who complete Developing Generative AI Applications with Python will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Developing Generative AI Applications with Python.
Co-authored by core Hugging Face engineers, this definitive manual teaches how to fine-tune, evaluate, and deploy transformer models across classification, named entity recognition, question answering, and text generation tasks. Clear PyTorch and Datasets workflows make it the primary cornerstone for mastering the Hugging Face ecosystem.
Guiding developers through implementing a GPT-style decoder in pure PyTorch, Sebastian Raschka demystifies tokenization, multi-head self-attention, pretraining loops, and weight loading. Subsequent chapters walk step-by-step through classification fine-tuning and instruction alignment.
Visual explanations paired with practical Hugging Face Transformers and Sentence-Transformers code demystify tokenization, semantic search, prompt engineering, and parameter-efficient fine-tuning. Readers gain immediate intuition for representation models and generative pipelines using modern open-weight checkpoints.
Considered a foundational text in natural language processing, this book provides a deep dive into the subject, essential for understanding the linguistic underpinnings of chatbots. It's widely used in undergraduate and graduate NLP and computational linguistics courses, offering both theory and application. is invaluable for building a solid background in the field.
Graduate-level textbook on deep learning. It covers a wide range of deep learning topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It is written by three leading AI researchers who are also professors at the University of Montreal.
Is an excellent starting point for anyone new to Python or programming in general. It covers fundamental programming concepts and Python basics with a hands-on, project-based approach, making it very practical for beginners. The third edition is updated to cover newer Python versions and is widely recommended for its clear explanations and engaging projects. It's often used as an introductory textbook.
Authored by Hugging Face engineers, this hands-on reference expands beyond text into image synthesis, audio generation, and multimodal pipelines using the Diffusers and Transformers libraries. Practical chapters cover DreamBooth, LoRA, ControlNet, and alignment techniques for production creative workflows.
Synthesizing production engineering practices for foundation models, Chip Huyen covers evaluation benchmarks, prompt orchestration, RAG pipelines, fine-tuning trade-offs, latency optimization, and KV-cache memory management. Teams gain a rigorous framework for shipping reliable LLM systems.
Ideal for beginners who want to quickly apply Python to practical tasks. focuses on using Python to automate everyday computer tasks, such as working with files, web scraping, and sending emails. It assumes no prior programming experience and is highly regarded for its clear, step-by-step instructions and focus on immediate productivity gains. The second edition widely used and practical resource.
Provides best practices and techniques for creating and improving conversational AI, including incorporating generative AI. It covers planning for continuous improvement and evaluating user experience, making it a valuable resource for building reliable and effective enterprise-level chatbots. This practical guide for developers and designers.
End-to-end engineering workflows walk through dataset curation, supervised fine-tuning with TRL and Unsloth, DPO preference alignment, and model publishing on the Hugging Face Hub. Practitioners learn how to connect open-source model training directly to scalable inference and RAG serving pipelines.
Focuses on the user experience (UX) design of conversational interfaces, including chatbots and voice assistants. It emphasizes understanding user needs and designing intuitive and effective conversational flows. This is essential reading for anyone involved in the design aspect of chatbots.
Graduate-level textbook on computer vision. It covers a wide range of computer vision topics, including image processing, image segmentation, and object detection. It is written by a leading computer vision researcher who is also a professor at the University of Washington.
Provides a practical guide to using generative AI, covering the different techniques and tools available. It is written by two leading experts in the field, Josh Patterson and Adam Gibson.
Delves into applying deep learning techniques to NLP problems. Given the prevalence of deep learning in modern chatbots and LLMs, this book is crucial for understanding the underlying technology driving more advanced conversational AI. It covers concepts like neural networks and their application in NLP tasks.
Is highly recommended for intermediate to advanced Python programmers looking to write more idiomatic and efficient code. It explores Python's often-overlooked features and best practices, delving into topics like data structures, the Python data model, and metaprogramming. It's a valuable resource for deepening understanding and is considered a must-read for those aiming for mastery.
Comprehensive guide to Flask web development. It covers everything from the basics of Flask to more advanced topics such as deploying Flask applications. The book is well-written and easy to follow, making it a great choice for beginners and experienced developers alike.
Bridging open-source model hubs with enterprise cloud infrastructure, this guide demonstrates how to fine-tune Hugging Face models with QLoRA, PEFT, and RLHF on managed compute. Architectural patterns cover quantization, distributed training, and low-latency deployment for enterprise applications.
Provides a comprehensive overview of reinforcement learning, including a chapter on IBM Watsonx. It good resource for anyone who wants to learn more about the theory and practice of reinforcement learning.
Provides a comprehensive overview of machine learning, including a chapter on IBM Watsonx. It good resource for anyone who wants to learn more about the theory and practice of machine learning.
Provides a visionary look at the future of artificial intelligence, including the potential impact of IBM Watsonx and other AI platforms. It must-read for anyone who wants to understand the future of AI and its implications for humanity.
Provides a comprehensive overview of the AI landscape, including the latest advances in IBM Watsonx and other AI platforms. It also discusses the ethical implications of AI and the potential impact of AI on the future of work and society.
Classic textbook on AI. It provides a comprehensive overview of the field, including its history, current state, and future potential. It covers a wide range of AI topics, including machine learning, deep learning, and natural language processing. It is written by two leading AI researchers.
Focuses on the application of neural networks to NLP problems. It's a good resource for understanding how deep learning architectures are specifically applied to process and generate human language, which is directly applicable to building neural network-based chatbots. The author provides a mathematical approach with practical examples.
Dan Jurafsky and James H. Martin's foundational computational linguistics textbook establishes the mathematical bedrock of n-gram probabilities, perplexity, tokenization, information retrieval, and sequence modeling that every large language model researcher builds upon.
Provides a practical introduction to machine learning using Python and the scikit-learn library. It's ideal for Python programmers interested in entering the field of machine learning and data science. It covers essential concepts and provides hands-on examples.
Offers a practical introduction to NLP using the Natural Language Toolkit (NLTK) in Python. It's a helpful resource for those who want to get hands-on with NLP concepts and build basic language processing applications, which are fundamental to chatbot development. This book is particularly useful for beginners and those with a programming background.
Provides a comprehensive guide to generative AI, covering both fundamental principles and practical applications in an enterprise context. Understanding generative AI is essential for working with the latest chatbot technologies. It's a relevant resource for those looking to apply generative AI in real-world scenarios.
Delves into the challenge of aligning advanced AI systems with human goals and values. This is highly relevant to chatbot development as ensuring chatbots are helpful and safe is paramount. It provides a detailed look at the technical and philosophical aspects of this critical issue.
Focuses on the practical aspects of building NLP systems for real-world applications, including chatbots. It covers the NLP pipeline and various use cases, providing valuable insights for developing functional conversational AI. It's a good resource for those interested in the implementation details.
Denis Rothman bridges Hugging Face transformers, vision-language models, and retrieval-augmented generation in Python, walking practitioners through attention mechanics, fine-tuning open-weight models, and multimodal deployment.
This concise book provides a fast-paced introduction to language models, including the fundamentals of transformer architectures and LLMs. It's a good resource for quickly grasping the core concepts behind the language models that power modern chatbots. It is suitable for those with some ML background.
Offers a collection of tips and tricks to help intermediate Python developers write more professional and Pythonic code. It provides concise explanations and practical examples of various Python features, making it a useful resource for leveling up coding skills and discovering best practices.
Investigating scaling laws, tokenization quirks, out-of-distribution generalization, and inference-time compute strategies, Suhas Pai bridges LLM research insights with pragmatic application architecture and safety evaluation.
Focused specifically on the Hugging Face ecosystem, this recipes-driven tutorial walks through custom tokenizer training, multilingual transfer learning, and model tracking. Developers gain hands-on experience configuring Trainer arguments, managing custom datasets, and exporting models for production.
Provides a guide to getting started with building chatbots, covering technological implementation and user experience. It's suitable for beginners who want to learn the basics of creating chatbots for various platforms. It's a practical entry point into the field.
Drawing on AWS distributed training experience, Emily Webber and Andrea Olgiati demystify dataset curation, tokenizer design, and multi-GPU pretraining loops for teams building custom foundation models from raw text.
Clear visual derivations of dot-product self-attention, residual streams, layer normalization, and contrastive pretraining make Simon Prince's MIT Press textbook an ideal mathematical companion for students studying Transformer architectures.
This guide provides a collection of best practices, development workflows, and recommendations for writing idiomatic Python code. It's a valuable resource for developers who want to follow community conventions and improve their overall Python development process.
Focusing on application-layer orchestration, Ben Auffarth walks Python developers through tool-calling agents, structured output parsing, conversational memory, and vector-store retrieval chains. Practical guardrails and evaluation callbacks help move LLM prototypes toward production.
Balancing proprietary APIs and open-weight models, Sinan Ozdemir illustrates prompt engineering, semantic chunking, embedding clustering, and custom fine-tuning workflows. Concise case studies show how to align model capabilities with enterprise latency and cost constraints.
Because PyTorch serves as the primary tensor engine underneath Hugging Face Transformers, Accelerate, and PEFT, mastering autograd, custom Dataset classes, and GPU memory semantics is indispensable. Clear explanations prepare engineers to debug custom training loops and mixed-precision execution.
Provides a business-oriented perspective on generative AI, discussing its potential impact on industries and how companies can use it to gain a competitive advantage. It is written by three leading experts in the field, Thomas Davenport, Rajeev Ronanki, and Nitin Mittal.
Great introduction to Flask for beginners. It covers the basics of Flask in a clear and concise way. The book also includes several examples that can be used to build your own Flask applications.
This comprehensive handbook covers a wide range of NLP techniques, from classical to statistical approaches, and explores various applications. It serves as a valuable reference tool for understanding different methods used in building NLP systems, including those relevant to chatbots. is suitable for researchers and practitioners seeking a broad overview of the field.
Provides a comprehensive overview of deep learning for natural language processing, including a chapter on IBM Watsonx's natural language processing capabilities. It good resource for those who want to learn more about how deep learning can be used to solve NLP problems.
Provides a comprehensive overview of computer vision with deep learning, including a chapter on IBM Watsonx's computer vision capabilities. It good resource for those who want to learn more about how deep learning can be used to solve computer vision problems.

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