IBM Generative AI Engineering

IBM via Coursera Professional Certificate

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Overview

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The generative AI market is expected to grow over 46% CAGR to 2030 (Statista). The demand for tech professionals with gen AI engineering skills is exploding! The IBM Generative AI Engineering Professional Certificate gives aspiring gen AI engineers, AI developers, data scientists, machine learning engineers, and AI research engineers the essential skills in gen AI, large language models (LLMs), and natural language processing (NLP) required to catch the eye of an employer. A gen AI engineer designs AI systems that produce new data—like images, text, audio, and video—using transformers and LLMs. In this program, you'll dive into AI, gen AI, and prompt engineering, along with data analysis, machine learning, and deep learning using Python. You'll work with libraries like SciPy and scikit-learn and build apps using frameworks and models such as BERT, GPT, and LLaMA. You'll use Hugging Face Transformers, PyTorch, RAG, and LangChain for developing and deploying LLM NLP-based apps, while exploring tokenization, language models, and transformer techniques. You’ll also get plenty of practical experience in hands-on labs and projects that you can talk about in interviews. Plus, you’ll complete a significant guided project where you’ll create your own real-world gen AI application. If you’re keen to stand out from the crowd with gen AI skills employers desperately need, ENROLL TODAY and transform your career opportunities in less than 6 months.

Syllabus

Course 1: Introduction to Artificial Intelligence (AI)
Course 2: Generative AI: Introduction and Applications
Course 3: Generative AI: Prompt Engineering Basics
Course 4: Python for Data Science, AI & Development
Course 5: Developing AI Applications with Python and Flask
Course 6: Building Generative AI-Powered Applications with Python
Course 7: Data Analysis with Python
Course 8: Machine Learning with Python
Course 9: Introduction to Deep Learning & Neural Networks with Keras
Course 10: Generative AI and LLMs: Architecture and Data Preparation
Course 11: Gen AI Foundational Models for NLP & Language Understanding
Course 12: Generative AI Language Modeling with Transformers
Course 13: Generative AI Engineering and Fine-Tuning Transformers
Course 14: Generative AI Advance Fine-Tuning for LLMs
Course 15: Fundamentals of AI Agents Using RAG and LangChain
Course 16: Project: Generative AI Applications with RAG and LangChain

Courses

2 reviews
17 hours 2 minutes
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Analyzing data with Python is a key skill for aspiring Data Scientists and Analysts! This course takes you from the basics of importing and cleaning data to building and evaluating predictive models. You’ll learn how to collect data from various sources, wrangle and format it, perform exploratory data analysis (EDA), and create effective visualizations. As you progress, you’ll build linear, multiple, and polynomial regression models, construct data pipelines, and refine your models for better accuracy. Through hands-on labs and projects, you’ll gain practical experience using popular Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, SciPy, and Scikit-learn. These tools will help you manipulate data, create insights, and make predictions. By completing this course, you’ll not only develop strong data analysis skills but also earn a Coursera certificate and an IBM digital badge to showcase your achievement.
4 reviews
20 hours 5 minutes
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Python is a core skill in machine learning, and this course equips you with the tools to apply it effectively. You’ll learn key ML concepts, build models with scikit-learn, and gain hands-on experience using Jupyter Notebooks. Start with regression techniques like linear, multiple linear, polynomial, and logistic regression. Then move into supervised models such as decision trees, K-Nearest Neighbors, and support vector machines. You’ll also explore unsupervised learning, including clustering methods and dimensionality reduction with PCA, t-SNE, and UMAP. Through real-world labs, you’ll practice model evaluation, cross-validation, regularization, and pipeline optimization. A final project on rainfall prediction and a course-wide exam will help you apply and reinforce your skills. Enroll now to start building machine learning models with confidence using Python.
2 reviews
12 hours 58 minutes
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Artificial Intelligence (AI) is all around us, seamlessly integrated into our daily lives and work. Enroll in this course to understand key AI terminologies and applications, launch your AI career, or transform your existing one. This course covers core AI concepts, including deep learning, machine learning, and neural networks. You’ll examine generative AI models, including large language models (LLMs) and their capabilities. Further, you’ll analyze the applications of AI across domains, such as natural language processing (NLP), computer vision, and robotics, uncovering how these advancements drive innovation and use cases. The course will help you discover how AI, especially generative AI, is reshaping business and work environments. You’ll also explore emerging career opportunities in this rapidly evolving field and gain insights into ethical considerations and AI governance that shape responsible innovation. The course includes hands-on labs and a project, providing a hands-on opportunity to explore AI’s use cases and applications. You will also hear from expert practitioners about the capabilities, applications, and ethical considerations surrounding AI. This course is suitable for everyone, including professionals, enthusiasts, and students interested in learning the fundamentals of AI.
3 reviews
1 day 1 hour 18 minutes
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Kickstart your Python journey with this beginner-friendly, self-paced course taught by an expert. Python is one of the most popular programming languages, and the demand for individuals with Python skills continues to grow. This course takes you from zero to programming in Python in a matter of hours—no prior programming experience is necessary! You’ll begin with Python basics, including data types, expressions, variables, and string operations. You will explore essential data structures such as lists, tuples, dictionaries, and sets, learning how to create, access, and manipulate them. Next, you will delve into logic concepts like conditions and branching, learning how to use loops and functions, along with important programming principles like exception handling and object-oriented programming. As you progress, you will gain practical experience reading from and writing to files and working with common file formats. You’ll also use powerful Python libraries like NumPy and Pandas for data manipulation and analysis. The course also covers APIs and web scraping, teaching you how to interact with REST APIs using libraries like requests and extract data from websites using BeautifulSoup. You will practice and apply what you learn through hands-on labs using Jupyter Notebooks. By the end of this course, you’ll feel comfortable creating basic programs, working with data, and automating real-world tasks using Python. This course is suitable for individuals interested in pursuing careers in Data Science, Data Analytics, Software Development, Data Engineering, AI, and DevOps and a variety of other technology-related roles.
0 reviews
10 hours 10 minutes
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This course introduces deep learning and neural networks with the Keras library. In this course, you’ll be equipped with foundational knowledge and practical skills to build and evaluate deep learning models. You’ll begin this course by gaining foundational knowledge of neural networks, including forward and backpropagation, gradient descent, and activation functions. You will explore the challenges of deep network training, such as the vanishing gradient problem, and learn how to overcome them using techniques like careful activation function selection. The hands-on labs in this course allow you to build regression and classification models, dive into advanced architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and autoencoders, and utilize pretrained models for enhanced performance. The course culminates in a final project where you’ll apply what you’ve learned to create a model that classifies images and generates captions. By the end of the course, you’ll be able to design, implement, and evaluate a variety of deep learning models and be prepared to take your next steps in the field of machine learning.
1 review
11 hours 40 minutes
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This short course is designed to equip learners with foundational skills in Python for developing AI-enabled web applications using the Flask framework and Watson AI libraries. You will explore the end-to-end lifecycle of building scalable web applications, starting from writing clean and modular Python code to deploying complete AI-powered solutions. The course begins by introducing Flask, a lightweight and flexible web framework, and helps learners build a strong foundation in web applications, APIs, and the overall development lifecycle. They will gain practical experience with the IBM Skills Network Cloud IDE, learn Python best practices including static code analysis, and write and run unit tests to ensure the reliability of their code. The course dives deeper into application development by teaching learners to build web applications with Flask covering topics such as routing, handling GET and POST requests, dynamic routes, and error management. By the end of the course, learners will complete a practice project and a final peer-reviewed project that showcases their ability to develop, test, and deploy AI-powered Flask applications. The hands-on experience gained throughout this course ensures learners are not only confident in their Python and Flask skills but are also ready to build intelligent web applications in real-world settings.
0 reviews
9 hours 16 minutes
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As generative AI (GenAI) reshapes workplaces and job roles, using it effectively is now essential. Prompt engineering is the key to directing GenAI models and refining their output for desired results. This course is for professionals, executives, students, and AI enthusiasts ready to harness prompt engineering to unlock tools like ChatGPT. You’ll learn practical techniques, structured methods, and best practices for crafting strong prompts. Explore zero-shot and few-shot prompting to boost reliability and output quality. Discover advanced methods such as the Interview Pattern, Chain-of-Thought, and Tree-of-Thought to produce accurate, context-aware responses. Hands-on labs and projects provide experience with multimodal prompting, the playoff method, and image generation. You’ll practice blending text and visuals and evaluating AI outputs for precision and usefulness. Podcasts, dialogues, and discussions link theory to real-world scenarios, while expert insights highlight strategies for effective prompt use. A final project and graded assessments ensure you can apply these techniques with confidence, leaving you with practical, job-ready skills. Hear from practitioners about the techniques and artistry behind writing impactful prompts. Enroll today to master prompt engineering and unlock GenAI’s potential.
0 reviews
8 hours 6 minutes
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This course is designed for everyone—professionals, executives, students, and enthusiasts—interested in learning about generative AI and leveraging its capabilities in their work and lives. It is your first step toward understanding the power of generative AI, driven by models such as large language models (LLMs). In this course, you will learn the fundamentals and evolution of generative AI, with additional readings and expert insights offering a deeper view of its history and advancements. You will explore its capabilities across text, image, audio, video, virtual worlds, code, and data, with key takeaways and enhanced summaries at the end of each section to reinforce learning. You will understand the applications of generative AI in industries such as IT, finance, healthcare, education, entertainment, and human resources. You will also discover the features of popular tools and models, including GPT, DALL-E, Stable Diffusion, and Synthesia. Hands-on labs provide opportunities to practice using IBM Generative AI Classroom and tools such as ChatGPT. You will also hear from industry practitioners sharing real-world insights. Interactive activities, podcasts, and scenario-based exercises help you apply concepts, while a final practical project consolidates your skills by generating and refining outputs across multiple formats.
0 reviews
14 hours 49 minutes
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Ready for an interactive learning experience to build real-world generative AI applications and chatbots? In this hands-on course, you’ll develop a series of guided projects using Python, Flask, Gradio, and LangChain to create AI-powered applications for practical scenarios, including a voice assistant, a meeting summarizer, a language translator, and a personalized career coach. You’ll work with popular large language models (LLMs) such as GPT-3, Llama 2, and Flan-UL2, hosted on platforms like IBM watsonx and Hugging Face. You’ll also explore advanced concepts, such as retrieval-augmented generation (RAG), to enhance LLM responses with external knowledge, and integrate speech-to-text (STT) and text-to-speech (TTS) using IBM Watson® Speech Libraries and OpenAI Whisper to enable voice interactions. While a basic understanding of Python is essential, knowledge of HTML, CSS, or JavaScript is helpful but not required. The course includes supporting readings and videos to build foundational knowledge of the models and frameworks used. In addition, a comprehensive course glossary will help reinforce your learning.
0 reviews
9 hours 57 minutes
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This IBM course will equip you with the skills to implement, train, and evaluate generative AI models for natural language processing (NLP) using PyTorch. You will explore core NLP tasks, such as document classification, language modeling, and language translation, and gain a foundation in building small and large language models. You will learn how to convert words into features using one-hot encoding, bag-of-words, embeddings, and embedding bags, as well as how Word2Vec models represent semantic relationships in text. The course covers training and optimizing neural networks for document categorization, developing statistical and neural N-Gram models, and building sequence-to-sequence models using encoder–decoder architectures. You will also learn to evaluate generated text using metrics such as BLEU. The hands-on labs provide practical experience with tasks such as classifying documents using PyTorch, generating text with language models, and integrating pretrained embeddings like Word2Vec. You will also implement sequence-to-sequence models to perform tasks such as language translation. Enroll today to build in-demand NLP skills and start creating intelligent language applications with PyTorch.
0 reviews
5 hours 41 minutes
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Ready to explore the exciting world of generative AI and large language models (LLMs)? This IBM course, part of the Generative AI Engineering Essentials with LLMs Professional Certificate, gives you practical skills to harness AI to transform industries. Designed for data scientists, ML engineers, and AI enthusiasts, you’ll learn to differentiate between various generative AI architectures and models, such as recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. You’ll also discover how LLMs, such as generative pretrained transformers (GPT) and bidirectional encoder representations from transformers (BERT), power real-world language tasks. Get hands-on with tokenization techniques using NLTK, spaCy, and Hugging Face, and build efficient data pipelines with PyTorch data loaders to prepare models for training. A basic understanding of Python, PyTorch, and familiarity with machine learning and neural networks are helpful but not mandatory. Enroll today and get ready to launch your journey into generative AI!
0 reviews
9 hours 18 minutes
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This course provides a practical introduction to using transformer-based models for natural language processing (NLP) applications. You will learn to build and train models for text classification using encoder-based architectures like Bidirectional Encoder Representations from Transformers (BERT), and explore core concepts such as positional encoding, word embeddings, and attention mechanisms. The course covers multi-head attention, self-attention, and causal language modeling with GPT for tasks like text generation and translation. You will gain hands-on experience implementing transformer models in PyTorch, including pretraining strategies such as masked language modeling (MLM) and next sentence prediction (NSP). Through guided labs, you’ll apply encoder and decoder models to real-world scenarios. This course is designed for learners interested in generative AI engineering and requires prior knowledge of Python, PyTorch, and machine learning. Enroll now to build your skills in NLP with transformers!
0 reviews
8 hours 2 minutes
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The demand for technical generative AI (GenAI) skills is increasing, and businesses are actively seeking AI engineers who can work with large language models (LLMs). This IBM course is designed to build job-ready skills that can accelerate your AI career. In this course, you’ll explore transformers and key model frameworks and platforms, including Hugging Face and PyTorch. You’ll begin with a foundational framework for optimizing LLMs and quickly advance to fine-tuning generative AI models. You’ll also learn advanced techniques such as parameter-efficient fine-tuning (PEFT), low-rank adaptation (LoRA), quantized LoRA (QLoRA), and prompting. The hands-on labs will give you valuable, practical experience including loading, pretraining, and fine-tuning models using industry-standard tools. These skills are directly applicable in real-world AI roles and are great for showcasing in interviews. If you’re ready to take your AI career to the next level and strengthen your resume with in-demand Gen AI competencies, enroll today and start applying your new skills in just one week!
0 reviews
9 hours 16 minutes
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Business demand for technical gen AI skills is exploding, and AI engineers who can work with large language models (LLMs) are in high demand. This Fundamentals of Building AI Agents using RAG and LangChain course builds job-ready skills that will fuel your AI career. In this course, you’ll explore retrieval-augmented generation (RAG), prompt engineering, and LangChain concepts. You’ll learn about the RAG process, its applications, encoders and tokenizers, and the FAISS library for high-dimensional vector search. Then, you’ll apply in-context learning and advanced prompt engineering techniques, including prompt templates and example selectors, to generate accurate responses. You’ll also work with LangChain’s tools, components, document loaders, retrievers, chains, and agents to simplify LLM-based application development. Through hands-on labs, you’ll develop AI agents that integrate LLMs, LangChain, and RAG technologies. You will also complete a real-world project you can showcase in interviews. A comprehensive cheat sheet and glossary are included to reinforce your learning. Enroll today and build in-demand generative AI skills in just 8 hours!
0 reviews
9 hours 1 minute
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"Fine-tuning large language models (LLMs) is essential for aligning them with specific business needs, improving accuracy, and optimizing performance. In today’s AI-driven world, organizations rely on fine-tuned models to generate precise, actionable insights that drive innovation and efficiency. This course equips aspiring generative AI engineers with the in-demand skills employers are actively seeking. You’ll explore advanced fine-tuning techniques for causal LLMs, including instruction tuning, reward modeling, and direct preference optimization. Learn how LLMs act as probabilistic policies for generating responses and how to align them with human preferences using tools such as Hugging Face. You’ll dive into reward calculation, reinforcement learning from human feedback (RLHF), proximal policy optimization (PPO), the PPO trainer, and optimal strategies for direct preference optimization (DPO). The hands-on labs in the course will provide real-world experience with instruction tuning, reward modeling, PPO, and DPO, giving you the tools to confidently fine-tune LLMs for high-impact applications. Build job-ready generative AI skills in just two weeks! Enroll today and advance your career in AI!"
0 reviews
9 hours 6 minutes
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Get ready to put your generative AI engineering skills into practice! In this hands-on guided project, you’ll apply the knowledge and techniques gained throughout the previous courses in the program to build your own real-world generative AI application. You’ll begin by filling in key knowledge gaps, such as using LangChain’s document loaders to ingest documents from various sources. You’ll then explore and apply text-splitting strategies to improve model responsiveness and use IBM watsonx to embed documents. These embeddings will be stored in a vector database, which you’ll connect to LangChain to develop an effective document retriever. As your project progresses, you’ll implement retrieval-augmented generation (RAG) to enhance retrieval accuracy, construct a question-answering bot, and build a simple Gradio interface for interactive model responses. By the end of the course, you’ll have a complete, portfolio-ready AI application that showcases your skills and serves as compelling evidence of your ability to engineer real-world generative AI solutions. If you're ready to elevate your career with hands-on experience, enroll today and take the next step toward becoming a confident AI engineer.

Taught by

Abhishek Gagneja, Alex Aklson, Antonio Cangiano, Ashutosh Sagar, Fateme Akbari, IBM Skills Network Team, Jeff Grossman, Joseph Santarcangelo, Kang Wang, Ramesh Sannareddy, Rav Ahuja, Roodra Pratap Kanwar, SAEED AGHABOZORGI, Sina Nazeri and Wojciech 'Victor' Fulmyk