IBM Generative AI Engineering

IBM via Coursera Professional Certificate

Overview

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)
- Offered by IBM. Artificial Intelligence (AI) is all around us, seamlessly integrated into our daily lives and work. Enroll in this course to ... Enroll for free.

Course 2: Generative AI: Introduction and Applications
- Offered by IBM. This course is designed for everyone, including professionals, executives, students, and enthusiasts, interested in learning ... Enroll for free.

Course 3: Generative AI: Prompt Engineering Basics
- Offered by IBM. This course is designed for everyone, including professionals, executives, students, and enthusiasts interested in ... Enroll for free.

Course 4: Python for Data Science, AI & Development
- Offered by IBM. Kickstart your learning of Python with this beginner-friendly self-paced course taught by an expert. Python is one of the ... Enroll for free.

Course 5: Developing AI Applications with Python and Flask
- Offered by IBM. This mini course is intended to apply basic Python skills for developing Artificial Intelligence (AI) enabled applications. ... Enroll for free.

Course 6: Building Generative AI-Powered Applications with Python
- Offered by IBM. Ready for an interactive learning experience to develop applications and chatbots for diverse use cases using generative AI? ... Enroll for free.

Course 7: Data Analysis with Python
- Offered by IBM. Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the ... Enroll for free.

Course 8: Machine Learning with Python
- Offered by IBM. Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to ... Enroll for free.

Course 9: Introduction to Deep Learning & Neural Networks with Keras
- Offered by IBM. Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning ... Enroll for free.

Course 10: Generative AI and LLMs: Architecture and Data Preparation
- Offered by IBM. This IBM short course, a part of Generative AI Engineering Essentials with LLMs Professional Certificate, will teach you the ... Enroll for free.

Course 11: Gen AI Foundational Models for NLP & Language Understanding
- Offered by IBM. This IBM course will teach you how to implement, train, and evaluate generative AI models for natural language processing ... Enroll for free.

Course 12: Generative AI Language Modeling with Transformers
- Offered by IBM. This course provides you with an overview of how to use transformer-based models for natural language processing (NLP). In ... Enroll for free.

Course 13: Generative AI Engineering and Fine-Tuning Transformers
- Offered by IBM. The demand for technical gen AI skills is exploding. Businesses are hunting hard for AI engineers who can work with large ... Enroll for free.

Course 14: Generative AI Advance Fine-Tuning for LLMs
- Offered by IBM. Fine-tuning a large language model (LLM) is crucial for aligning it with specific business needs, enhancing accuracy, and ... Enroll for free.

Course 15: Fundamentals of AI Agents Using RAG and LangChain
- Offered by IBM. Business demand for technical gen AI skills is exploding and AI engineers who can work with large language models (LLMs) are ... Enroll for free.

Course 16: Project: Generative AI Applications with RAG and LangChain
- Offered by IBM. Get ready to put all your gen AI engineering skills into practice! This guided project will test and apply the knowledge and ... Enroll for free.

Courses

2 reviews
15 hours 23 minutes
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Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models. Topics covered include: - collecting and importing data - cleaning, preparing & formatting data - data frame manipulation - summarizing data - building machine learning regression models - model refinement - creating data pipelines You will learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. You will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them. In addition to video lectures you will learn and practice using hands-on labs and projects. You will work with several open source Python libraries, including Pandas and Numpy to load, manipulate, analyze, and visualize cool datasets. You will also work with scipy and scikit-learn, to build machine learning models and make predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge.
4 reviews
13 hours 24 minutes
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Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning. This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more. You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms. By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.
2 reviews
13 hours 3 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 the key AI terminology and applications and 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. Furthermore, you’ll examine AI’s application across domains such as natural language processing (NLP), computer vision, and robotics, uncovering how these advancements drive innovation and use cases. This course explores AI's transformative impact, including generative AI, on businesses. It also explains how AI can revolutionize your work and environment and what career opportunities it offers. Finally, the course explores AI ethics and governance, prevalent concerns and issues surrounding the AI landscape. The course includes hands-on labs and a project, providing an 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 3 minutes
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Kickstart your learning of Python with this beginner-friendly self-paced course taught by an expert. Python is one of the most popular languages in the programming and data science world and demand for individuals who have the ability to apply Python has never been higher. This introduction to Python course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn about Python basics and the different data types. You will familiarize yourself with Python Data structures like List and Tuples, as well as logic concepts like conditions and branching. You will use Python libraries such as Pandas, Numpy & Beautiful Soup. You’ll also use Python to perform tasks such as data collection and web scraping with APIs. 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 anyone who wants to learn Data Science, Data Analytics, Software Development, Data Engineering, AI, and DevOps as well as a number of other job roles.
0 reviews
8 hours 21 minutes
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Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library. After completing this course, learners will be able to: • Describe what a neural network is, what a deep learning model is, and the difference between them. • Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines. • Demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks. • Build deep learning models and networks using the Keras library.
1 review
11 hours 11 minutes
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This mini course is intended to apply basic Python skills for developing Artificial Intelligence (AI) enabled applications. In this hands-on project you will assume the role of a developer and perform tasks including: - Develop functions and application logic - Exchange data using Watson AI libraries - Write unit tests, and - Package the application for distribution. You will demonstrate your foundational Python skills by employing different techniques to develop web applications and AI powered solutions. After completing this course, you will have added another project to your portfolio and gained the confidence to begin developing AI enabled applications using Python and Flask, Watson AI libraries, build and run unit tests, and package the application for distribution out in the real world.
0 reviews
7 hours 11 minutes
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This course is designed for everyone, including professionals, executives, students, and enthusiasts interested in leveraging effective prompt engineering techniques to unlock the full potential of generative artificial intelligence (AI) tools like ChatGPT. Prompt engineering is a process to effectively guide generative AI models and control their output to produce desired results. In this course, you will learn the techniques, approaches, and best practices for writing effective prompts. You will learn about prompt techniques like zero-shot and few-shot, which can improve the reliability and quality of large language models (LLMs). You will also explore various prompt engineering approaches like Interview Pattern, Chain-of-Thought, and Tree-of-Thought, which aim at generating precise and relevant responses. You will be introduced to commonly used prompt engineering tools like IBM watsonx Prompt Lab, Spellbook, and Dust. The hands-on labs included in the course offer an opportunity to optimize results by creating effective prompts in the IBM Generative AI Classroom. You will also hear from practitioners about the tools and approaches used in prompt engineering and the art of writing effective prompts.
0 reviews
6 hours 42 minutes
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This course is designed for everyone, including professionals, executives, students, and enthusiasts, interested in learning about generative AI and leveraging its capabilities in their work and lives. This course is your first step toward understanding the capabilities of generative AI, powered by different models, including large language models (LLMs). In this course, you will learn about the fundamentals and evolution of generative AI. You will explore the capabilities of generative AI in different domains, including text, image, audio, video, virtual worlds, code, and data. You will understand the applications of generative AI across different sectors and industries. You will learn about the capabilities and features of common generative AI models and tools, such as GPT, DALL-E, Stable Diffusion, and Synthesia. Hands-on labs, included in the course, provide an opportunity to explore the use cases of generative AI through IBM Generative AI Classroom and popular tools like ChatGPT. You will also hear from the practitioners about the capabilities, applications, and tools of generative AI.
0 reviews
13 hours 52 minutes
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Ready for an interactive learning experience to develop applications and chatbots for diverse use cases using generative AI? This course provides an opportunity to work on guided projects that provide step-by-step instructions to build generative AI-powered applications. You'll utilize Python, along with related libraries like Flask and Gradio, and frameworks such as Langchain. In the course, you will work on hands-on projects to build chatbots and apps by utilizing popular large language models (LLMs) such as GPT-3 and Llama 2, hosted on platforms such as IBM watsonx and Hugging Face. Additionally, you'll explore retrieval-augmented generation (RAG) technology, enhancing LLMs by incorporating external information beyond their training data. This course also equips you to build voice-enabled chatbots and apps using IBM Watson® Speech Libraries for Embed. To develop these projects, you'll be using Python, making it essential to have a basic understanding of the language. While knowing some HTML, CSS, and JavaScript can be beneficial, it's not a requirement. The course includes supporting videos and readings to build a foundational understanding of models, frameworks, and technologies used in the projects.
0 reviews
7 hours 57 minutes
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This IBM course will teach you how to implement, train, and evaluate generative AI models for natural language processing (NLP). The course will help you acquire knowledge of NLP applications including document classification, language modeling, language translation, and fundamentals for building small and large language models. You will learn about converting words to features. You will understand one-hot encoding, bag-of-words, embedding, and embedding bags. You also will learn how Word2Vec embedding models are used for feature representation in text data. You will implement these capabilities using PyTorch. The course will teach you how to build, train, and optimize neural networks for document categorization. In addition, you will learn about the N-gram language model and sequence-to-sequence models. This course will help you evaluate the quality of generated text using metrics, such as BLEU. You will practice what you learn using Hands-on Labs and perform tasks such as implementing document classification using torchtext in PyTorch. You will gain the skills to build and train a simple language model with a neural network to generate text and integrate pre-trained embedding models, such as word2vec, for text analysis and classification. In addition, you will apply your new skills to develop sequence-to-sequence models in PyTorch and perform tasks such as language translation.
0 reviews
5 hours 38 minutes
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This IBM short course, a part of Generative AI Engineering Essentials with LLMs Professional Certificate, will teach you the basics of using generative AI and Large Language Models (LLMs). This course is suitable for existing and aspiring data scientists, machine learning engineers, deep-learning engineers, and AI engineers. You will learn about the types of generative AI and its real-world applications. You will gain the knowledge 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 will learn the differences in the training approaches used for each model. You will be able to explain the use of LLMs, such as Generative Pre-Trained Transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT). You will also learn about the tokenization process, tokenization methods, and the use of tokenizers for word-based, character-based, and subword-based tokenization. You will be able to explain how you can use data loaders for training generative AI models and list the PyTorch libraries for preparing and handling data within data loaders. The knowledge acquired will help you use the generative AI libraries in Hugging Face. It will also prepare you to implement tokenization and create an NLP data loader. For this course, a basic knowledge of Python and PyTorch and an awareness of machine learning and neural networks would be an advantage, though not strictly required.
0 reviews
8 hours 51 minutes
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This course provides you with an overview of how to use transformer-based models for natural language processing (NLP). In this course, you will learn to apply transformer-based models for text classification, focusing on the encoder component. You’ll learn about positional encoding, word embedding, and attention mechanisms in language transformers and their role in capturing contextual information and dependencies. Additionally, you will be introduced to multi-head attention and gain insights on decoder-based language modeling with generative pre-trained transformers (GPT) for language translation, training the models, and implementing them in PyTorch. Further, you’ll explore encoder-based models with bidirectional encoder representations from transformers (BERT) and train using masked language modeling (MLM) and next sentence prediction (NSP). Finally, you will apply transformers for translation by gaining insight into the transformer architecture and performing its PyTorch implementation. The course offers practical exposure with hands-on activities that enables you to apply your knowledge in real-world scenarios. This course is part of a specialized program tailored for individuals interested in Generative AI engineering. This course requires a working knowledge of Python, PyTorch, and machine learning.
0 reviews
8 hours 2 minutes
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The demand for technical gen AI skills is exploding. Businesses are hunting hard for AI engineers who can work with large language models (LLMs). This Generative AI Engineering and Fine-Tuning Transformers course builds job-ready skills that will power your AI career forward. During this course, you’ll explore transformers, model frameworks, and platforms such as Hugging Face and PyTorch. You’ll begin with a general framework for optimizing LLMs and quickly move on to fine-tuning generative AI models. Plus, you’ll learn about parameter-efficient fine-tuning (PEFT), low-rank adaptation (LoRA), quantized low-rank adaptation (QLoRA), and prompting. Additionally, you’ll get valuable hands-on experience in online labs that you can talk about in interviews, including loading, pretraining, and fine-tuning models with Hugging Face and PyTorch. If you’re keen to take your AI career to the next level and boost your resume with in-demand gen AI competencies that catch the eye of an employer, ENROLL today and have job-ready skills you can use straight away within a week!
0 reviews
6 hours 59 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. During this course, you’ll explore retrieval-augmented generation (RAG), prompt engineering, and LangChain concepts. You’ll look at RAG, its applications, and its process, along with encoders, their tokenizers, and the FAISS library. Then, you’ll apply in-context learning and prompt engineering to design and refine prompts for accurate responses. Plus, you’ll explore LangChain tools, components, and chat models, and work with LangChain to simplify the application development process using LLMs. Additionally, you’ll get valuable hands-on practice in online labs developing applications using integrated LLM, LangChain, and RAG technologies. Plus, you’ll complete a real-world project you can discuss in interviews. If you’re keen to boost your resume and extend your generative AI skills to applying transformer-based LLMs, ENROLL today and build job-ready skills in just 8 hours.
0 reviews
8 hours 16 minutes
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Fine-tuning a large language model (LLM) is crucial for aligning it with specific business needs, enhancing accuracy, and optimizing its performance. In turn, this gives businesses precise, actionable insights that drive efficiency and innovation. This course gives aspiring gen AI engineers valuable fine-tuning skills employers are actively seeking. During this course, you’ll explore different approaches to fine-tuning and causal LLMs with human feedback and direct preference. You’ll look at LLMs as policies for probability distributions for generating responses and the concepts of instruction-tuning with Hugging Face. You’ll learn to calculate rewards using human feedback and reward modeling with Hugging Face. Plus, you’ll explore reinforcement learning from human feedback (RLHF), proximal policy optimization (PPO) and PPO Trainer, and optimal solutions for direct preference optimization (DPO) problems. As you learn, you’ll get valuable hands-on experience in online labs where you’ll work on reward modeling, PPO, and DPO. If you’re looking to add in-demand capabilities in fine-tuning LLMs to your resume, ENROLL TODAY and build the job-ready skills employers are looking for in just two weeks!
0 reviews
9 hours 4 minutes
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Get ready to put all your gen AI engineering skills into practice! This guided project will test and apply the knowledge and understanding you’ve gained throughout the previous courses in the program. You will build your own real-world gen AI application. During this course, you will fill the final gaps in your knowledge to extend your understanding of document loaders from LangChain. You will then apply your new skills to uploading your own documents from various sources. Next, you will look at text-splitting strategies and use them to enhance model responsiveness. Then, you will use watsonx to embed documents, a vector database to store document embeddings, and LangChain to develop a retriever to fetch documents. As you work through your project, you will also implement RAG to improve retrieval, create a QA bot, and set up a simple Gradio interface to interact with your models. By the end of the course, you will have a hands-on project that provides engaging evidence of your generative AI engineering skills that you can talk about in interviews. If you’re ready to add some real-world experience to your portfolio, enroll today and fuel your AI engineering career.

Taught by

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