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Develop job-ready gen AI skills employers need. Build highly sought-after gen AI engineering skills and practical experience in just 6 months. No prior experience required.
Instructors: IBM Skills Network Team
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(499 reviews)
Recommended experience
Beginner level
Basic computer literacy
(499 reviews)
Recommended experience
Beginner level
Basic computer literacy
Job-ready skills employers are crying out for in gen AI, machine learning, deep learning, NLP apps, and large language models in just 6 months.
Build and deploy generative AI applications, agents and chatbots using Python libraries like Flask, SciPy and ScikitLearn, Keras, and PyTorch.
Key gen AI architectures and NLP models, and how to apply techniques like prompt engineering, model training, and fine-tuning.
Apply transformers like BERT and LLMs like GPT for NLP tasks, with frameworks like RAG and LangChain.
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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.
Applied Learning Project
Practical Experience Employers Look For
Practical experience speaks volumes in a job interview. This Professional Certificate gives you valuable hands-on experience that confirms to employers you’ve got what it takes!
The hands-on work includes:
Generating text, images, and code through gen AI
Applying prompt engineering techniques and best practices
Creating multiple gen AI-powered applications with Python and deploying them using Flask
Creating an NLP data loader
Developing and training a simple language model with a neural network
Applying transformers for classification, and building and evaluating a translation model
Performing prompt engineering and in-context learning
Fine-tuning models to improve performance
Using LangChain tools and components for different applications
Building AI agents and applications with RAG and LangChain in a significant guided project.
Describe what AI is and explain the core concepts related to AI
Demonstrate how AI applications and use cases can transform our lives and our work
Recognize the potential and impact of AI to transform businesses and careers
Describe the issues, limitations, and ethical concerns surrounding AI
Describe generative AI and distinguish it from discriminative AI.
Describe the capabilities of generative AI and its use cases in the real world.
Identify the applications of generative AI in different sectors and industries.
Explore common generative AI models and tools for text, code, image, audio, and video generation.
Explain the concept and relevance of prompt engineering in generative AI models.
Apply best practices for creating prompts and explore examples of impactful prompts.
Practice common prompt engineering techniques and approaches for writing effective prompts.
Explore commonly used tools for prompt engineering to aid with prompt engineering.
Learn Python - the most popular programming language and for Data Science and Software Development.
Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.
Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.
Access and web scrape data using APIs and Python libraries like Beautiful Soup.
Describe the steps and processes involved in creating a Python application including the application development lifecycle
Create Python modules, run unit tests, and package applications while ensuring the PEP8 coding best practices
Explain the features of Flask and deploy applications on the web using the Flask framework
Create and deploy an AI-based application onto a web server using IBM Watson AI Libraries and Flask
Explain the core concepts of generative AI models, AI technologies, and AI platforms such as IBM watsonx and Hugging Face.
Integrate and enhance large language models (LLMs) using RAG technology to infuse intelligence into apps and chatbots.
Utilize Python libraries like Flask and Gradio to create web applications that interact with generative AI models.
Build generative AI-powered applications and chatbots using generative AI models, Python, and related frameworks.
Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data
Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy
Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines
Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making
Job-ready foundational machine learning skills in Python in just 6 weeks, including how to utilizeScikit-learn to build, test, and evaluate models.
How to apply data preparation techniques and manage bias-variance tradeoffs to optimize model performance.
How to implement core machine learning algorithms, including linear regression, decision trees, and SVM, for classification and regression tasks.
How to evaluate model performance using metrics, cross-validation, and hyperparameter tuning to ensure accuracy and reliability.
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.
Differentiate between generative AI architectures and models, such as RNNs, Transformers, VAEs, GANs, and Diffusion Models.
Describe how LLMs, such as GPT, BERT, BART, and T5, are used in language processing.
Implement tokenization to preprocess raw textual data using NLP libraries such as NLTK, spaCy, BertTokenizer, and XLNetTokenizer.
Create an NLP data loader using PyTorch to perform tokenization, numericalization, and padding of text data.
Explain how to use one-hot encoding, bag-of-words, embedding, and embedding bags to convert words to features.
Build and use word2vec models for contextual embedding.
Build and train a simple language model with a neural network.
Utilize N-gram and sequence-to-sequence models for document classification, text analysis, and sequence transformation.
Explain the concept of attention mechanisms in transformers, including their role in capturing contextual information.
Describe language modeling with the decoder-based GPT and encoder-based BERT.
Implement positional encoding, masking, attention mechanism, document classification, and create LLMs like GPT and BERT.
Use transformer-based models and PyTorch functions for text classification, language translation, and modeling.
Sought-after job-ready skills businesses need for working with transformer-based LLMs for generative AI engineering... in just 1 week.
How to perform parameter-efficient fine-tuning (PEFT) using LoRA and QLoRA
How to use pretrained transformers for language tasks and fine-tune them for specific tasks.
How to load models and their inferences and train models with Hugging Face.
In-demand gen AI engineering skills in fine-tuning LLMs employers are actively looking for in just 2 weeks
Instruction-tuning and reward modeling with the Hugging Face, plus LLMs as policies and RLHF
Direct preference optimization (DPO) with partition function and Hugging Face and how to create an optimal solution to a DPO problem
How to use proximal policy optimization (PPO) with Hugging Face to create a scoring function and perform dataset tokenization
In-demand job-ready skills businesses need for building AI agents using RAG and LangChain in just 8 hours.
How to apply the fundamentals of in-context learning and advanced methods of prompt engineering to enhance prompt design.
Key LangChain concepts, tools, components, chat models, chains, and agents.
How to apply RAG, PyTorch, Hugging Face, LLMs, and LangChain technologies to different applications.
Gain practical experience building your own real-world gen AI application that you can talk about in interviews.
Get hands-on using LangChain to load documents and apply text splitting techniques with RAG and LangChain to enhance model responsiveness.
Create and configure a vector database to store document embeddings and develop a retriever to fetch document segments based on queries.
Set up a simple Gradio interface for model interaction and construct a QA bot using LangChain and an LLM to answer questions from loaded documents.
At IBM, we know how rapidly tech evolves and recognize the crucial need for businesses and professionals to build job-ready, hands-on skills quickly. As a market-leading tech innovator, we’re committed to helping you thrive in this dynamic landscape. Through IBM Skills Network, our expertly designed training programs in AI, software development, cybersecurity, data science, business management, and more, provide the essential skills you need to secure your first job, advance your career, or drive business success. Whether you’re upskilling yourself or your team, our courses, Specializations, and Professional Certificates build the technical expertise that ensures you, and your organization, excel in a competitive world.
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Earn a degree from world-class universities - 100% online
Upskill your employees to excel in the digital economy
This PC is self-paced. If you spend 5-6 hours a week learning, you can complete the PC in just under 6 months!
Basic computer literacy.
Yes, it is highly recommended that you take the courses in the order they are presented. Each course and its modules build on the knowledge you've gained in earlier modules.
Not at this time.
This PC is intended to give you the skills and confidence you need to take on jobs such as Generative AI Engineer, AI Research Engineer, NLP Engineer, Machine Learning Engineer (Generative Models), Deep Learning Engineer, and Data Scientist
A gen AI engineer designs and develops AI systems that can generate new data, such as images, text, audio, and video. They leverage transformers to create synthetic data and solve creative and practical problems in diverse industries such as entertainment, healthcare, and finance. Gen AI engineers also use large language models (LLMs) and machine learning to design systems that understand human language.
This PC is designed for those interested in gen AI engineering, including training, developing, fine-tuning, and deploying large language models as well as developing agents and applications that leverage these AI models. This includes custom-built models as well as pre-trained models, such as generative pre-trained transformers (GPT) and BERT, for building natural language processing (NLP)-based applications. The PC will train you for an entry-level gen AI engineer role from scratch and give you the ability to work with newer generations of AI models and vector databases.
It is also suitable for:
Software developers and programmers who want to specialize their skills in generative AI
Aspiring software engineers who want to enhance their skills to create innovative applications powered by generative AI
Working professionals and technophiles from other fields who want to transition to software engineering or generative AI
Students and beginners who want to kickstart their careers in programming and software engineering, with a focus on the cutting-edge field of generative AI
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Certificate, you’re automatically subscribed to the full Certificate. Visit your learner dashboard to track your progress.
¹Based on Coursera learner outcome survey responses, United States, 2021.
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