IBM Generative AI Engineering Professional Certificate

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.