Generative-AI-Engineering

IBM Generative AI Engineering Professional Certificate

Professional Certificate - 16 course series 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.

Module 1: Introduction to Artificial Intelligence (AI)

What you'll learn

• Explain the fundamental concepts and applications of AI in various domains.
• Describe the core principles of machine learning, deep learning, and neural networks, and apply them to real-world scenarios.
• Analyze the role of generative AI in transforming business operations, identifying opportunities for innovation and process improvement.
• Design a generative AI solution for an organizational challenge, integrating ethical considerations.

Skills you'll gain

Generative AI Natural Language Processing Robotics Risk Mitigation Responsible AI Business Logic

Module 2: Generative AI – Introduction and Applications

What I Learned

• Differentiated generative AI from discriminative AI
• Explored real-world generative AI use cases across industries
• Identified generative AI models for text, code, image, audio, and video

Skills Gained

• Generative AI
• ChatGPT
• Artificial Intelligence & Machine Learning (AI/ML)
• Deep Learning
• Machine Learning

Module 3: Generative AI – Prompt Engineering Basics

What I Learned

• Fundamentals and importance of prompt engineering
• Best practices for writing effective prompts
• Common prompt patterns and techniques

Skills Gained

• Prompt Engineering
• Prompt Patterns
• Context Management
• AI Workflows
• Decision Making

Module 4: Python for Data Science, AI & Development

What I Learned

• Python fundamentals and programming logic
• Data analysis using Pandas and NumPy
• Web data extraction using APIs and web scraping

Skills Gained

• Python Programming
• Pandas & NumPy
• Data Analysis & Manipulation
• RESTful APIs
• Web Scraping
• Object-Oriented Programming (OOP)

Module 5: Developing AI Applications with Python and Flask

What I Learned

• Python application development lifecycle
• Building REST APIs and web apps using Flask
• Deploying AI-powered applications

Skills Gained

• Flask
• RESTful APIs
• Application Deployment
• Unit Testing
• Server-Side Development

Module 6: Building Generative AI-Powered Applications with Python

What I Learned

• Built chatbots using LLMs and RAG
• Integrated speech-to-text and text-to-speech
• Developed AI web applications using Flask and Gradio

Skills Gained

• Generative AI
• Retrieval-Augmented Generation (RAG)
• LangChain
• Hugging Face
• OpenAI APIs

Module 7: Data Analysis with Python

What I Learned

• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Regression modeling and prediction

Skills Gained

• Exploratory Data Analysis
• Regression Analysis
• Scikit-learn
• Data Visualization
• Statistical Analysis

Module 8: Machine Learning with Python

What I Learned

• Supervised and unsupervised learning techniques
• Model evaluation and optimization
• End-to-end machine learning workflows

Skills Gained

• Machine Learning
• Classification & Regression
• Clustering
• Feature Engineering
• Model Evaluation

Module 9: Introduction to Deep Learning & Neural Networks

What I Learned

• Fundamentals of neural networks
• Built CNNs, RNNs, and transformers
• Evaluated deep learning models

Skills Gained

• Deep Learning
• Keras
• Neural Networks
• Transfer Learning

Module 10: Generative AI & LLM Architecture and Data Preparation

What I Learned

• Generative AI architectures (Transformers, GANs, VAEs)
• Tokenization and NLP preprocessing
• Building NLP data pipelines with PyTorch

Skills Gained

• PyTorch
• Large Language Models (LLMs)
• Tokenization
• NLP Data Pipelines

Module 11: Foundational Models for NLP & Language Understanding

What I Learned

• Text representation techniques and embeddings
• Word2Vec and sequence models
• Ethical considerations in NLP

Skills Gained

• Natural Language Processing (NLP)
• Embeddings
• Feature Engineering
• Model Evaluation

Module 12: Generative AI Language Modeling with Transformers

What I Learned

• Attention mechanisms and transformer architecture
• GPT vs BERT modeling approaches
• Transformer-based NLP applications

Skills Gained

• Transformers
• Hugging Face
• Transfer Learning
• Text Mining

Module 13: Generative AI Engineering & Fine-Tuning Transformers

What I Learned

• Fine-tuned LLMs using LoRA and QLoRA
• Ran inference and training using Hugging Face
• Optimized model performance

Skills Gained

• LLM Fine-Tuning
• Parameter-Efficient Fine-Tuning (PEFT)
• Performance Optimization

Module 14: Advanced Fine-Tuning for LLMs

What I Learned

• Instruction tuning and reward modeling
• Applied RLHF, PPO, and DPO techniques
• Evaluated fine-tuned LLMs

Skills Gained

• Reinforcement Learning
• RLHF
• PPO & DPO
• Model Evaluation

Module 15: Fundamentals of AI Agents Using RAG and LangChain

What I Learned

• Built AI agents using LangChain
• Applied advanced prompt engineering
• Integrated RAG into LLM workflows

Skills Gained

• AI Agents
• LangChain
• Retrieval-Augmented Generation (RAG)

Module 16: Project – Generative AI Applications with RAG and LangChain

What I Learned

• Built an end-to-end generative AI application
• Implemented vector databases for document retrieval
• Developed a QA chatbot using Gradio and LangChain

Skills Gained

• Vector Databases
• Embeddings
• LLM Applications
• Gradio
• End-to-End RAG Systems