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IBM Generative AI Engineering Professional Certificate

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IBM

IBM Generative AI Engineering Professional Certificate

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.

IBM Skills Network Team
Sina Nazeri
Abhishek Gagneja

Instructors: IBM Skills Network Team +13 more

47,464 already enrolled

Included with Coursera Plus

Earn a career credential that demonstrates your expertise
4.7

(1,883 reviews)

Beginner level

Recommended experience

6 months
at 6 hours a week
Flexible schedule
Learn at your own pace
Earn a career credential that demonstrates your expertise
4.7

(1,883 reviews)

Beginner level

Recommended experience

6 months
at 6 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • 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.

Skills you'll gain

  • Category: ChatGPT
  • Category: Keras (Neural Network Library)
  • Category: Large Language Modeling
  • Category: PyTorch (Machine Learning Library)
  • Category: Flask (Web Framework)
  • Category: Application Deployment
  • Category: Data Cleansing
  • Category: Supervised Learning
  • Category: Data Transformation
  • Category: Data Import/Export
  • Category: Unsupervised Learning
  • Category: Prompt Engineering

Details to know

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Taught in English

Advance your career with in-demand skills

  • Receive professional-level training from IBM
  • Demonstrate your technical proficiency
  • Earn an employer-recognized certificate from IBM

Professional Certificate - 16 course series

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

Category: Generative AI
Category: Business Ethics
Category: Computer Science
Category: ChatGPT
Category: Natural Language Processing
Category: Robotic Process Automation
Category: Business Intelligence
Category: Data Governance
Category: Risk Mitigation
Category: Content Creation

What you'll learn

  • 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.

Skills you'll gain

Category: Generative AI
Category: ChatGPT
Category: Prompt Engineering
Category: Image Analysis
Category: Large Language Modeling
Category: Content Creation
Category: OpenAI
Category: Program Development
Category: Virtual Environment
Category: Artificial Intelligence

What you'll learn

  • Explain the concept, relevance, and best practices of prompt engineering to guide generative AI models in producing meaningful, accurate outputs.

  • Apply prompt engineering techniques to text prompts, improving the reliability and quality of large language models.

  • Practice prompt engineering techniques and approaches, including interview pattern, chain-of-thought, tree-of-thought, to improve prompt outcomes.

  • Explore commonly used tools for prompt engineering to aid with prompt engineering.

Skills you'll gain

Category: Prompt Engineering
Category: Large Language Modeling
Category: ChatGPT
Category: IBM Cloud

What you'll learn

  • Develop a foundational understanding of Python programming by learning basic syntax, data types, expressions, variables, and string operations.

  • Apply Python programming logic using data structures, conditions and branching, loops, functions, exception handling, objects, and classes.

  • Demonstrate proficiency in using Python libraries such as Pandas and Numpy and developing code using Jupyter Notebooks.

  • Access and extract web-based data by working with REST APIs using requests and performing web scraping with BeautifulSoup.

Skills you'll gain

Category: Data Structures
Category: NumPy
Category: Pandas (Python Package)
Category: Web Scraping
Category: Python Programming
Category: Data Manipulation
Category: Programming Principles
Category: Object Oriented Programming (OOP)
Category: JSON
Category: Data Import/Export
Category: Jupyter
Category: Automation
Category: Application Programming Interface (API)
Category: Scripting
Category: Restful API
Category: Computer Programming
Category: Data Processing
Category: Data Analysis

What you'll learn

  • 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

  • Build and deploy web applications using Flask, including routing, error handling, and CRUD operations.

  • Create and deploy an AI-based application onto a web server using IBM Watson AI Libraries and Flask

Skills you'll gain

Category: Flask (Web Framework)
Category: Restful API
Category: Unit Testing
Category: Python Programming
Category: Debugging
Category: Development Environment
Category: IBM Cloud
Category: Programming Principles
Category: Web Applications
Category: Code Review
Category: Artificial Intelligence
Category: Application Deployment
Category: API Design

What you'll learn

  • Explain the core concepts of generative AI, including large language models, speech technologies, and platforms such as IBM watsonX, and Hugging Face

  • Build generative AI-powered applications and chatbots using LLMs, retrieval-augmented generation(RAG), and foundational Python frameworks

  • Integrate speech-to-text (STT) and text-to-speech (TTS) technologies to enable voice interfaces in generative AI applications

  • Develop web-based AI applications using Python libraries, such as Flask and Gradio, along with basic front-end tools like HTML, CSS, and JavaScript

Skills you'll gain

Category: Large Language Modeling
Category: Flask (Web Framework)
Category: Generative AI
Category: Natural Language Processing
Category: IBM Cloud
Category: Application Development
Category: OpenAI
Category: Prompt Engineering
Category: Python Programming
Category: Web Development
Category: HTML and CSS
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Web Applications
Data Analysis with Python

Data Analysis with Python

Course 716 hours

What you'll learn

  • Construct Python programs to clean and prepare data for analysis by addressing missing values, formatting inconsistencies, normalization, and binning

  • Analyze real-world datasets through exploratory data analysis (EDA) using libraries such as Pandas, NumPy, and SciPy to uncover patterns and insights

  • Apply data operation techniques using dataframes to organize, summarize, and interpret data distributions, correlation analysis, and data pipelines

  • Develop and evaluate regression models using Scikit-learn, and use these models to generate predictions and support data-driven decision-making

Skills you'll gain

Category: Regression Analysis
Category: Scikit Learn (Machine Learning Library)
Category: Data Manipulation
Category: Pandas (Python Package)
Category: Data Analysis
Category: Predictive Modeling
Category: Data Wrangling
Category: Data Pipelines
Category: NumPy
Category: Matplotlib
Category: Exploratory Data Analysis
Category: Statistical Analysis
Category: Python Programming
Category: Feature Engineering
Category: Data Transformation
Category: Data Import/Export
Category: Data Cleansing
Category: Data-Driven Decision-Making
Category: Data Visualization
Machine Learning with Python

Machine Learning with Python

Course 820 hours

What you'll learn

  • Explain key concepts, tools, and roles involved in machine learning, including supervised and unsupervised learning techniques.

  • Apply core machine learning algorithms such as regression, classification, clustering, and dimensionality reduction using Python and scikit-learn.

  • Evaluate model performance using appropriate metrics, validation strategies, and optimization techniques.

  • Build and assess end-to-end machine learning solutions on real-world datasets through hands-on labs, projects, and practical evaluations.

Skills you'll gain

Category: Regression Analysis
Category: Machine Learning
Category: Classification And Regression Tree (CART)
Category: Dimensionality Reduction
Category: Scikit Learn (Machine Learning Library)
Category: Unsupervised Learning
Category: Supervised Learning
Category: Applied Machine Learning
Category: Decision Tree Learning
Category: Feature Engineering
Category: Predictive Modeling
Category: Statistical Modeling

What you'll learn

  • Describe the foundational concepts of deep learning, neurons, and artificial neural networks to solve real-world problems

  • Explain the core concepts and components of neural networks and the challenges of training deep networks

  • Build deep learning models for regression and classification using the Keras library, interpreting model performance metrics effectively.

  • Design advanced architectures, such as CNNs, RNNs, and transformers, for solving specific problems like image classification and language modeling

Skills you'll gain

Category: Deep Learning
Category: Artificial Neural Networks
Category: Keras (Neural Network Library)
Category: Network Architecture
Category: Regression Analysis
Category: Machine Learning Methods
Category: Image Analysis
Category: Network Model
Category: Machine Learning
Category: Computer Vision
Category: Natural Language Processing

What you'll learn

  • 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 applied in natural language processing tasks

  • Implement tokenization to preprocess raw text using NLP libraries like NLTK, spaCy, BertTokenizer, and XLNetTokenizer

  • Create an NLP data loader in PyTorch that handles tokenization, numericalization, and padding for text datasets

Skills you'll gain

Category: Natural Language Processing
Category: Generative AI
Category: Large Language Modeling
Category: PyTorch (Machine Learning Library)
Category: Data Pipelines
Category: Text Mining
Category: Deep Learning
Category: Artificial Neural Networks
Category: Artificial Intelligence

What you'll learn

  • Explain how one-hot encoding, bag-of-words, embeddings, and embedding bags transform text into numerical features for NLP models

  • Implement Word2Vec models using CBOW and Skip-gram architectures to generate contextual word embeddings

  • Develop and train neural network-based language models using statistical N-Grams and feedforward architectures

  • Build sequence-to-sequence models with encoder–decoder RNNs for tasks such as machine translation and sequence transformation

Skills you'll gain

Category: Natural Language Processing
Category: PyTorch (Machine Learning Library)
Category: Artificial Neural Networks
Category: Applied Machine Learning
Category: Statistical Methods
Category: Large Language Modeling
Category: Data Ethics
Category: Feature Engineering
Category: Generative AI
Category: Text Mining
Category: Semantic Web
Category: Deep Learning

What you'll learn

  • Explain the role of attention mechanisms in transformer models for capturing contextual relationships in text

  • Describe the differences in language modeling approaches between decoder-based models like GPT and encoder-based models like BERT

  • Implement key components of transformer models, including positional encoding, attention mechanisms, and masking, using PyTorch

  • Apply transformer-based models for real-world NLP tasks, such as text classification and language translation, using PyTorch and Hugging Face tools

Skills you'll gain

Category: Text Mining
Category: Natural Language Processing
Category: PyTorch (Machine Learning Library)
Category: Generative AI
Category: Applied Machine Learning
Category: Deep Learning
Category: Artificial Neural Networks
Category: Large Language Modeling

What you'll learn

  • Sought-after, job-ready skills businesses need for working with transformer-based LLMs in generative AI engineering

  • How to perform parameter-efficient fine-tuning (PEFT) using methods like LoRA and QLoRA to optimize model training

  • How to use pretrained transformer models for language tasks and fine-tune them for specific downstream applications

  • How to load models, run inference, and train models using the Hugging Face and PyTorch frameworks

Skills you'll gain

Category: PyTorch (Machine Learning Library)
Category: Generative AI
Category: Large Language Modeling
Category: Natural Language Processing
Category: Prompt Engineering
Category: Deep Learning
Category: Performance Tuning

What you'll learn

  • In-demand generative AI engineering skills in fine-tuning LLMs that employers are actively seeking

  • Instruction tuning and reward modeling using Hugging Face, plus understanding LLMs as policies and applying RLHF techniques

  • Direct preference optimization (DPO) with partition function and Hugging Face, including how to define optimal solutions to DPO problems

  • Using proximal policy optimization (PPO) with Hugging Face to build scoring functions and tokenize datasets for fine-tuning

Skills you'll gain

Category: Reinforcement Learning
Category: Natural Language Processing
Category: Large Language Modeling
Category: Generative AI
Category: Performance Tuning
Category: Prompt Engineering
Category: Probability Distribution

What you'll learn

  • In-demand, job-ready skills businesses seek for building AI agents using RAG and LangChain in just 8 hours

  • How tapply the fundamentals of in-context learning and advanced prompt engineering timprove prompt design

  • Key LangChain concepts, including tools, components, chat models, chains, and agents

  • How tbuild AI applications by integrating RAG, PyTorch, Hugging Face, LLMs, and LangChain technologies

Skills you'll gain

Category: Prompt Engineering
Category: Natural Language Processing
Category: OpenAI
Category: Generative AI Agents
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Large Language Modeling
Category: Artificial Intelligence
Category: ChatGPT
Category: Generative AI

What you'll learn

  • Gain practical experience building your own real-world generative AI application to showcase in interviews

  • Create and configure a vector database to store document embeddings and develop a retriever to fetch relevant segments based on user queries

  • Set up a simple Gradio interface for user interaction and build a question-answering bot using LangChain and a large language model (LLM)

Skills you'll gain

Category: Large Language Modeling
Category: Generative AI
Category: Natural Language Processing
Category: Databases
Category: User Interface (UI)

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Instructors

IBM Skills Network Team
IBM Skills Network Team
IBM
84 Courses1,361,126 learners
Sina Nazeri
Sina Nazeri
IBM
2 Courses37,027 learners
Abhishek Gagneja
Abhishek Gagneja
IBM
6 Courses210,624 learners

Offered by

IBM

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