IBM
IBM Generative AI Engineering Professional Certificate
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

Access provided by Coursera Learning Team

17,692 already enrolled

Earn a career credential that demonstrates your expertise
4.7

(858 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

(858 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.

Details to know

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Taught in English
Recently updated!

November 2024

Advance your career with in-demand skills

  • Receive professional-level training from IBM
  • Demonstrate your technical proficiency
  • Earn an employer-recognized certificate from IBM
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Professional Certificate - 16 course series

Introduction to Artificial Intelligence (AI)

Course 112 hours4.7 (17,112 ratings)

What you'll learn

  • 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

Skills you'll gain

Category: Artificial Intelligence
Category: Deep Learning
Category: Generative AI
Category: Machine Learning
Category: Artificial Neural Networks
Category: Natural Language Processing
Category: Emerging Technologies
Category: Ethical Standards And Conduct
Category: Governance
Category: OpenAI
Category: Data Ethics
Category: Computer Vision
Category: ChatGPT
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Business Transformation

Generative AI: Introduction and Applications

Course 27 hours4.7 (1,996 ratings)

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: Deep Learning
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Artificial Intelligence
Category: IBM Cloud
Category: Augmented and Virtual Reality (AR/VR)
Category: OpenAI

Generative AI: Prompt Engineering Basics

Course 37 hours4.8 (3,060 ratings)

What you'll learn

  • 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.

Skills you'll gain

Category: Generative AI
Category: Image Analysis
Category: IBM Cloud
Category: Software Development Tools
Category: ChatGPT
Category: Technical Communication
Category: Natural Language Processing

Python for Data Science, AI & Development

Course 425 hours4.6 (40,166 ratings)

What you'll learn

  • 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.

Skills you'll gain

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

Developing AI Applications with Python and Flask

Course 511 hours4.4 (1,001 ratings)

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

  • 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

Skills you'll gain

Category: Python Programming
Category: Restful API
Category: Unit Testing
Category: Application Development
Category: Flask (Web Framework)
Category: Application Programming Interface (API)
Category: Application Deployment
Category: Web Applications
Category: Style Guides
Category: Natural Language Processing
Category: Artificial Intelligence
Category: Programming Principles
Category: IBM Cloud

Building Generative AI-Powered Applications with Python

Course 613 hours4.7 (126 ratings)

What you'll learn

  • 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.

Skills you'll gain

Category: Generative AI
Category: Natural Language Processing
Category: IBM Cloud
Category: OpenAI
Category: Serverless Computing
Category: Web Applications
Category: Front-End Web Development
Category: Back-End Web Development
Category: Application Development
Category: Cloud Applications
Category: Artificial Intelligence
Category: Python Programming

Data Analysis with Python

Course 715 hours4.7 (18,866 ratings)

What you'll learn

  • 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

Skills you'll gain

Category: Regression Analysis
Category: Scikit Learn (Machine Learning Library)
Category: NumPy
Category: Predictive Modeling
Category: Pandas (Python Package)
Category: Data Manipulation
Category: Data Cleansing
Category: Data Pipelines
Category: Exploratory Data Analysis
Category: Data Wrangling
Category: Data Transformation
Category: Data Analysis
Category: Python Programming
Category: Feature Engineering
Category: Predictive Analytics
Category: Statistical Analysis
Category: Data Import/Export
Category: Statistical Modeling
Category: Machine Learning Methods

Machine Learning with Python

Course 820 hours4.7 (16,949 ratings)

What you'll learn

  • 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.

Skills you'll gain

Category: Machine Learning
Category: Regression Analysis
Category: Supervised Learning
Category: Dimensionality Reduction
Category: Unsupervised Learning
Category: Machine Learning Algorithms
Category: Scikit Learn (Machine Learning Library)
Category: Classification And Regression Tree (CART)
Category: Data Manipulation
Category: Jupyter
Category: Applied Machine Learning
Category: Predictive Modeling
Category: Random Forest Algorithm
Category: Statistical Modeling
Category: Statistical Machine Learning
Category: Feature Engineering
Category: Python Programming
Category: Matplotlib

Introduction to Deep Learning & Neural Networks with Keras

Course 99 hours4.7 (1,731 ratings)

What you'll learn

Skills you'll gain

Category: Deep Learning
Category: Artificial Neural Networks
Category: Keras (Neural Network Library)
Category: Unsupervised Learning
Category: PyTorch (Machine Learning Library)
Category: Tensorflow
Category: Machine Learning
Category: Supervised Learning
Category: Regression Analysis
Category: Artificial Intelligence and Machine Learning (AI/ML)

Generative AI and LLMs: Architecture and Data Preparation

Course 105 hours4.7 (151 ratings)

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 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.

Skills you'll gain

Category: Natural Language Processing
Category: PyTorch (Machine Learning Library)
Category: Data Processing
Category: Generative AI
Category: Jupyter
Category: Machine Learning
Category: Artificial Neural Networks
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Deep Learning

What you'll learn

  • 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.

Skills you'll gain

Category: Natural Language Processing
Category: Deep Learning
Category: Artificial Neural Networks
Category: Generative AI
Category: PyTorch (Machine Learning Library)
Category: Machine Learning Methods
Category: Text Mining
Category: Feature Engineering

Generative AI Language Modeling with Transformers

Course 128 hours4.6 (62 ratings)

What you'll learn

  • 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.

Skills you'll gain

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

Generative AI Engineering and Fine-Tuning Transformers

Course 138 hours4.4 (36 ratings)

What you'll learn

  • 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.

Skills you'll gain

Category: PyTorch (Machine Learning Library)
Category: Generative AI
Category: Natural Language Processing
Category: Applied Machine Learning
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Deep Learning
Category: Application Frameworks
Category: Performance Tuning

Generative AI Advance Fine-Tuning for LLMs

Course 148 hours4.3 (60 ratings)

What you'll learn

  • 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

Skills you'll gain

Category: Generative AI
Category: Reinforcement Learning
Category: Machine Learning
Category: OpenAI
Category: Artificial Intelligence
Category: Natural Language Processing
Category: Tensorflow
Category: PyTorch (Machine Learning Library)
Category: Performance Tuning
Category: ChatGPT

Fundamentals of AI Agents Using RAG and LangChain

Course 156 hours4.7 (55 ratings)

What you'll learn

  • 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.

Skills you'll gain

Category: Natural Language Processing
Category: Application Frameworks
Category: Artificial Intelligence
Category: Open Source Technology
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Generative AI
Category: Application Development

What you'll learn

  • 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.

Skills you'll gain

Category: Generative AI
Category: Artificial Intelligence
Category: Application Development
Category: Unstructured Data
Category: Document Management
Category: User Interface (UI)
Category: Data Storage
Category: Databases
Category: Natural Language Processing

Instructors

IBM Skills Network Team
IBM
64 Courses1,179,461 learners
Sina Nazeri
IBM
2 Courses22,193 learners
Abhishek Gagneja
IBM
6 Courses177,585 learners

Offered by

IBM

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