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

Sponsored by Coursera Learning Team

4,009 already enrolled

Earn a career credential that demonstrates your expertise
4.8

(159 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
4.8

(159 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

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Professional Certificate - 16 course series

Introduction to Artificial Intelligence (AI)

Course 113 hours4.7 (15,585 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

Generative AI: Introduction and Applications

Course 26 hours4.7 (1,519 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.

Generative AI: Prompt Engineering Basics

Course 37 hours4.8 (2,509 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.

Python for Data Science, AI & Development

Course 425 hours4.6 (39,078 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.

Developing AI Applications with Python and Flask

Course 511 hours4.4 (895 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: Software Testing

Building Generative AI-Powered Applications with Python

Course 613 hours4.8 (83 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.

Data Analysis with Python

Course 715 hours4.7 (18,611 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

Machine Learning with Python

Course 813 hours4.7 (16,540 ratings)

What you'll learn

  • Describe the various types of Machine Learning algorithms and when to use them 

  • Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression 

  • Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees 

  • Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics 

Skills you'll gain

Category: Machine Learning

Introduction to Deep Learning & Neural Networks with Keras

Course 98 hours4.7 (1,657 ratings)

What you'll learn

Skills you'll gain

Category: Algorithms
Category: Artificial Neural Networks
Category: Deep Learning
Category: Human Learning
Category: Machine Learning
Category: Machine Learning Algorithms
Category: Network Model
Category: Applied Machine Learning
Category: Network Architecture
Category: Python Programming

Generative AI and LLMs: Architecture and Data Preparation

Course 105 hours4.6 (78 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.

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.

Generative AI Language Modeling with Transformers

Course 128 hours4.5 (26 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.

Generative AI Engineering and Fine-Tuning Transformers

Course 138 hours4.8 (13 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.

Generative AI Advance Fine-Tuning for LLMs

Course 148 hours4.4 (13 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

Fundamentals of AI Agents Using RAG and LangChain

Course 156 hours4.6 (21 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.

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.

Instructors

IBM Skills Network Team
IBM
58 Courses1,034,194 learners
Sina Nazeri
IBM
2 Courses13,151 learners
Abhishek Gagneja
IBM
5 Courses156,498 learners

Offered by

IBM

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