Course

Credit Type:
Course
ACE ID:
IBM-0036
Version:
2
Organization:
Coursera Inc.
Location:
Online
Length:
24 weeks (180 hours)
Minimum Passing Score:
70
ACE Credit Recommendation Period:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 3 Generative AI Engineering
Upper-Division Baccalaureate 3 Machine Learning
Upper-Division Baccalaureate 3 Python Programming
Upper-Division Baccalaureate 3 AI Application Development
Upper-Division Baccalaureate 3 AI Agents
Upper-Division Baccalaureate 2 AI Ethics
Description

Objective:

This Professional Certificate is offered through Coursera, an ACE Authorized Instructional Platform.

The program objective is to equip learners with in-demand skills in the rapidly growing field of Generative AI. Through a comprehensive, hands-on learning journey, students will gain an understanding of foundational concepts such as prompt engineering, large language models (LLMs), and neural networks, along with practical skills for building, deploying, and scaling GenAI solutions using open-source tools and IBM technologies.

Throughout the program, learners complete applied labs, projects, and case studies focused on real-world GenAI use cases. They will explore tools such as LangChain, Hugging Face, and IBM watsonx.ai to create chatbots, enhance applications with AI-generated content, and implement ethical considerations in GenAI systems. By the end of the program, students will be able to develop and evaluate generative AI solutions that are secure, responsible, and effective in business contexts.

This certificate is designed for software developers, data scientists, and technology professionals looking to deepen their expertise in Generative AI, as well as for career switchers who want to build a competitive skill set in one of today’s most transformative technologies.

Learning Outcomes:

  • Build and deploy generative AI applications, agents and chatbots using Python libraries like Flask, SciPy and ScikitLearn, Keras, and PyTorch
  • Use key generative AI architectures and NLP models
  • 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

General Topics:

  • Introduction to Artificial Intelligence (AI)
  • AI Concepts, Terminology, and Application Domains
  • Business and Career Transformation Through AI
  • Introduction and Applications of AI
  • Applications and Tools of Generative AI
  • Generative AI: Introduction and Applications
  • Introduction and Capabilities of Generative AI
  • Generative AI: Prompt Engineering Basics
  • Prompt Engineering for Generative AI
  • Prompt Engineering: Techniques and Approaches
  • Python Data Structures
  • Python Programming Fundamentals
  • Working with Data in Python
  • APIs and Data Collection
  • Python Coding Practices and Packaging Concepts
  • Web App Deployment using Flask
  • Image Captioning with Generative AI
  • Create Your Own ChatGPT-like Website
  • Create a Voice Assistant
  • Generative AI-Powered Meeting Assistant
  • Summarize Your Private Data with Generative AI
  • Babel Fish with LLM and STT TTS
  • Data Wrangling
  • Exploratory Data Analysis
  • Importing Data Sets
  • Model Development
  • Model Evaluation and Refinement
  • Building Unsupervised Learning Models
  • Building Supervised Learning Models
  • Evaluating and Validating Machine Learning Models
  • Introduction to Machine Learning
  • Introduction to Neural Networks and Deep Learning
  • Basics of Deep Learning
  • Keras and Deep Learning Libraries
  • Deep Learning Models
  • Different Approaches to Instruction-Tuning
  • Fine-Tuning Causal LLMs with Human Feedback and Direct Preference
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Computer Based Training
  • Practical Exercises

Methods of Assessment:

  • Examinations
  • Other
  • Quizzes
  • Peer review graded projects with rubric
Supplemental Materials
Equivalencies

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