Duration

Coursera Business Learning

2 days (8 Hours)

Build your subject-matter expertise

This course is part of the AI For Business Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

Module 1

Big Data and Artificial Intelligence

In this module, you will be introduced to Big Data and examine how machine learning is used throughout various business segments. You will also learn how data is analyzed and extracted, and how digital technologies have been used to expand and transform businesses. You will also get a detailed look at data management tools and how they are best implemented and the value of data warehouses. By the end of this module, you will have gained insight into how machine learning can be used as a general-purpose technology, and some best techniques and practices for data mining.

AI for Business Introduction
Course Introduction
Big Data Overview
Big Data Analysis
Data Management Tools
Data Management Infrastructure
Data Analysis: Extracting Intelligence from Big Data
Introduction to Artificial Intelligence
Machine Learning Overview
Reinforcement Learning
A Detailed View of Machine Learning
Assignment

Module 2

Training and Evaluating Machine Learning Algorithms

In this module, you will get an in-depth look at contrasting Machine Learning methods, including logistic regression and neural nets. You will also learn about Deep Learning and its relationship to neural networks and how to best optimize Machine Learning algorithms. Lastly, you will be introduced to loss functions and how to best measure and review errors to maintain the integrity of your algorithms. By the end of this module, you will have learned about Machine Learning methods, the limitations and value of Deep Learning, how best to drive precision and accuracy in algorithms, and how to get the best training data for those algorithms.

Specific Machine Learning Methods: A Deep Dive
Intro to Model Selection
Feature Engineering and Deep Learning Introduction
Deep Learning
How Deep Learning Works
Limitations of Deep Learning
Evaluating ML Performance
Common Loss Functions
Tradeoffs Between Loss Functions
How is Training Data Acquired?
The Over-Fitting Problem
Test Data
Examples of End-to-End Work Flow
Assignment

Module 3

ML Application and Emerging Methods

In this module, you will take a look at Machine Learning within natural language processing and using generative modeling to create new data. You will also focus on AutoML and how to best utilize automated processes to make your algorithms more efficient. You will also review the no-code Machine Learning tool Teachable Machine, which serves to make Deep and Machine Learning more accessible. By the end of this module, you will be able to use AutoML in your algorithms and be able to navigate and use Teachable Machine in practice for no-code solutions to building an algorithm.

Natural Language Processing
GANs and VAEs
Intro to AutoML
Using AutoML
Teachable Machine
TensorFlow Playground
ML Operations
Chicken and Egg
Assignment

Module 4 Industry Interview

In this module, you will hear from an industry leader and gain valuable insight into data sampling and building

realistic usable models. Ed Lee, VP of Global Menu Strategy & Global Marketing at McDonald's, will allow you to review real-world solutions and how they handle data issues as one of the most successful global brands. By the end of this module, you will have heard from a top industry expert in their field and gained firsthand knowledge and understanding of how Big Data plays into maintaining privacy in data and also utilizing that data to enhance your marketing, content, and refine your algorithms.

Interview With Ed Lee
1 peer review

Module 5 Generative AI

In this module, you will explore multiple aspects of generative AI. Not only will you gain an understanding of

how it makes predictions and generates content, but you will also gain an understanding of how large language models work. Diving deeper, you will explore the generative AI stack as well as foundation models and their versatility in performing a broad range of tasks. Reflecting on research studies, you will examine the implications of generative AI on work and productivity, including the potential for both human displacement and enhancement. You will gain insights for crafting instructions to improve the quality of output from large language modules and explore how a company building an application on top of foundation models may gain a competitive advantage.

Generative AI Overview
Implications of Generative AI on Work
Generative AI's Implication on Productivity
The Generative AI Stack
Foundation Models
Prompt Engineering Principles Improving Output Quality
Customizing LLM Output
Differentiation Gaining Competitive Advantage
Assignment

Overview

Artificial Intelligence (AI) is not a new technology, it has been in development for decades. Today, however, AI is in the spotlight as it reshapes the way businesses operate, innovate, and engage with customers.

Generative AI stands at the forefront of this transformation. This course demonstrates the foundations and practices of using Generative AI in Business settings.

This course provides a brief history of AI and moves quickly into how you can use Generative AI to improve your ways of working. We’ll cover content creation as well as using AI in financial forecasting. Finally, we will also touch on the ethical implications of AI as we seek to evaluate the trajectory of AI in our business environment. Upon completing this course, learners will have a foundational understanding of AI, analyze which AI framework is useful for them, and synthesize this knowledge into their daily work. This program is designed for anyone who is interested in examining the origins of AI and how it can be effectively used by businesspeople. Learners should ideally possess the following prerequisite skills: basic critical thinking; open- mindedness; and an active learning mindset.

Learning Outcome

Analyze the historical evolution of AI and Generative AI to promote critical thinking.
Construct strategies for implementing generative AI applications in business environments and determine the best models for specific contexts
Differentiate between various generative AI models and identify their advantages across frameworks.
Illustrate the advantages and benefits of implementing generative AI in practical business environments.

Skills Gain

Restate the evolution of AI.
Analyze the ethical implications of AI in business.
Effectively evaluate AI applications in business.

Module Details

Generative AI stands at the forefront of this transformation. This course demonstrates the foundations and practices of using Generative AI in Business settings. This course provides a brief history of AI and moves

quickly into how you can use Generative AI to improve your ways of working. We’ll cover content creation as well as using AI in financial forecasting. Finally, we will also touch on the ethical implications of AI as we seek to evaluate the trajectory of AI in our business environment.

Modules

Introduction to the Course and the Instructor
What is Artificial Intelligence (AI)?
The Difference between Generative and Discriminative AI
Key Concepts: Neural Networks, Training, and Data
A Brief History of Generative AI
Milestone AI Models
AI in Business Today
Content Creation and Marketing
Product Design and Manufacturing
Financial Management with Generative AI
Boosting Productivity and Efficiency
Data-Driven Decision Making
Cost Reduction
Ethical Implications of Generative AI
Data Quality
Economic Implications

7 readings

Welcome to the Course: Lesson Overview
The Brief History of Artificial Intelligence: What's Next?
[Optional] The Generative AI Lab Playground
Who are the Competitors of ChatGPT? 10 Biggest Market Players
Top 7 Use Cases of Generative AI in Accounting
Top 7 Generative AI Benefits for Business
Silicon Valley & Ethical AI Principles
Assignment
Final Assessment

1 ungraded lab

Access Your GPT GenAI Playground

Programme scope, delivery arrangements and suitability are confirmed during enquiry.

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