Ementorhub

What you'll learn

Explore common machine learning use cases implemented by businesses.

Assess the feasibility of your own ML use case and its ability to meaningfully impact your business.

Identify the requirements to build, train, and evaluate an ML model.

Define data characteristics and biases that affect the quality of ML models and recognize key considerations for managing ML projects.

Skills you'll gain

Business Analysis

Data Governance

Machine Learning

Business Ethics

Feasibility Studies

Project Management Life Cycle

Google Cloud Platform

IT Management

Innovation

Data Strategy

Data Ethics

Artificial Intelligence and Machine Learning (AI/ML)

Applied Machine Learning

Technical Management

There are 8 modules in this course

Business professionals in non-technical roles have a unique opportunity to lead or influence machine learning projects. If you have questions about machine learning and want to understand how to use it, without the technical jargon, this course is for you. Learn how to translate business problems into machine learning use cases and vet them for feasibility and impact. Find out how you can discover unexpected use cases, recognize the phases of an ML project and considerations within each, and gain confidence to propose a custom ML use case to your team or leadership or translate the requirements to a technical team.

Module 2: Identifying business value for using ML

Module 3: Defining ML as a practice

Module 4: Building and evaluating ML models

Module 5: Using ML responsibly and ethically

Module 6: Discovering ML use cases in day-to-day business

Module 7: Managing ML projects successfully

Module 8: Summary