Machine Learning- Managing Machine Learning Projects with Google Cloud -1

Machine Learning

Managing Machine Learning Projects with Google Cloud

Transform business problems into actionable machine learning use cases! This two-day course teaches you how to evaluate feasibility, maximize impact, and communicate effectively with stakeholders and technical teams

Duration

2 Days

Target Audience

Designed for professionals in technical roles, including business analysts, IT managers, project managers, and product managers, this course helps you bridge business needs with machine learning solutions. VPs and above are encouraged to explore the Data-driven Transformation with Google Cloud course for leadership-level strategies

COURSE AGENDA

Introduction

Differentiate between AI, machine learning (ML), and deep learning.
Understand high-level applications of ML for improving business processes or creating new value.
Start assessing the feasibility of ML use cases for your business.

What is Machine Learning?

Distinguish between supervised and unsupervised ML problem types.
Identify examples of regression, classification, and clustering problem statements.
Learn Google’s standard definition of ML and the key considerations for each component in an ML project.

Employing ML

Explore the end-to-end process for executing an ML project, including key considerations for each phase.
Practice pitching a custom ML problem statement designed to impact your business meaningfully.

Discovering ML Use Cases

Discover common ML opportunities in day-to-day business processes to address operational challenges and enhance outcomes.

How to be Successful at ML

Identify the requirements and strategies businesses need to successfully adopt and implement ML.

Summary

Recap the key concepts, tools, and strategies discussed throughout the course.
Participate in a competition for the best ML use case presentation, judged on creativity, originality, and feasibility.