Managing Machine Learning Projects with Google Cloud

Overview

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.

Syllabus

Introduction

Introduction

Identifying business value for using ML

Introduction
AI vs ML vs Deep Learning
Phase 1: Assess feasibility
Practice assessing the feasibility of ML use cases
Module 2: Graded Quiz
Module 2: Worksheet

Defining ML as a practice

Common ML problem types
Standard algorithm and data
Data quality
Predictive insights and decisions
More ML examples
Practice series: Analyze the ML use case
Module 3: Worksheet
Saving the world's bees
Google Assistant for accessibility
Exercise review and Why ML now
Module 3: Graded Quiz

Building and evaluating ML models

Features and labels
Building labeled datasets
Training an ML model
General best practices
Identifying Damaged Car Parts with Vertex AutoML Vision
Lab 1: Review
Module 4: Graded Quiz

Using ML responsibly and ethically

Human bias in ML
Google's AI Principles
Common types of human bias
Evaluating model fairness
Guidelines and Hands-on Lab
Inspecting a Dataset for Bias using TensorFlow Data Validation and Facets Overview
Lab 2: Review
Module 5: Graded Quiz