Managing Machine Learning Projects with Google Cloud 

Introduction

How to download course resources

How to send feedback

Course Slides

Introduction

AI vs ML vs Deep Learning

Phase 1: Assess feasibility

Practice assessing the feasibility of ML use cases

Worksheet

Identifying business value for using ML

Common ML problem types

Standard algorithm and data

Data quality

Predictive insights and decisions

More ML examples

Practice series: Analyze the ML use case

Saving the world's bees

Google Assistant for accessibility

Exercise review and Why ML now

Module 3: Worksheet

Defining ML as a practice

Features and labels

Building labeled datasets

Training an ML model

General best practices

Introduction to hands-on labs

Lab 1: Review

Building and evaluating ML models

Human bias in ML

Google's AI Principles

Common types of human bias

Evaluating model fairness

Guidelines and Hands-on Lab

Lab 2: Review

Using ML responsibly and ethically

Replacing rule-based systems with ML

Automate processes and understand unstructured data

Personalize applications with ML

Creative uses of ML

Sentiment analysis and Hands-on Lab

Lab 3: Review

Sentiment Analysis Worksheet

Discovering ML use cases in day-to-day business

Key consideration 1: business value

Data strategy (pillars 1?3)

Data strategy (pillars 4?7)

Data governance

Build successful ML teams

Create a culture of innovation and Hands-on Lab

Lab 4: Review

Managing ML projects successfully

Summary