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Managing Machine Learning Projects with Google Cloud
  • Level Foundation
  • المدة 13 ساعات hours
  • الطبع بواسطة Google Cloud
  • Offered by

عن

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.

الوحدات

Module 1: Welcome to the course

    • 1 Videos
    • 2 Readings
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1 Videos

  • Introduction

2 Readings

  • How to download course resources
  • How to send feedback

Module 2: Introduction

    • 1 Videos
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1 Videos

  • Introduction

Understanding ML with examples

    • 1 Videos
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1 Videos

  • AI vs ML vs Deep Learning

Module 2: Machine learning projects

    • 2 Videos
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2 Videos

  • Phase 1: Assess feasibility
  • Practice assessing the feasibility of ML use cases

Assignments

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Identifying business value for using ML

1 Readings

  • Worksheet

Module 3: Introduction

    • 1 Videos
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1 Videos

  • Common ML problem types

Machine Learning Defined

    • 3 Videos
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3 Videos

  • Standard algorithm and data
  • Data quality
  • Predictive insights and decisions

Real-life Use Cases

    • 5 Videos
    • 1 Readings
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5 Videos

  • 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

1 Readings

  • Module 3: Worksheet

Assessment

    • 1 Assignment
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1 Assignment

  • Defining ML as a practice

Module 4: Introduction

    • 1 Videos
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1 Videos

  • Features and labels

Tools to label datasets

    • 1 Videos
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1 Videos

  • Building labeled datasets

Guidelines for training ML models

    • 2 Videos
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2 Videos

  • Training an ML model
  • General best practices

Getting started with Qwiklabs

    • 2 Videos
    • 1 ExternalTool
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1 External Tool

  • Identifying damaged car parts with AutoML Vision

2 Videos

  • Introduction to hands-on labs
  • Lab 1: Review

Assessment

    • 1 Assignment
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1 Assignment

  • Building and evaluating ML models

Module 5: Introduction

    • 1 Videos
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1 Videos

  • Human bias in ML

Guiding Principles

    • 2 Videos
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2 Videos

  • Google's AI Principles
  • Common types of human bias

ML fairness

    • 1 Videos
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1 Videos

  • Evaluating model fairness

Hands-on Lab 2

    • 2 Videos
    • 1 ExternalTool
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1 External Tool

  • Inspecting a dataset for bias using TensorFlow Data Validation and Facets

2 Videos

  • Guidelines and Hands-on Lab
  • Lab 2: Review

Assessment

    • 1 Assignment
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1 Assignment

  • Using ML responsibly and ethically

Module 6: Introduction

    • 2 Videos
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2 Videos

  • Replacing rule-based systems with ML
  • Automate processes and understand unstructured data

Beyond the basics

    • 2 Videos
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2 Videos

  • Personalize applications with ML
  • Creative uses of ML

Worksheet and Hands-on Lab 3

    • 2 Videos
    • 1 Readings
    • 1 ExternalTool
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1 External Tool

  • Sentiment Analysis with Natural Language API

2 Videos

  • Sentiment analysis and Hands-on Lab
  • Lab 3: Review

1 Readings

  • Sentiment Analysis Worksheet

Assessment

    • 1 Assignment
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1 Assignment

  • Discovering ML use cases in day-to-day business

Module 7: Introduction

    • 1 Videos
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1 Videos

  • Key consideration 1: business value

Key consideration 2

    • 2 Videos
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2 Videos

  • Data strategy (pillars 1–3)
  • Data strategy (pillars 4–7)

Key consideration 3 and 4

    • 2 Videos
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2 Videos

  • Data governance
  • Build successful ML teams

Key consideration 5 and Hands-on Lab

    • 2 Videos
    • 1 ExternalTool
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1 External Tool

  • Evaluate an ML model with BigQuery ML

2 Videos

  • Create a culture of innovation and Hands-on Lab
  • Lab 4: Review

Assessment

    • 1 Assignment
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1 Assignment

  • Managing ML projects successfully

Module 8: Course Summary

    • 1 Videos
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1 Videos

  • Summary

Auto Summary

"Managing Machine Learning Projects with Google Cloud" is a foundational course designed for business professionals in non-technical roles. Offered by Coursera, it helps learners understand and lead machine learning projects by translating business problems into feasible ML use cases. The course spans 780 minutes and is available through Starter, Professional, and Paid subscriptions. Gain confidence in proposing ML solutions and collaborating effectively with technical teams. Perfect for those looking to influence ML initiatives without deep technical knowledge.

Google Cloud Training
Instructor

Google Cloud Training