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How Google does Machine Learning

Approx. 8 hours to complete

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Course Summary

This course is designed to teach you how to build machine learning models using Google Cloud Platform. You will learn how to use TensorFlow, Cloud ML Engine, and other tools to create and train machine learning models.

Key Learning Points

Learn how to build machine learning models using Google Cloud Platform
Use TensorFlow and Cloud ML Engine to create and train machine learning models
Gain hands-on experience through exercises and projects

Learning Outcomes

Understand the fundamentals of machine learning and how to apply it to real-world problems
Learn how to use Google Cloud Platform tools to build and train machine learning models
Gain hands-on experience through exercises and projects

Prerequisites or good to have knowledge before taking this course

Some programming experience in Python
Familiarity with basic concepts of machine learning

Course Difficulty Level

Intermediate

Course Format

Online self-paced
Video lectures and hands-on exercises
Projects

Similar Courses

Machine Learning with TensorFlow on Google Cloud Platform
Applied Data Science with Python
Data Engineering on Google Cloud Platform

Related Education Paths

Notable People in This Field

Co-founder, Coursera
Senior Fellow, Google

Related Books

Description

What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models.

Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this. >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<

Knowledge

Frame a business use case as a machine learning problem.
Gain a broad perspective of machine learning and where it can be used
Convert a candidate use case to be driven by machine learning
Recognize biases that machine learning can amplify.

Outline

Introduction to Course
Introduction to Specialization
Why Google?
Why Google Cloud?
Latest from Google
Introduction to ML on Google Cloud

What it means to be AI first
What it means to be AI first
Two stages of ML
ML in Google Products
ML in Google Photos
Google Translate and Gmail
Replacing Heuristic Rules
Pre-trained models
Machine Learning with Sara Robinson (ML, not rules)
Vision API in action
Video intelligence API
Cloud Speech-to-Text API
Translation and NL4
Text-to-Speech
DialogFlow
Lab Intro: Pretrained ML APIs Intro
Lab Solution Invoking Machine Learning APIs
It's all about data
A data strategy
Training and serving skew
ML Training Phases
Lab Intro - Framing an ML Problem
Lab debrief
Demo: ML in applications
What it Means to be AI First
Introduction to AI First
Pre-trained ML APIs
All about data

How Google does ML
An ML strategy
Transform your business
Introduction
ML Surprise
The secret sauce
ML and Business Processes
End of phases deep dive
How Google does ML
Transform your business
How Google does ML

Inclusive ML
Machine Learning and Human Bias
Evaluating Metrics for Inclusion
Statistical Measurements and acceptable tradeoffs
Equality of Opportunity
Simulating Decisions
Finding Errors in your dataset using Facets
Inclusive ML
Inclusive ML

Python Notebooks in the cloud
Module Introduction
AI Platform Notebooks
Demo AI Platform Notebooks
Development process
Computation and storage
Lab Intro: Analyzing data using AI Platform Notebooks and BigQuery
Lab Debrief Analyzing Data using AI Platform Notebooks and BigQuery
AI Platform Notebooks
Python Notebooks in the Cloud
Python Notebooks in the Cloud

Summary
Summary
Resources - Compiled as PDF
All Quiz Questions on PDF
Course Slides

Summary of User Reviews

Discover Google's approach to Machine Learning with Coursera's online course. Users have rated this course highly for its comprehensive content, practical exercises, and engaging lectures. One key aspect that many users appreciated was the hands-on experience provided through the use of TensorFlow, a popular ML framework.

Pros from User Reviews

Comprehensive course content
Engaging lectures
Practical exercises
Hands-on experience with TensorFlow

Cons from User Reviews

Requires prior knowledge of programming and statistics
Some users found the pace too slow
Lack of personalized feedback
Limited interaction with instructors
Not suitable for advanced learners

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