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Google Cloud Big Data and Machine Learning Fundamentals

Approx. 12 hours to complete

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Course Summary

This course covers the fundamentals of big data and machine learning on Google Cloud Platform. It is designed for those who want to learn how to use Google Cloud Platform to build their own big data solutions.

Key Learning Points

Learn how to use Google Cloud Platform to build big data solutions
Understand the fundamentals of machine learning
Get hands-on experience with Google Cloud Platform tools

Job Positions & Salaries of people who have taken this course might have

- USA: $130,000
- India: ₹1,500,000
- Spain: €45,000
- USA: $130,000
- India: ₹1,500,000
- Spain: €45,000
- USA: $140,000
- India: ₹2,000,000
- Spain: €50,000
- USA: $130,000
- India: ₹1,500,000
- Spain: €45,000
- USA: $140,000
- India: ₹2,000,000
- Spain: €50,000
- USA: $85,000
- India: ₹800,000
- Spain: €30,000

Learning Outcomes

Understand the fundamentals of big data and machine learning
Be able to use Google Cloud Platform to build big data solutions
Gain hands-on experience with Google Cloud Platform tools

Prerequisites or good to have knowledge before taking this course

Basic programming knowledge in Python
Familiarity with the command line interface

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Hands-on labs and activities
Video lectures and quizzes

Similar Courses

Data Engineering, Big Data, and Machine Learning on GCP
Data Science on Google Cloud Platform

Related Education Paths

Notable People in This Field

CEO of Google
Co-founder of Coursera

Related Books

Description

This course introduces participants to the big data capabilities of Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants get an overview of Google Cloud and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud.

Knowledge

Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud.
Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.
Employ BigQuery to carry out interactive data analysis.
Choose between different data processing products on Google Cloud.

Outline

Introduction to the Data and Machine Learning on Google Cloud Course
Welcome to Big Data and Machine Learning Fundamentals
Introduction to Google Cloud Platform
Compute Power for Analytic and ML Workloads
Demo: Creating a VM on Compute Engine
Elastic Storage with Google Cloud Storage
Build on Google's Global Network
Security: On-premise vs Cloud-native
Evolution of Google Cloud Big Data Tools
Getting Started with Google Cloud and Qwiklabs
Choosing the right approach
What you can do with Google Cloud Platform
Activity: Explore real customer solution architectures
Key roles in a data-driven organization
Google Cloud Public Datasets program
Module Resources
Module Review

Recommending Products using Cloud SQL and Spark
How businesses use recommendation systems
Introduction to machine learning
Challenge: ML for recommending housing rentals
Approach: Move from on-premise to Google Cloud Platform
Demo: From zero to an Apache Spark job in 10 minutes or less
Challenge: Utilizing and tuning on-premise clusters
Move storage off-cluster with Google Cloud Storage
Lab Intro
Module Resources
Module Review

Predict Visitor Purchases Using BigQuery ML
Introduction to BigQuery
Demo: Query 2 billion lines of Github code in less than 30 seconds
BigQuery: Fast SQL Engine
Demo: Exploring bike share data with SQL
Data quality
BigQuery managed storage
Insights from geographic data
Demo: Analyzing lightning strikes with BigQuery GIS
Choosing a ML model type for structured data
Predicting customer lifetime value
BigQuery ML: Create models with SQL
Phases in ML model lifecycle
BigQuery ML: key features walkthrough
Lab Intro
Module Resources
Module Review

Real-time IoT Dashboards with Pub/Sub, Dataflow, and Data Studio
Modern data pipeline challenges
Message-oriented architectures with Cloud Pub/Sub
Designing streaming pipelines with Apache Beam
Implementing streaming pipelines on Cloud Dataflow
Visualizing insights with Data Studio
Creating charts with Data Studio
Demo: Data Studio walkthrough
Lab Intro
Module Resources
Module Review

Deriving Insights from Unstructured Data using Machine Learning
Where is unstructured ML used in business?
How does ML on unstructured data work?
Demo: ML built into Google Photos
Comparing approaches to ML
Demo: Using ML building blocks
Using pre-built AI to create a chatbot
Customizing Pre-built models with AutoML
Lab Intro
Building a Custom Model
Demo: Text classification done three ways
Additional resources to build custom models
Module Resources

Summary
Course Summary

Summary of User Reviews

Coursera's GCP Big Data & ML Fundamentals course has received positive reviews from students. The course covers various aspects of big data and machine learning on Google Cloud Platform. Students have appreciated the practical approach of the course.

Key Aspect Users Liked About This Course

Practical approach of the course

Pros from User Reviews

Hands-on experience with Google Cloud Platform tools
Clear explanation of big data and machine learning concepts
Real-world examples and case studies
Great instructor engagement and support
Flexible learning schedule

Cons from User Reviews

Some students found the course to be too basic
Course materials could be more organized
The course could be more challenging for advanced learners
Some technical issues with the platform
Course could benefit from more interactive elements

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