Price: 5.00 USD | Size: 1.2 GB | Duration : 2.5 Hours | 48 Video Lessons | ★★★★★ 4.4
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Applied Machine Learning with BigQuery on Google's Cloud Online Course
What you'll learn
You'll receive an introduction to BigQuery specific to machine learning
You Learn the Basics of the Google Cloud Platform, specific to BigQuery
You'll learn the basics of applied machine learning from a machine learning engineer
Learn how to building your own machine learning models at scale using BigQuery
Requirements
You should have a basic knowledge of SQL
You should have basic knowledge of machine learning
Description
Welcome to Applied Machine Learning with BigQuery on Google's Cloud.
Right now, applied machine learning is one of the most in-demand career fields in the world, and will continue to be for some time. Most of applied machine learning is supervised. That means models are built against existing datasets.
Most real-world machine learning models are built in the cloud or on large on-prem boxes. In the real-world, we don't built models on laptops or on desktop computers.
Google Cloud Platform's BigQuery is a serverless, petabyte-scale data warehouse designed to house structured datasets and enable lightning fast SQL queries. Data scientists and machine learning engineers can easily move their large datasets to BigQuery without having to worry about scale or administration, so you can focus on the tasks that really matter – generating powerful analysis and insights.
In this course, you’ll:
Get an introduction to BigQuery ML.
Get a good introductory grounding in Google Cloud Platform, specific to BigQuery.
Learn the basics of applied machine learning.
Understand the history, architecture and use cases of BigQuery for machine learning engineers.
Learn how to building your own machine learning models at scale using BigQuery.
This is a mid-level course and basic experience with SQL and Python will help you get the most out of this course.
So what are you waiting for? Get hands-on with BigQuery and harness the benefits of GCP's fully managed data warehousing service.
Who this course is for:
If you're interested in learning how to build real-world models at scale, this course is for you
If you want to learn the most used service on GCP, this course is for you
If you want to learn why so many machine learning engineers use BigQuery, this course is for you
Course Content
01.01-introduction
01.02-section introduction
01.03-scaling out instead of up
01.04-googles scaled out revolution
01.05-demo creating an account on googles cloud platform
02.01-section introduction
02.02-bigquery defined
02.03-bigquery stores structured data
02.04-parallel execution
02.05-demo web ui
02.06-what bigquery is not
02.07-bigquery technology stack
02.08-demo navigation basics
03.01-section introduction
03.02-three core careers
03.03-applied machine learning
03.04-the machine learning process
03.05-types of machine learning
03.06-why python is king
03.07-install python on windows
03.08-install python on a mac
03.09-array
03.10-basic jupyter notebook navigation
04.01-section overview
04.02-core machine learning libraries
04.03-demo core machine learning libraries
04.04-sourcing data
04.05-exploratory data analysis
04.06-data cleansing
04.07-demo modeling
05.01-section introduction
05.02-linear regression
05.03-demo linear regression
05.04-classification
05.05-demo classification
05.06-what is an artificial neural network
06.01-section introduction
06.02-datasets and tables
06.03-demo datasets and tables
06.04-demo cloud datalab
06.05-demo modeling the titanic dataset in cloud datalab
06.06-demo modeling the iris dataset on cloud datalab
06.07-demo scale cloud datalab
06.08-bigquery ml
06.09-demo bigquery ml binary logistic regression
06.10-installing the google cloud sdk
06.11-demo gsutil navigation basics
06.12-demo segmenting datasets
Code.zip
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