Machine Learning with Remote Sensing in Google Earth Engine

Machine Learning with Remote Sensing in Google Earth Engine, available at $44.99, has an average rating of 3.6, with 14 lectures, based on 82 reviews, and has 385 subscribers.

You will learn about Learn to learn applying machine learning algorithms using satellite data Learn processing analyzing large volume of remotely sensed satellite data with the Earth Engine API Learn to collect reference training data for image classification Learn to remove clouds from Landsat imageries Learn to calculate multi-spectral indices with satellite bands Learn to assess the accuracy of classification This course is ideal for individuals who are Anyone who want to understand the application of various machine learning techniques using satellite data. or Anyone who wants to learn big Earth observation data analysis on the cloud. or Anyone who wants to start a career in spatial data science. It is particularly useful for Anyone who want to understand the application of various machine learning techniques using satellite data. or Anyone who wants to learn big Earth observation data analysis on the cloud. or Anyone who wants to start a career in spatial data science.

Enroll now: Machine Learning with Remote Sensing in Google Earth Engine

Summary

Title: Machine Learning with Remote Sensing in Google Earth Engine

Price: $44.99

Average Rating: 3.6

Number of Lectures: 14

Number of Published Lectures: 14

Number of Curriculum Items: 14

Number of Published Curriculum Objects: 14

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Learn to learn applying machine learning algorithms using satellite data

Learn processing analyzing large volume of remotely sensed satellite data with the Earth Engine API

Learn to collect reference training data for image classification

Learn to remove clouds from Landsat imageries

Learn to calculate multi-spectral indices with satellite bands

Learn to assess the accuracy of classification

Who Should Attend

Anyone who want to understand the application of various machine learning techniques using satellite data.

Anyone who wants to learn big Earth observation data analysis on the cloud.

Anyone who wants to start a career in spatial data science.

Target Audiences

Anyone who want to understand the application of various machine learning techniques using satellite data.

Anyone who wants to learn big Earth observation data analysis on the cloud.

Anyone who wants to start a career in spatial data science.

Do you want to learn how to access, process, and analyze remote sensing data using open-source cloud-based platforms?

Do you want to master machine learning algorithms to predict Earth Observation big data?

Do you want to start a spatial data scientist career in the geospatial industry?

 

Enroll in my new course to masterMachine Learning with Remote Sensing in Google Earth Engine.

 

I will provide you with hands-on training with example data, sample scripts, and real-world applications.  

By taking this course, you will take your geospatial data science skills to the next level by gaining proficiency in applying machine learning algorithms to predict satellite data using an open-source big data analytics tool, Earth Engine API, a cloud-based Earth observation data visualization analysis by powered by Google.

In this Machine Learning with Earth Engine APIcourse, I will help you get up and running on the Google Earth Engine cloud platform. Then you will apply various machine learning algorithms including linear regression, clustering, CART, and random forests. We will use Landsat satellite data to predict land use land cover classification. All sample data and scripts will be provided to you as an added bonus throughout the course.

 

Jump in right now to enroll. To get started click the enroll button.

Course Curriculum

Chapter 1: Get Started with Earth Engine API

Lecture 1: Welcome

Lecture 2: Introduction to Earth Engine

Lecture 3: Explore Earth Engine

Lecture 4: Sign Up

Chapter 2: Code Editor

Lecture 1: Code Editor

Lecture 2: JavaScript Syntax

Chapter 3: Linear Regression

Lecture 1: Linear Regression

Lecture 2: Assignment: Linear Regression

Chapter 4: Clustering

Lecture 1: Clustering

Chapter 5: Training Data

Lecture 1: Training Data

Chapter 6: CART

Lecture 1: CART

Chapter 7: Random Forests

Lecture 1: Random Forests

Chapter 8: Final Project

Lecture 1: Final Project

Chapter 9: Bonus Lectures

Lecture 1: Bonus

Instructors

Spatial eLearning
Learn Geospatial Data Science Skills!

Dr. Alemayehu Midekisa
Geospatial Data Scientist

Rating Distribution

1 stars: 4 votes

2 stars: 6 votes

3 stars: 15 votes

4 stars: 28 votes

5 stars: 29 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

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