Cluster Analysis & Unsupervised Machine Learning in R
What you'll learn
- Your complete guide to unsupervised learning and clustering using R-programming language
- It covers both theoretical background of UNSUPERVISED MACHINE LERANING as well as practical examples in R and R-Studio
- Fully understand the basics of Machine Learning, Cluster Analysis & Unsupervised Machine Learning
- Highly practical data science examples related to unsupervised machine learning and clustering
- Be Able To Harness The Power Of R For Practical Data Science
- You will have a glimpse on the power of cloud computimg with Google services (i.e. Earth Engine)
- It covers a real-world application of K-means clustering for mapping tasks in UAE
- Improve your R-programming and JavaScript coding skills
- Implement Unsupervised Clustering Techniques Such As k-means Clustering and Hierarchical Clustering
- Apply your newly learned skills to your independent project
- Evaluate Model Performance & Learn The Best Practices For Evaluating Machine Learning Model Accuracy
- Learn R-programming from scratch: R crash course is included that you could start R-programming for machine learning
Course content
- Preview02:33
- 09:27What is Machine Leraning and it's main types?
- 01:40Overview of Machine Leraning in R
Requirements
- Availabiliy computer and internet
- R-programming skills is NOT a requirement, but would be a plus
Description
HERE IS WHY YOU SHOULD TAKE THIS COURSE:
This course will be your complete guide to unsupervised learning and clustering using R-programming language and JavaScript.
Unlike other courses, it offers NOT ONLY the guided demonstrations of the R-scripts but also covers theoretical background that will allow you to FULLY UNDERSTAND & APPLY UNSUPERVISED MACHINE LEARNING (K-means, Hierarchical clustering) in R.
This course also covers all the main aspects of practical and highly applied data science related to unsupervised machine learning and clustering techniques. Thus, if you take this course, you will save lots of time & money on other expensive materials in the R based data science domain.
In this age of big data, companies across the globe use R and Google Cloud Computing Services to analyze big volumes of data for business and research. By becoming proficient in unsupervised learning in R, you can give your company a competitive edge and boost your career to the next level. In addition, you will have a chance to test the power of cloud computing with Google services (i.e. Earth Engine) for a real-world application of unsupervised K-means learning for mapping applications.
THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF UNSUPERVISED MACHINE LEARNING: THEORY & PRACTISE
- Fully understand the basics of Machine Learning, Cluster Analysis & Unsupervised Machine Learning from theory to practice
- Harness applications of unsupervised learning (cluster analysis) in R and with Google Cloud Services
- Machine Learning, Supervised Learning, Unsupervised Learning in R
- Complete two independent projects on Unsupervised Machine Learning in R and using Google Cloud Services
- Implement Unsupervised Clustering Techniques (k-means Clustering and Hierarchical Clustering etc)
- and MORE
NO PRIOR R OR STATISTICS/MACHINE LEARNING / R KNOWLEDGE REQUIRED:
You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
My course will help you implement the methods using real data obtained from different sources, including implementing a real-life project on the cloud computing platform of Google. Thus, after completing my unsupervised data clustering course in R, you’ll easily use different data streams and data science packages to work with real data in R.
I will also provide you with the all scripts and data used in the course.
In case it is your first encounter with R, don’t worry, my course a full introduction to the R & R-programming in this course.
This course is different from other training resources. Each lecture seeks to enhance your data science and clustering skills (K-means, Hierarchical clustering, weighted-K means, Heat mapping, etc) in a demonstrable and easy-to-follow manner and provide you with practically implementable solutions. You’ll be able to start analyzing different streams of data for your projects and gain appreciation from your future employers with your improved machine learning skills and knowledge of the cutting edge data science methods.
The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning, and R in their field.
One important part of the course is the practical exercises. You will be given some precise instructions and datasets to run Machine Learning algorithms using the R and Google Cloud Computing tools.
JOIN MY COURSE NOW!
Who this course is for:
- The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning and R in their field.
- Everyone who would like to learn Data Science Applications In The R & R Studio Environment
- Everyone who would like to learn theory and implementation of Unsupervised Learning On Real-World Data
Instructors
![Kate Alison](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.3 Instructor Rating
- 805 Reviews
- 25,991 Students
- 20 Courses
I am a passionate data scientist, Earth Observation (EO), and GIS expert and educator. I received my M.Sc. in Earth Observation and applied data science from the University of Southampton (United Kingdom) and I also hold a Ph.D. Degree in EO from Germany. I do regular teaching and training all over the world as well as do regularly consultancies on the mentioned topic. I have thousands of satisfied clients all over the world! And now I will be glad if I can teach also you these interesting, highly applied, and exciting topics!
For GIS & Remote Sensing students:
If you would like to learn comprehensively geospatial data analysis, here is a preferred order for how to take my courses:
Option 1: Take all individual courses that have more details on specific subjects, more lectures, and more labs in the following order:
1. Get started with GIS & Remote Sensing in QGIS #Beginners
2. Remote Sensing in QGIS: Fundamentals of Image Analysis 2020
3. Core GIS: Land Use and Land Cover & Change Detection in QGIS
4. Machine Learning in GIS: Understand the Theory and Practice
5. Machine Learning in GIS: Land Use/Land Cover Image Analysis
6. Machine Learning in ArcGIS: Map Land Use/ Land Cover in GIS
7. Object-based image analysis & classification in QGIS/ArcGIS
8. ArcGIS: Learn Deep Learning in ArcGIS to advance GIS skills
8. Google Earth Engine for Big GeoData Analysis: 3 Courses in 1
10. Google Earth Engine for Machine Learning & Change Detection
Option 2: Take my ‘joint’ courses that contain summarized information from the above courses, though in fewer details (labs, videos):
1. Geospatial Data Analyses & Remote Sensing: 4 Classes in 1
2. Machine Learning in GIS and Remote Sensing: 5 Courses in 1
3. Google Earth Engine for Big GeoData Analysis: 3 Courses in 1
4. Google Earth Engine for Machine Learning & Change Detection
For Data Science & Machine Learning Students:
1. R Crash Course - Introduction to R / RStudio / R-programming
2. Get started with R -Introduction to R-programming #Beginners (FREE)
3. R - Cluster Analysis & Unsupervised Machine Learning in R
- 4.5 Instructor Rating
- 186 Reviews
- 10,261 Students
- 8 Courses
Ich bin ein erfahrener Berater und Experte in Data Science. Ich habe mein MSc in Informatik an der TH Köln und MBA an der Universität Durham (UK) erlangt und habe mich später im Fachbereich Informatik promoviert. Als erfahrene Trainer mit mehr als 15 Jahren Berufserfahrung möchte ich meine Leidenschaft, praktische Erfahrungen und Kenntnisse in den Themen Big Data, Data Science, Data Analytics und IT management mit den anderen teilen und die praktische Kompetenzen von meinen Studenten auf ein sehr hohes Niveau bringen.