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Updated in [October 16th, 2023]

What does this course tell?
(Please note that the following overview content is from the original platform)
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 MORENO 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!

We considered the value of this course from many aspects, and finally summarized it for you from two aspects: skills and knowledge, and the people who benefit from it:
(Please note that our content is optimized through artificial intelligence tools and carefully reviewed by our editorial staff.)
What skills and knowledge will you acquire during this course?
By taking the course "Cluster Analysis & Unsupervised Machine Learning in R," learners will acquire the following skills and knowledge:
1. Understanding the basics of Machine Learning, Cluster Analysis & Unsupervised Machine Learning from theory to practice.
2. Applying unsupervised learning (cluster analysis) in R and with Google Cloud Services.
3. Implementing unsupervised clustering techniques such as k-means clustering and hierarchical clustering.
4. Completing two independent projects on unsupervised machine learning in R and using Google Cloud Services.
5. Gaining proficiency in R and R-programming, even for learners with no prior R or statistics/machine learning knowledge.
6. Using different data streams and data science packages to work with real data in R.
7. Implementing practical solutions for data analysis using machine learning algorithms.
8. Gaining appreciation from future employers with improved machine learning skills and knowledge of cutting-edge data science methods.
9. Applying cluster analysis, unsupervised machine learning, and R in professional fields.
10. Gaining practical experience through exercises and datasets provided in the course.
Who will benefit from this course?
Professionals in the field of data science, machine learning, and data analysis will benefit from this course. It is suitable for individuals who want to gain a comprehensive understanding of unsupervised learning and clustering techniques using R-programming language and JavaScript.
Specifically, professionals working in companies that analyze big volumes of data for business and research purposes can benefit from this course. By becoming proficient in unsupervised learning in R, they can give their company a competitive edge and advance their career.
Additionally, individuals interested in exploring the power of cloud computing with Google services for real-world applications of unsupervised learning, such as mapping applications, can find value in this course.
No prior knowledge of R or statistics/machine learning is required, making it accessible to beginners as well. The course provides a full introduction to R and R-programming, making it suitable for individuals who are new to R.

Course Syllabus

Introduction

Software used in this course

R Crash Course - get started with R-programming in R-Studio

Unsupervised learning: Hierarchical Clustering in R

Unsupervised Learning: K-Means Clustering

More Unsupervised Clustering techniques: Hands-On

Performance Evaluation of Unsupervised Learning Clustering Algorithms in R

Independent Project in Cluster Analysis based on Case Study

Applied Example: unsupervised K-means learning for mapping applications

Bonus