Harness Power of R for unsupervised machine Learning (k-means, hierarchical clustering) – With Practical Examples in R
What you will 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
☑ 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
HERE IS WHY YOU SHOULD TAKE THIS COURSE:
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!
What is Machine Leraning and it’s main types?
Overview of Machine Leraning in R
Software used in this course
What is R and RStudio?
How to install R and RStudio in 2020
Lab: Get started with R in RStudio
Sign up for Google Earth Engine (needed for your projects later in the course)
Interface of Google Earth Engine: Code Editor & Explorer
R Crash Course – get started with R-programming in R-Studio
Lab: Installing Packages and Package Management in R
Lab: Variables in R and assigning Variables in R
Overview of data types and data structures in R
Lab: data types and data structures in R
Functions in R – overview
Lab: Functions in R – get started!
Lab: For Loops in R
Unsupervised learning: Hierarchical Clustering in R
Unsupervised Learning & Clustering: theory
Hierarchical Clustering: Example
Hierarchical Clustering: Lab
Hierarchical Clustering: Merging points
Heat Maps: theory
Heat Maps: Lab
Unsupervised Learning: K-Means Clustering
K-Means Clustering: Theory
Example K-Means Clustering in R: Lab
K-means clustering: Application to email marketing
Heatmaps to visualize K-Means Results in R: Examplery Lab
More Unsupervised Clustering techniques: Hands-On
Starting with Fuzzy K-means in R
Entropy Weighted K-Means in R
Model-based Unsupervised Clustering in R
Performance Evaluation of Unsupervised Learning CLustering Algorithms in R
How to assess a Clustering Tendency of the dataset
Selecting the number of clusters for unsupervised Clustering methods (K-Means)
Assessing the performance of unsupervised learning (clustering) algorithms
How to compare the performance of different unsupervised clustering algoritms?
Applied Example: unsupervised K-means learning for mapping applications
Understanding using satellite images for mapping tasks: short introduction
Import images and their visualization in Earth Engine
Unsupervised K-means satellite image analysis in Earth Engine for mapping