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Cluster Analysis & Unsupervised Machine Learning in R

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Cluster Analysis & Unsupervised Machine Learning in R, available at $44.99, has an average rating of 4.4, with 41 lectures, based on 39 reviews, and has 4682 subscribers.

You will learn about 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 This course is ideal for individuals who are The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning and R in their field. or Everyone who would like to learn Data Science Applications In The R & R Studio Environment or Everyone who would like to learn theory and implementation of Unsupervised Learning On Real-World Data It is particularly useful for The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning and R in their field. or Everyone who would like to learn Data Science Applications In The R & R Studio Environment or Everyone who would like to learn theory and implementation of Unsupervised Learning On Real-World Data.

Enroll now: Cluster Analysis & Unsupervised Machine Learning in R

Summary

Title: Cluster Analysis & Unsupervised Machine Learning in R

Price: $44.99

Average Rating: 4.4

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $29.99

Quality Status: approved

Status: Live

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
  • 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

Who Should Attend

  • 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

Target Audiences

  • 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

Here’s why enrolling in this course is a smart choice:

This comprehensive course will serve as your ultimate guide to unsupervised learning and clustering techniques, utilizing the R-programming language and JavaScript.

In addition to practical demonstrations of R-scripts, this course delves into the theoretical foundations of unsupervised machine learning, providing you with a deep understanding of concepts such as K-means and Hierarchical clustering.

You’ll gain expertise in various aspects of practical data science related to unsupervised machine learning and clustering, saving you valuable time and resources compared to other expensive materials in the field of R-based data science.

Unlocking Opportunities:

In today’s era of big data, organizations worldwide harness the power of R and Google Cloud Computing Services for data analysis in business and research. Mastering unsupervised learning in R can give your career a significant boost and provide your company with a competitive edge. Moreover, you’ll explore the capabilities of cloud computing using Google services like Earth Engine, applying unsupervised K-means learning to real-world mapping applications.

Course Content:

This course comprises eight comprehensive sections, covering every facet of unsupervised machine learning, from theory to practice:

  • Gain a solid grasp of Machine Learning, Cluster Analysis, and Unsupervised Machine Learning from theory to practical application.

  • Leverage the potential of unsupervised learning, including cluster analysis, both in R and with Google Cloud Services.

  • Dive into Machine Learning, Supervised Learning, and Unsupervised Learning within the R environment.

  • Complete two independent projects focusing on Unsupervised Machine Learning, one in R and the other using Google Cloud Services.

  • Implement Unsupervised Clustering Techniques, including K-means Clustering and Hierarchical Clustering, among others.

No Prior Knowledge Required:

This course is designed for learners with no prior experience in R or statistics/machine learning. It begins with fundamental R Data Science concepts and gradually progresses to more complex topics. You’ll work with real data from various sources, including a real-life project on Google’s cloud computing platform. All scripts and data used in the course will be provided, making your learning journey smooth and practical.

Unique Approach:

This course stands out from other training resources due to its hands-on, easy-to-follow methods, which simplify even the most complex R concepts. Each lecture aims to enhance your data science and clustering skills, empowering you with practical solutions. By the end of the course, you’ll confidently analyze diverse data streams for your projects, earning recognition from future employers for your advanced machine learning expertise and knowledge of cutting-edge data science techniques.

Target Audience:

Ideal for professionals needing to use cluster analysis, unsupervised machine learning, and R in their field, this course offers valuable insights and skills essential for success.

Practical Exercises:

Engage in practical exercises where you’ll receive precise instructions and datasets to implement machine learning algorithms using R and Google Cloud Computing tools.

Enroll Now:

Join this course today to embark on a transformative journey in the realm of unsupervised machine learning and clustering.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What is Machine Leraning and it's main types?

Lecture 3: Overview of Machine Leraning in R

Chapter 2: Software used in this course

Lecture 1: What is R and RStudio?

Lecture 2: How to install R and RStudio in 2020

Lecture 3: Lab: Get started with R in RStudio

Lecture 4: Sign up for Google Earth Engine (needed for your projects later in the course)

Lecture 5: Interface of Google Earth Engine: Code Editor & Explorer

Chapter 3: R Crash Course – get started with R-programming in R-Studio

Lecture 1: Introduction

Lecture 2: Lab: Installing Packages and Package Management in R

Lecture 3: Lab: Variables in R and assigning Variables in R

Lecture 4: Overview of data types and data structures in R

Lecture 5: Lab: data types and data structures in R

Lecture 6: Dataframes: overview

Lecture 7: Functions in R – overview

Lecture 8: Lab: Functions in R – get started!

Lecture 9: Lab: For Loops in R

Lecture 10: Read Data into R

Chapter 4: Unsupervised learning: Hierarchical Clustering in R

Lecture 1: Unsupervised Learning & Clustering: theory

Lecture 2: Hierarchical Clustering: Example

Lecture 3: Hierarchical Clustering: Lab

Lecture 4: Hierarchical Clustering: Merging points

Lecture 5: Heat Maps: theory

Lecture 6: Heat Maps: Lab

Chapter 5: Unsupervised Learning: K-Means Clustering

Lecture 1: K-Means Clustering: Theory

Lecture 2: Example K-Means Clustering in R: Lab

Lecture 3: K-means clustering: Application to email marketing

Lecture 4: Heatmaps to visualize K-Means Results in R: Examplery Lab

Lecture 5: Model-based Unsupervised Clustering in R

Chapter 6: More Unsupervised Clustering techniques: Hands-On

Lecture 1: Starting with Fuzzy K-means in R

Lecture 2: Entropy Weighted K-Means in R

Chapter 7: Performance Evaluation of Unsupervised Learning Clustering Algorithms in R

Lecture 1: How to assess a Clustering Tendency of the dataset

Lecture 2: Selecting the number of clusters for unsupervised Clustering methods (K-Means)

Lecture 3: Assessing the performance of unsupervised learning (clustering) algorithms

Lecture 4: How to compare the performance of different unsupervised clustering algoritms?

Chapter 8: Independent Project in Cluster Analysis based on Case Study

Lecture 1: Introduction to Case Study

Lecture 2: Project Assignment

Chapter 9: Applied Example: unsupervised K-means learning for mapping applications

Lecture 1: Understanding using satellite images for mapping tasks: short introduction

Lecture 2: Import images and their visualization in Earth Engine

Lecture 3: Unsupervised K-means satellite image analysis in Earth Engine for mapping

Chapter 10: Bonus

Lecture 1: Bonus Lecture

Instructors

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 1 votes
  • 3 stars: 5 votes
  • 4 stars: 7 votes
  • 5 stars: 24 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.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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