Udemy - Cluster Analysis & Unsupervised Machine Learning in R

seeders: 0
leechers: 1
Added 5 years ago by tutsnode in Other

Download Fast Safe Anonymous
movies, software, shows...

Files

Udemy - Cluster Analysis & Unsupervised Machine Learning in R (Size: 1.72 GB)
  .pad
  0 89 B
  1 85 B
  2 152.91 KB
  3 301.83 KB
  4 367.21 KB
  5 393.02 KB
  6 262.79 KB
  7 136.49 KB
  8 78.56 KB
  9 170.6 KB
  10 420.48 KB
  11 409.28 KB
  12 312 KB
  13 145.28 KB
  14 337.9 KB
  15 181.63 KB
  16 310.46 KB
  17 55.52 KB
  18 274.75 KB
  19 96.61 KB
  20 236.38 KB
  21 304.15 KB
  22 352.9 KB
  23 364.53 KB
  24 394.55 KB
  25 456.67 KB
  26 169 KB
  27 316.59 KB
  28 353.94 KB
  29 174.84 KB
  30 187.73 KB
  31 449.76 KB
  32 18 KB
  33 23.11 KB
  34 341.77 KB
  35 375.53 KB
  36 277.79 KB
  37 359.53 KB
  38 256.15 KB
  39 80.54 KB
  40 346.73 KB
  TutsNode.com.txt 63 B
  [TGx]Downloaded from torrentgalaxy.to .txt 585 B
  [TutsNode.com] - Cluster Analysis & Unsupervised Machine Learning in R
  1. Introduction
  1. Introduction.mp4 25.55 MB
  1. Introduction.srt 3.04 KB
  2. What is Machine Leraning and it's main types.mp4 46.86 MB
  2. What is Machine Leraning and it's main types.srt 11.23 KB
  3. Overview of Machine Leraning in R.mp4 5.66 MB
  3. Overview of Machine Leraning in R.srt 2.07 KB
  10. Bonus
  1. Bonus Lecture.mp4 5.92 MB
  1. Bonus Lecture.srt 1.33 KB
  2. Software used in this course
  1. What is R and RStudio.mp4 12.23 MB
  1. What is R and RStudio.srt 3.06 KB
  2. How to install R and RStudio in 2020.mp4 38.7 MB
  2. How to install R and RStudio in 2020.srt 6.17 KB
  3. Lab Get started with R in RStudio.mp4 47.7 MB
  3. Lab Get started with R in RStudio.srt 9.52 KB
  4. Sign up for Google Earth Engine (needed for your projects later in the course).mp4 45.67 MB
  4. Sign up for Google Earth Engine (needed for your projects later in the course).srt 4.33 KB
  5. Interface of Google Earth Engine Code Editor & Explorer.mp4 127.12 MB
  5. Interface of Google Earth Engine Code Editor & Explorer.srt 9.26 KB
  3. R Crash Course - get started with R-programming in R-Studio
  1. Introduction.mp4 3.96 MB
  10. Read Data into R.mp4 31.91 MB
  10. Read Data into R.srt 4.82 KB
  10.1 Example1.xlsx 9.81 KB
  10.2 Example3.txt 292 B
  10.3 002_ReadingDaraInR.R 744 B
  10.4 Example.csv 292 B
  2. Lab Installing Packages and Package Management in R.mp4 24.15 MB
  2. Lab Installing Packages and Package Management in R.srt 4.48 KB
  2.1 R Crash Course I_udemy_script.R 12.94 KB
  3. Lab Variables in R and assigning Variables in R.mp4 7.65 MB
  3. Lab Variables in R and assigning Variables in R.srt 1.71 KB
  4. Overview of data types and data structures in R.mp4 27.2 MB
  4. Overview of data types and data structures in R.srt 8.21 KB
  5. Lab data types and data structures in R.mp4 48.1 MB
  5. Lab data types and data structures in R.srt 8.82 KB
  6. Dataframes overview.mp4 16.67 MB
  6. Dataframes overview.srt 3.98 KB
  7. Functions in R - overview.mp4 33.23 MB
  7. Functions in R - overview.srt 8.63 KB
  8. Lab Functions in R - get started!.mp4 18.48 MB
  8. Lab Functions in R - get started!.srt 3.98 KB
  9. Lab For Loops in R.mp4 24.83 MB
  9. Lab For Loops in R.srt 3.97 KB
  4. Unsupervised learning Hierarchical Clustering in R
  1. Unsupervised Learning & Clustering theory.mp4 19.06 MB
  1. Unsupervised Learning & Clustering theory.srt 7.16 KB
  2. Hierarchical Clustering Example.mp4 26.64 MB
  2. Hierarchical Clustering Example.srt 8.97 KB
  3. Hierarchical Clustering Lab.mp4 12.63 MB
  3. Hierarchical Clustering Lab.srt 2.51 KB
  3.1 007_Hierarchical_clustering.R 390 B
  4. Hierarchical Clustering Merging points.mp4 6.25 MB
  4. Hierarchical Clustering Merging points.srt 3.08 KB
  5. Heat Maps theory.mp4 19.32 MB
  5. Heat Maps theory.srt 9.83 KB
  6. Heat Maps Lab.mp4 26.61 MB
  6. Heat Maps Lab.srt 4.75 KB
  6.1 007_Hierarchical_clustering_part3_heatmaps.R 536 B
  5. Unsupervised Learning K-Means Clustering
  1. K-Means Clustering Theory.mp4 17.48 MB
  1. K-Means Clustering Theory.srt 5.62 KB
  2. Example K-Means Clustering in R Lab.mp4 22.83 MB
  2. Example K-Means Clustering in R Lab.srt 5.32 KB
  2.1 009_KMenas.R 959 B
  3. K-means clustering Application to email marketing.mp4 110.7 MB
  3. K-means clustering Application to email marketing.srt 14.54 KB
  3.1 009_KMenas_part2.R 1.26 KB
  4. Heatmaps to visualize K-Means Results in R Examplery Lab.mp4 24.69 MB
  4. Heatmaps to visualize K-Means Results in R Examplery Lab.srt 4.16 KB
  4.1 009_KMenas.R 959 B
  5. Model-based Unsupervised Clustering in R.mp4 74.62 MB
  5. Model-based Unsupervised Clustering in R.srt 10.3 KB
  5.1 012_EM_clustering.R 1.44 KB
  6. More Unsupervised Clustering techniques Hands-On
  1. Starting with Fuzzy K-means in R.mp4 102.14 MB
  1. Starting with Fuzzy K-means in R.srt 14.51 KB
  1.1 010_FuzzyKMenas.R 1.43 KB
  2. Entropy Weighted K-Means in R.mp4 61.92 MB
  2. Entropy Weighted K-Means in R.srt 8.95 KB
  2.1 011_WeigthedKMenas.R 928 B
  7. Performance Evaluation of Unsupervised Learning Clustering Algorithms in R
  1. How to assess a Clustering Tendency of the dataset.mp4 42.32 MB
  1. How to assess a Clustering Tendency of the dataset.srt 5.53 KB
  1.1 013_ClusteringTendency.R 1.16 KB
  2. Selecting the number of clusters for unsupervised Clustering methods (K-Means).mp4 59.33 MB
  2. Selecting the number of clusters for unsupervised Clustering methods (K-Means).srt 9.24 KB
  2.1 009_KMenas_part3_cluster_assign.R 2.26 KB
  3. Assessing the performance of unsupervised learning (clustering) algorithms.mp4 38.45 MB
  3. Assessing the performance of unsupervised learning (clustering) algorithms.srt 7.19 KB
  3.1 014_ClusteringPerformance.R 817 B
  4. How to compare the performance of different unsupervised clustering algoritms.mp4 27.16 MB
  4. How to compare the performance of different unsupervised clustering algoritms.srt 5.64 KB
  4.1 015_ClusteringSelect.R 457 B
  8. Independent Project in Cluster Analysis based on Case Study
  1. Introduction to Case Study.mp4 58.09 MB
  1. Introduction to Case Study.srt 7.81 KB
  1.1 samsungData.rda 27.27 MB
  2. Project Assignment.mp4 68.24 MB
  2. Project Assignment.srt 9.54 KB
  9. Applied Example unsupervised K-means learning for mapping applications
  1. Understanding using satellite images for mapping tasks short introduction.mp4 63.37 MB
  1. Understanding using satellite images for mapping tasks short introduction.srt 8.63 KB
  2. Import images and their visualization in Earth Engine.mp4 141.91 MB
  2. Import images and their visualization in Earth Engine.srt 11.39 KB
  3. Unsupervised K-means satellite image analysis in Earth Engine for mapping.mp4 103.71 MB
  3. Unsupervised K-means satellite image analysis in Earth Engine for mapping.srt 9.01 KB
  3.1 Lab2_GEE_import_data.pdf 330.12 KB

Description



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 youimplement the methods using real dataobtained 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

Requirements

Availabiliy computer and internet
R-programming skills is NOT a requirement, but would be a plus

Last Updated 12/2020

Related Torrents

torrent name size uploader age seed leech
5
9
2
1
0