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Clustering and Classification with R

A comprehensive course on Clustering and Classification with R which includes numerous practical examples

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Created by Minerva Singh
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4.6 (166 ratings)
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This course includes

7 h 55 min of video content
Beginner Difficulty
Perpetual Access
Access on mobile and Tablet
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Learning Objectives

Be Able To Harness The Power Of R For Practical Data Science
Carry Out Basic Data Pre-processing and Wrangling In R Studio
Implement Dimensional Reduction Techniques (PCA) and Feature Selection
Evaluate Model Performance and Learn The Best Practices For Evaluating Machine Learning Model Accuracy
Read In Data Into The R Environment From Different Sources
Implement Unsupervised/Clustering Techniques Such As k-means Clustering

Table of Contents

69 lectures
7 h 55 min
Welcome to Clustering and Classification with Machine learning in R
04:51
Data and Scripts For the Course
00:03
Installing R and R studio
06:36
Read in CSV and Excel Data
09:56
Read in Unzipped Folder
03:04
Read in online CSV
04:04
Read in Google Sheets
04:03
Read in Data from Online HTML Tables-Part 1
04:13
Read in Data from Online HTML Tables-Part 2
06:24
Read Data from a Database
08:23
Remove Missing Values
17:12
More Data Cleaning
08:05
Introduction to dplyr for Data Summarizing-Part 1
06:11
Introduction to dplyr for Data Summarizing-Part 2
04:44
Exploratory Data Analysis(EDA): Basic Visualizations with R
18:53
More Exploratory Data Analysis with xda
04:16
Data Exploration and Visualization With dplyr and ggplot2
06:07
Associations between Quantitative Variables-Theory
03:43
Testing for Correlation
19:50
Evaluate the Relation Between Nominal Variables
06:14
Cramer's V for Examining the Strength of Association Between Nominal Variable
03:35
How is Machine Learning Different from Statistical Data Analysis?
05:36
What is Machine Learning(ML) about? Some theoretical pointers
05:32
K-Means Clustering
14:31
Other Ways of Selecting Cluster Numbers
03:27
Fuzzy K-Means Clustering
18:14
Weighted k-means
06:04
Partitioning Around Medoids (PAM)
06:48
Hierarchical Clustering in R
14:13
Expectation-Maximization (EM) in R
05:50
DBSCAN Clustering in R
04:58
Cluster a Mixed Dataset
04:01
Should We Even Do Clustering?
03:07
Assess Clustering Performance
05:46
Which Clustering Algorithm to Choose?
03:55
Dimension Reduction- theory
03:17
Principal Component Analysis (PCA)
13:10
More on PCA
04:27
Multidimensional Scaling
02:57
Singular Value Decomposition (SVD)
02:50
Removing Highly Correlated Predictor Variables
16:42
Variable Selection Using LASSO Regression
03:42
Variable Selection With FSelector
13:35
Boruta Analysis for Feature Selection
04:51
Some Basic Supervised Learning Concepts
10:10
Pre-processing for Supervised Learning
03:31
Binary Classification
00:09
What are GLMs?
05:25
Logistic Regression Models as Binary Classifiers
09:10
Binary Classifier with PCA
06:29
Some pointers on Evaluating Accuracy
09:42
Obtain Binary Classification Accuracy Metrics
08:18
More on Binary Accuracy Measures
04:19
Linear Discriminant Analysis
12:55
Multi-class Classification Models
00:08
Our Multi-class Classification Problem
06:14
Classification Trees
11:55
More on Classification Tree Visualization
09:20
Classification with Party Package
05:12
Decision Trees
08:39
Random Forest (RF) Classification
08:15
Examine Individual Variable Importance for Random Forests
03:53
GBM Classification
07:50
Support Vector Machines (SVM) for Classification
03:55
More SVM for Classification
03:42
Variable Importance in SVM Modelling with rminer
03:03
Fuzzy C-Means Clustering
06:11
Read in DTA Extension File
04:03
Github
05:16

Description

HERE IS WHY YOU SHOULD TAKE THIS COURSE:

This course your complete guide to both supervised and unsupervised learning using R...

That means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on R based data science.

In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in R, you can give your company a competitive edge and boost your career to the next level.

LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.

I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic...

This course will give you a robust grounding in the main aspects of machine learning- clustering and classification.

Unlike other R instructors, I dig deep into the machine learning features of R and gives you a one-of-a-kind grounding in Data Science!

You will go all the way from carrying out data reading and cleaning to machine learning to finally implementing powerful machine learning algorithms and evaluating their performance using R.

THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF R MACHINE LEARNING:

  • A full introduction to the R Framework for data science
  • Data Structures and Reading in R, including CSV, Excel and HTML data
  • How to Pre-Process and “Clean” data by removing NAs/No data,visualization
  • Machine Learning, Supervised Learning, Unsupervised Learning in R
  • Model building and selection...and MUCH MORE!

By the end of the course, you’ll have the keys to the entire R Machine Learning Kingdom!

NO PRIOR R OR STATISTICS/MACHINE LEARNING 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. Many courses use made-up data that does not empower students to implement R based data science in real life.

After taking this course, you’ll easily use data science packages like caret to work with real data in R...

You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning. Again, we'll work with real data and you will have access to all the code and data used in the course.

JOIN MY COURSE NOW!

Who this course is for:

  • Students Interested In Getting Started With Data Science Applications In The R and R Studio Environment
  • Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data
  • Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data Using R
  • About Instructor

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    Minerva Singh

    Data Scientist (Cambridge University)

    I have completed PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.

    I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).

    Featured Reviews

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    Aug 04, 2021

    Rajesh Kumar

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    Clustering and classification with machine learning in R is a high-value course which has been brilliantly explained by the instructor by way of practical examples. I find the course most useful for my work and will be able to apply it on real time basis.
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    Sep 10, 2021

    Raj Kumar Majumdar

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    This is an excellent course underlining the importance of machine learning for supervised and unsupervised learning in R. The practical examples given by the instructor have added clarity to the concept. I will be able to apply the concept to my work to reap the benefit of this superb course.