Clustering & Classification With Machine Learning In R
4.7 (129 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,487 students enrolled

Clustering & Classification With Machine Learning In R

Harness The Power Of Machine Learning For Unsupervised & Supervised Learning In R -- With Practical Examples
4.7 (129 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,487 students enrolled
Created by Minerva Singh
Last updated 2/2020
English
English [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 8 hours on-demand video
  • 3 articles
  • 60 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Be Able To Harness The Power Of R For Practical Data Science
  • Read In Data Into The R Environment From Different Sources
  • Carry Out Basic Data Pre-processing & Wrangling In R Studio
  • Implement Unsupervised/Clustering Techniques Such As k-means Clustering
  • Implement Dimensional Reduction Techniques (PCA) & Feature Selection
  • Implement Supervised Learning Techniques/Classification Such As Random Forests
  • Evaluate Model Performance & Learn The Best Practices For Evaluating Machine Learning Model Accuracy
Course content
Expand all 68 lectures 07:50:28
+ Read in Data From Different Sources in R
7 lectures 40:07
Read in CSV & Excel Data
09:56
Read in Unzipped Folder
03:04
Read in Googlesheets
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
+ Data Pre-processing and Visualization
11 lectures 01:38:50
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 & Visualization With dplyr & ggplot2
06:07
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

Data pre-processing quiz

Section 3 Quiz
2 questions
+ Unsupervised Learning in R
12 lectures 01:30:54
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 Meloids (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

Unsupervised Learning Quiz

Section 5 Quiz
2 questions
+ Feature/Dimension Reduction
5 lectures 26:41
Principal Component Analysis (PCA)
13:10
More on PCA
04:27
Multidimensional Scaling
02:57
Singular Value Decomposition (SVD)
02:50

Dimension reduction quiz

Section 6 Quiz
2 questions
+ Feature Selection to Select the Most Relevant Predictors
4 lectures 38:50
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
+ Supervised Learning Theory
2 lectures 13:41
Some Basic Supervised Learning Concepts
10:10
Pre-processing for Supervised Learning
03:31
+ Supervised Learning: Classification
20 lectures 02:08:33
What are GLMs?
05:25
Logistic Regression Models as Binary Classifiers
09:10
Binary Classifier with PCA
06:29
Obtain Binary Classification Accuracy Metrics
08:18
Linear Discriminant Analysis
12:55
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

Classification Quiz

Section 9 Quiz
3 questions
+ Additional Lectures
2 lectures 10:14
Fuzzy C-Means Clustering
06:11
Read in DTA Extension File
04:03
Requirements
  • Should Be Able To Operate & Install Software On A Computer
  • Prior Exposure To Common Machine Learning Terms Such As Unsupervised & Supervised Learning
Description

HERE IS WHY YOU SHOULD TAKE THIS COURSE:

This course your complete guide to both supervised & 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 & 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 & 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 & 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...& 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 & 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