**Clustering and Classification with Machine Learning in R**

English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 7h 42m | 1.34 GB

eLearning | Skill level: All Levels

The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R

This course is your complete guide to both supervised and unsupervised learning using R. This course covers all the main aspects of practical data science; if you take this course, there is no need to take other courses or buy 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 take your career to the next level.

Over the course of research, the author realized that almost all the R data science courses and books out there do take account of 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, the author digs deep into R’s machine learning features and give 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 via R.

The following topics will be covered: –

- 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, and unsupervised learning in R
- Model building and selection and much more!

The course will help you implement 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 such as Caret to work with real data in R. You’ll even understand concepts such as unsupervised learning, dimension reduction, and supervised learning.

Learn

- Read-in data into the R environment from different sources
- Carry out basic data pre-processing and wrangling in R Studio
- Implement unsupervised/clustering techniques such as K-means clustering
- Implement dimensional reduction techniques (PCA) and feature selection
- Implement supervised learning techniques/classification such as Random Forests
- Evaluate model performance and learn the best practices for evaluating machine learning model accuracy

**+ Table of Contents**

**Introduction to the Course**

1 Welcome to Clustering & Classification with Machine Learning in R

2 Installing R and R Studio

**Read in Data from Different Sources in R**

3 Read in CSV & Excel Data

4 Read in Unzipped Folder

5 Read in Online CSV

6 Read in Googlesheets

7 Read in Data from Online HTML Tables-Part 1

8 Read in Data from Online HTML Tables-Part 2

9 Read Data from a Database

**Data Pre-processing and Visualization**

10 Remove Missing Values

11 More Data Cleaning

12 Introduction to dplyr for Data Summarizing-Part 1

13 Introduction to dplyr for Data Summarizing-Part 2

14 Exploratory Data Analysis (EDA) – Basic Visualizations with R

15 More Exploratory Data Analysis with xda

16 Data Exploration & Visualization With dplyr & ggplot2

17 Associations Between Quantitative Variables- Theory

18 Testing for Correlation

19 Evaluate the Relation Between Nominal Variables

20 Cramer’s V for Examining the Strength of Association Between Nominal Variable

**Machine Learning for Data Science**

21 How is Machine Learning Different from Statistical Data Analysis

22 What is Machine Learning (ML) About Some Theoretical Pointers

**Unsupervised Learning in R**

23 K-Means Clustering

24 Other Ways of Selecting Cluster Numbers

25 Fuzzy K-Means Clustering

26 Weighted k-means

27 Partitioning Around Meloids (PAM)

28 Hierarchical Clustering in R

29 Expectation-Maximization (EM) in R

30 DBSCAN Clustering in R

31 Cluster a Mixed Dataset

32 Should We Even Do Clustering

33 Assess Clustering Performance

34 Which Clustering Algorithm to Choose

**Feature Dimension Reduction**

35 Dimension Reduction-theory

36 Principal Component Analysis (PCA)

37 More on PCA

38 Multidimensional Scaling

39 Singular Value Decomposition (SVD)

**Feature Selection to Select the Most Relevant Predictors**

40 Removing Highly Correlated Predictor Variables

41 Variable Selection Using LASSO Regression

42 Variable Selection with FSelector

43 Boruta Analysis for Feature Selection

**Supervised Learning Theory**

44 Some Basic Supervised Learning Concepts

45 Pre-processing for Supervised Learning

**Supervised Learning – Classification**

46 What are GLMs

47 Logistic Regression Models as Binary Classifiers

48 Binary Classifier with PCA

49 Some Pointers on Evaluating Accuracy

50 Obtain Binary Classification Accuracy Metrics

51 More on Binary Accuracy Measures

52 Linear Discriminant Analysis

53 Our Multi-class Classification Problem

54 Classification Trees

55 More on Classification Tree Visualization

56 Classification with Party Package

57 Decision Trees

58 Random Forest (RF) Classification

59 Examine Individual Variable Importance for Random Forests

60 GBM Classification

61 Support Vector Machines (SVM) for Classification

62 More SVM for Classification

63 Variable Importance in SVM Modelling with rminer

**Additional Lectures**

64 Fuzzy C-Means Clustering

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