Clustering & Classification With Machine Learning In R

Clustering & Classification With Machine Learning In R, available at $79.99, has an average rating of 4.25, with 71 lectures, 4 quizzes, based on 222 reviews, and has 2593 subscribers.

You will learn about 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 This course is ideal for individuals who are Students Interested In Getting Started With Data Science Applications In The R & R Studio Environment or Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data or Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data Using R It is particularly useful for Students Interested In Getting Started With Data Science Applications In The R & R Studio Environment or Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data or Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data Using R.

Enroll now: Clustering & Classification With Machine Learning In R

Summary

Title: Clustering & Classification With Machine Learning In R

Price: $79.99

Average Rating: 4.25

Number of Lectures: 71

Number of Quizzes: 4

Number of Published Lectures: 71

Number of Published Quizzes: 4

Number of Curriculum Items: 75

Number of Published Curriculum Objects: 75

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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 themain 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!

Course Curriculum

Chapter 1: Introduction to the Course

Lecture 1: Welcome to Clustering & Classification with Machine Learning in R

Lecture 2: Data and Scripts For the Course

Lecture 3: Installing R and R Studio

Chapter 2: Read in Data From Different Sources in R

Lecture 1: Read in CSV & Excel Data

Lecture 2: Read in Unzipped Folder

Lecture 3: Read in Online CSV

Lecture 4: Read in Googlesheets

Lecture 5: Read in Data from Online HTML Tables-Part 1

Lecture 6: Read in Data from Online HTML Tables-Part 2

Lecture 7: Read Data from a Database

Chapter 3: Data Pre-processing and Visualization

Lecture 1: Remove Missing Values

Lecture 2: More Data Cleaning

Lecture 3: Introduction to dplyr for Data Summarizing-Part 1

Lecture 4: Introduction to dplyr for Data Summarizing-Part 2

Lecture 5: Exploratory Data Analysis(EDA): Basic Visualizations with R

Lecture 6: More Exploratory Data Analysis with xda

Lecture 7: Data Exploration & Visualization With dplyr & ggplot2

Lecture 8: Associations Between Quantitative Variables- Theory

Lecture 9: Testing for Correlation

Lecture 10: Evaluate the Relation Between Nominal Variables

Lecture 11: Cramer's V for Examining the Strength of Association Between Nominal Variable

Chapter 4: Machine Learning for Data Science

Lecture 1: How is Machine Learning Different from Statistical Data Analysis?

Lecture 2: What is Machine Learning (ML) About? Some Theoretical Pointers

Chapter 5: Unsupervised Learning in R

Lecture 1: K-Means Clustering

Lecture 2: Other Ways of Selecting Cluster Numbers

Lecture 3: Fuzzy K-Means Clustering

Lecture 4: Weighted k-means

Lecture 5: Partitioning Around Meloids (PAM)

Lecture 6: Hierarchical Clustering in R

Lecture 7: Expectation-Maximization (EM) in R

Lecture 8: DBSCAN Clustering in R

Lecture 9: Cluster a Mixed Dataset

Lecture 10: Should We Even Do Clustering?

Lecture 11: Assess Clustering Performance

Lecture 12: Which Clustering Algorithm to Choose?

Chapter 6: Feature/Dimension Reduction

Lecture 1: Dimension Reduction-theory

Lecture 2: Principal Component Analysis (PCA)

Lecture 3: More on PCA

Lecture 4: Multidimensional Scaling

Lecture 5: Singular Value Decomposition (SVD)

Chapter 7: Feature Selection to Select the Most Relevant Predictors

Lecture 1: Removing Highly Correlated Predictor Variables

Lecture 2: Variable Selection Using LASSO Regression

Lecture 3: Variable Selection With FSelector

Lecture 4: Boruta Analysis for Feature Selection

Chapter 8: Supervised Learning Theory

Lecture 1: Some Basic Supervised Learning Concepts

Lecture 2: Pre-processing for Supervised Learning

Chapter 9: Supervised Learning: Classification

Lecture 1: Binary Classification

Lecture 2: What are GLMs?

Lecture 3: Logistic Regression Models as Binary Classifiers

Lecture 4: Binary Classifier with PCA

Lecture 5: Some Pointers on Evaluating Accuracy

Lecture 6: Obtain Binary Classification Accuracy Metrics

Lecture 7: More on Binary Accuracy Measures

Lecture 8: Linear Discriminant Analysis

Lecture 9: Multi-class Classification Models

Lecture 10: Our Multi-class Classification Problem

Lecture 11: Classification Trees

Lecture 12: More on Classification Tree Visualization

Lecture 13: Classification with Party Package

Lecture 14: Decision Trees

Lecture 15: Random Forest (RF) Classification

Lecture 16: Examine Individual Variable Importance for Random Forests

Lecture 17: GBM Classification

Lecture 18: Support Vector Machines (SVM) for Classification

Lecture 19: More SVM for Classification

Lecture 20: Variable Importance in SVM Modelling with rminer

Chapter 10: Additional Lectures

Lecture 1: Fuzzy C-Means Clustering

Lecture 2: Read in DTA Extension File

Lecture 3: Github

Lecture 4: What Is Data Science?

Lecture 5: Group By Time

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 3 votes
2 stars: 12 votes
3 stars: 21 votes
4 stars: 53 votes
5 stars: 133 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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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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