R Ultimate 2024- R for Data Science and Machine Learning

R Ultimate 2024: R for Data Science and Machine Learning, available at $79.99, has an average rating of 4.83, with 204 lectures, based on 193 reviews, and has 2811 subscribers.

You will learn about learn all aspects of R from Basics, over Data Science, to Machine Learning and Deep Learning learn R basics (data types, structures, variables, and more) learn R programming (writing loops, functions, and more) data im- and export basic data manipulation (piping, filtering, aggregation of results, data reshaping, set operations, joining datasets) data visualisation (different packages are learned, e.g. ggplot, plotly, leaflet, dygraphs) advanced data manipulation (outlier detection, missing data handling, regular expressions) regression models (create and apply regression models) model evaluation (What is underfitting and overfitting? Why is data splitted into training and testing? What are resampling techniques?) regularization (What is regularization? How can you apply it?) classification models (understand different algorithms and learn how to apply logistic regression, decision trees, random forests, support vector machines) association rules (learn the apriori model) clustering (kmeans, hierarchical clustering, DBscan) dimensionality reduction (factor analysis, principal component analysis) Reinforcement Learning (upper confidence bound) Deep Learning (deep learning for multi-target regression, binary and multi-label classification) Deep Learning (learn image classification with convolutional neural networks) Deep Learning (learn about Semantic Segmentation) Deep Learning (Recurrent Neural Networks, LSTMs) More on Deep Learning, e.g. Autoencoders, pretrained models, R/Shiny for web application development and deployment This course is ideal for individuals who are R beginners interested in learning R or data science practitioners who want to deepen their knowledge or developers who want to learn different aspects of Machine Learning It is particularly useful for R beginners interested in learning R or data science practitioners who want to deepen their knowledge or developers who want to learn different aspects of Machine Learning.

Enroll now: R Ultimate 2024: R for Data Science and Machine Learning

Summary

Title: R Ultimate 2024: R for Data Science and Machine Learning

Price: $79.99

Average Rating: 4.83

Number of Lectures: 204

Number of Published Lectures: 204

Number of Curriculum Items: 204

Number of Published Curriculum Objects: 204

Original Price: 69.99

Quality Status: approved

Status: Live

What You Will Learn

learn all aspects of R from Basics, over Data Science, to Machine Learning and Deep Learning

learn R basics (data types, structures, variables, and more)

learn R programming (writing loops, functions, and more)

data im- and export

basic data manipulation (piping, filtering, aggregation of results, data reshaping, set operations, joining datasets)

data visualisation (different packages are learned, e.g. ggplot, plotly, leaflet, dygraphs)

advanced data manipulation (outlier detection, missing data handling, regular expressions)

regression models (create and apply regression models)

model evaluation (What is underfitting and overfitting? Why is data splitted into training and testing? What are resampling techniques?)

regularization (What is regularization? How can you apply it?)

classification models (understand different algorithms and learn how to apply logistic regression, decision trees, random forests, support vector machines)

association rules (learn the apriori model)

clustering (kmeans, hierarchical clustering, DBscan)

dimensionality reduction (factor analysis, principal component analysis)

Reinforcement Learning (upper confidence bound)

Deep Learning (deep learning for multi-target regression, binary and multi-label classification)

Deep Learning (learn image classification with convolutional neural networks)

Deep Learning (learn about Semantic Segmentation)

Deep Learning (Recurrent Neural Networks, LSTMs)

More on Deep Learning, e.g. Autoencoders, pretrained models,

R/Shiny for web application development and deployment

Who Should Attend

R beginners interested in learning R

data science practitioners who want to deepen their knowledge

developers who want to learn different aspects of Machine Learning

Target Audiences

R beginners interested in learning R

data science practitioners who want to deepen their knowledge

developers who want to learn different aspects of Machine Learning

You want to be able to perform your own data analyses with R? You want to learn how to get business-critical insights out of your data? Or you want to get a job in this amazing field? In all of these cases, you found the right course!

We will start with the very Basics of R, like data types and -structures, programming of loops and functions, data im- and export.

Then we will dive deeper into data analysis: we will learn how to manipulate data by filtering, aggregating results, reshaping data, set operations, and joining datasets. We will discover different visualisation techniques for presenting complex data. Furthermore find out to present interactive timeseries data, or interactive geospatial data.

Advanced data manipulation techniques are covered, e.g. outlier detection, missing data handling, and regular expressions.

We will cover all fieldsof Machine Learning: Regressionand Classificationtechniques, Clustering, Association Rules, Reinforcement Learning, and, possibly most importantly, Deep Learning for Regression, Classification, Convolutional Neural Networks, Autoencoders, Recurrent Neural Networks,

You will also learn to develop web applications and how to deploy them with R/Shiny.

For each field, different algorithms are shown in detail: their core concepts are presented in 101 sessions. Here, you will understand how the algorithm works. Then we implement it together in lab sessions. We develop code, before I encourage you to work on exercise on your own, before you watch my solution examples. With this knowledge you can clearly identify a problem at hand and develop a plan of attack to solve it.

You will understand the advantages and disadvantages of different models and when to use which one. Furthermore, you will know how to take your knowledge into the real world.

You will get access to an interactive learning platformthat will help you to understand the concepts much better.

In this course code will never come out of thin air via copy/paste. We will developevery important line of code togetherand I will tell you why and how we implement it.

Take a look at some sample lectures. Or visit some of my interactive learning boards. Furthermore, there is a 30 day money back warranty, so there is no risk for you taking the course right now. Don’t wait. See you in the course.

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Course Overview

Lecture 2: R and RStudio (Overview and Installation)

Lecture 3: How to get the code

Lecture 4: How to get the code (alternative)

Lecture 5: RStudio Introduction / Project Setup

Lecture 6: File Formats

Lecture 7: Rmarkdown Lab

Lecture 8: Package Handling

Chapter 2: Data Types and -structures

Lecture 1: Basic Data Types 101

Lecture 2: Basic Data Types Lab

Lecture 3: Matrices and Arrays Lab

Lecture 4: Lists

Lecture 5: Factors

Lecture 6: Dataframes

Lecture 7: Strings Lab

Lecture 8: Datetime

Chapter 3: R Programming

Lecture 1: Operators

Lecture 2: Loops 101

Lecture 3: Loops Lab

Lecture 4: Functions 101

Lecture 5: Functions Lab (Intro)

Lecture 6: Functions Lab (Coding)

Chapter 4: Data Im- and Export

Lecture 1: Data Import Lab

Lecture 2: Data Export Lab

Lecture 3: Web Scraping Intro

Lecture 4: Web Scraping Lab

Chapter 5: Basic Data Manipulation

Lecture 1: Piping 101

Lecture 2: Filtering 101

Lecture 3: Filtering Lab

Lecture 4: Filtering Exercise

Lecture 5: Filtering Solution

Lecture 6: Data Aggregation 101

Lecture 7: Data Aggregation Lab

Lecture 8: Data Aggregation Exercise

Lecture 9: Data Aggregation Solution

Lecture 10: Data Reshaping 101

Lecture 11: Data Reshaping Lab

Lecture 12: Data Reshaping Exercise

Lecture 13: Data Reshaping Solution

Lecture 14: Set Operations 101

Lecture 15: Set Operations Lab

Lecture 16: Joining Datasets 101

Lecture 17: Joining Datasets Lab

Chapter 6: Data Visualisation

Lecture 1: Visualisation Overview

Lecture 2: ggplot 101

Lecture 3: ggplot Lab

Lecture 4: plotly Lab (Intro)

Lecture 5: plotly Lab

Lecture 6: leaflet Lab (Intro)

Lecture 7: leaflet Lab

Lecture 8: dygraphs Lab (Intro)

Lecture 9: dygraphs Lab

Chapter 7: Advanced Data Manipulation

Lecture 1: Outlier Detection 101

Lecture 2: Outlier Detection Lab (Intro)

Lecture 3: Outlier Detection Lab

Lecture 4: Outlier Detection Exercise

Lecture 5: Outlier Detection Solution

Lecture 6: Missing Data Handling 101

Lecture 7: Missing Data Handling Lab (Intro)

Lecture 8: Missing Data Handling Lab (1/1)

Lecture 9: Regular Expressions 101

Lecture 10: Regular Expressions Lab

Chapter 8: Machine Learning: Introduction

Lecture 1: AI 101

Lecture 2: Machine Learning 101

Lecture 3: Models

Chapter 9: Machine Learning: Regression

Lecture 1: Regression Types 101

Lecture 2: Univariate Regression 101

Lecture 3: Univariate Regression Interactive

Lecture 4: Univariate Regression Lab

Lecture 5: Univariate Regression Exercise

Lecture 6: Univariate Regression Solution

Lecture 7: Polynomial Regression 101

Lecture 8: Polynomial Regression Lab

Lecture 9: Multivariate Regression 101

Lecture 10: Multivariate Regression Lab

Lecture 11: Multivariate Regression Exercise

Lecture 12: Multivariate Regression Solution

Chapter 10: Machine Learning: Model Preparation and Evaluation

Lecture 1: Underfitting / Overfitting 101

Lecture 2: Train / Validation / Test Split 101

Lecture 3: Train / Validation / Test Split Interactive

Lecture 4: Train / Validation / Test Split Lab

Lecture 5: Resampling Techniques 101

Lecture 6: Resampling Techniques Lab

Chapter 11: Machine Learning: Regularization

Lecture 1: Regularization 101

Lecture 2: Regularization Lab

Chapter 12: Machine Learning: Classification Basics

Lecture 1: Confusion Matrix 101

Lecture 2: ROC Curve 101

Lecture 3: ROC Curve Interactive

Instructors

Bert Gollnick
Data Scientist

Rating Distribution

1 stars: 0 votes

2 stars: 4 votes

3 stars: 8 votes

4 stars: 55 votes

5 stars: 126 votes

Frequently Asked Questions

How long do I have access to the course materials?

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

Can I take my courses with me wherever I go?

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!