The Ultimate R Programming Machine Learning Course

The Ultimate R Programming & Machine Learning Course, available at $19.99, has an average rating of 4.55, with 97 lectures, based on 13 reviews, and has 75 subscribers.

You will learn about Set up and navigate the R programming environment with confidence. Master the foundational building blocks of the R language. Install and manage R packages effectively using R Studio. Understand and utilize various data types and variables in R. Efficiently manipulate data for analysis and visualization. Develop and assess predictive models using advanced R packages. Apply machine learning methods to real-world data scenarios. Gain deep insights into industry applications of machine learning tools. Implement advanced machine learning concepts and techniques. Create visually compelling and informative plots using ggplot2. Optimize R code for high-performance computing tasks. Scrape web data and interact with databases seamlessly. Generate professional reports and documents with R Markdown. Design and execute training and test data sets for robust analysis. Acquire practical experience through hands-on projects and real-world examples. Transform complex data challenges into actionable insights with R. Enhance your career prospects in data science and statistics. This course is ideal for individuals who are Programmers or Data Scientists or Anyone interested in R & Machine Learning It is particularly useful for Programmers or Data Scientists or Anyone interested in R & Machine Learning.

Enroll now: The Ultimate R Programming & Machine Learning Course

Summary

Title: The Ultimate R Programming & Machine Learning Course

Price: $19.99

Average Rating: 4.55

Number of Lectures: 97

Number of Published Lectures: 97

Number of Curriculum Items: 97

Number of Published Curriculum Objects: 97

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Set up and navigate the R programming environment with confidence.

Master the foundational building blocks of the R language.

Install and manage R packages effectively using R Studio.

Understand and utilize various data types and variables in R.

Efficiently manipulate data for analysis and visualization.

Develop and assess predictive models using advanced R packages.

Apply machine learning methods to real-world data scenarios.

Gain deep insights into industry applications of machine learning tools.

Implement advanced machine learning concepts and techniques.

Create visually compelling and informative plots using ggplot2.

Optimize R code for high-performance computing tasks.

Scrape web data and interact with databases seamlessly.

Generate professional reports and documents with R Markdown.

Design and execute training and test data sets for robust analysis.

Acquire practical experience through hands-on projects and real-world examples.

Transform complex data challenges into actionable insights with R.

Enhance your career prospects in data science and statistics.

Who Should Attend

Programmers

Data Scientists

Anyone interested in R & Machine Learning

Target Audiences

Programmers

Data Scientists

Anyone interested in R & Machine Learning

Unleash the Power of R: Master Data Science and Statistics with the Ultimate Language for Data Enthusiasts

Embark on an exhilarating journey into the world of data science with our comprehensive R programming and machine learning course. This meticulously crafted program is designed to transform you into a proficient R programmer, capable of solving complex data challenges with ease and confidence.

Why Choose This Course?

Comprehensive Curriculum: Our immersive, solution-driven curriculum takes you from the basics to advanced techniques, ensuring a well-rounded and practical understanding of the R language.

Hands-On Learning: Engage in hands-on exercises and real-world examples that illuminate the art of data manipulation, predictive modeling, and statistical analysis.

Expert Instructors: Learn from industry experts who bring years of experience and a deep understanding of R programming and machine learning to the table.

What You’ll Learn

1. Introduction to R & R Studio: Get started with R and R Studio, setting up your environment for success.

2. Building Blocks of R: Dive deep into R packages, functions, data structures, control flow, and loops.

3. Data Types & Variables: Master the various data types in R and understand how to effectively use R variables.

4. Package Management: Learn to install and load essential R packages for enhanced functionality.

5. Data Manipulation: Efficiently manipulate data in R, preparing it for thorough analysis.

6. Predictive Modeling & Assessment: Gain expertise in prediction and model assessment using advanced R packages.

7. Machine Learning Techniques: Understand and apply machine learning methods, exploring an extensive set of R packages tailored for data science.

8. Advanced Machine Learning Concepts: Implement advanced machine learning concepts, elevating your analytical capabilities.

9. Data Analysis & Visualization: Harness the power of the renowned ggplot2 library to create visually stunning and insightful statistical plots.

10. High-Performance Computing: Tackle high-performance computing tasks, optimizing your R code for speed and efficiency.

11. Web Scraping & Databases: Learn to scrape web data and interact with databases, unlocking new data sources.

12. Document Creation: Create professional reports and documents using R Markdown and other tools.

13. Real-World Applications: Apply your skills to real-world scenarios, gaining deep insights into the practical application of machine learning tools in the industry.

Course Highlights

Interactive Projects: Work on interactive projects that simulate real-world challenges, providing a practical and engaging learning experience.

Community Support: Join a vibrant community of fellow learners, sharing knowledge and insights.

Flexible Learning: Learn at your own pace with lifetime access to course materials and updates.

Who Should Enroll?

This course is perfect for data enthusiasts, aspiring data scientists, statisticians, and anyone looking to enhance their programming skills and dive deep into the world of data science and machine learning with R. Whether you’re a beginner or an experienced professional, our course is tailored to help you achieve your goals.

Transform Your Career

Upon completing this transformative course, you’ll emerge as a skilled R programmer, equipped with the techniques and expertise to excel in the ever-evolving fields of data science and statistics. Don’t miss this opportunity to supercharge your data journey. Enroll today and unleash the power of R!

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction

Lecture 2: Welcome Message

Chapter 2: Getting started

Lecture 1: Downloading and installing R

Lecture 2: Introduction to R console

Lecture 3: Learn About R studio

Lecture 4: Learn How to Install and Load Packages with R studio

Lecture 5: Learn How to Use R as a calculator

Lecture 6: Learn and Understand R variables

Lecture 7: Understanding the different data types

Lecture 8: Learn How to store data in vectors – 1

Lecture 9: Learn How to store data in vectors – 2

Lecture 10: Learn and Understand Call Functions

Lecture 11: Advanced Data Structures

Lecture 12: Data Structures in R – Learning Data.frames

Lecture 13: Data Structures in R – Data.frames in Depth

Lecture 14: Data Structures in R – Learning Lists

Lecture 15: Data Structures – Learning Matrices

Lecture 16: Data Structures – Learning Arrays

Lecture 17: R – Learn How to Read a CSV File into R

Lecture 18: R – Excel is not easily readable

Lecture 19: R – Learn How to Read from database

Lecture 20: R – Learn How to Read data files from other statistical tools

Lecture 21: R – Learn How to Load binary R files

Lecture 22: R – Learn How to Load data included with R

Lecture 23: R – Learn How to Scrape data from the web

Chapter 3: R In Depth – Learn How to Create Statistical Graphs

Lecture 1: Introduction

Lecture 2: Base Graphics – Making histograms & Making scatterplots

Lecture 3: Base Graphics – Making boxplots

Lecture 4: Intro to ggplot2

Lecture 5: ggplot2 – Learn about plot histograms & densities

Lecture 6: Learn how to make scatterplots

Lecture 7: Learn how to make boxplots & Violin plots

Lecture 8: Learn how to make line plots

Lecture 9: Learn how to create small multiples

Lecture 10: Learn how to control colors and shapes

Lecture 11: Learn how to add themes to graphs

Chapter 4: R Programming In Depth

Lecture 1: Introduction

Lecture 2: Understanding the basics of function arguments

Lecture 3: R In Depth – Return a value from a function

Lecture 4: R In Depth – Gain flexibility with do.call

Lecture 5: R In Depth – Use if statements to control program flow

Lecture 6: R In Depth – Stagger if statements with else

Lecture 7: R In Depth – Check multiple statements with switch

Lecture 8: R In Depth – Run checks on entire vectors

Lecture 9: R In Depth – Check compound statements

Lecture 10: R In Depth – Iterate with a for loop

Lecture 11: R In Depth – Iterate with a while loop

Lecture 12: R In Depth – Control loops with break and next

Chapter 5: Learn and Understand Data Munging

Lecture 1: Introduction

Lecture 2: Data Munging – Repeat an operation on a list

Lecture 3: Data Munging – Learn About the mapply

Lecture 4: Data Munging – Learn About the aggregate function

Lecture 5: Data Munging – Learn About plyr package

Lecture 6: Data Munging – Combine datasets

Lecture 7: Data Munging – Join datasets

Lecture 8: Data Munging – Switch storage paradigms

Chapter 6: Learn How to Manipulate Strings

Lecture 1: Introduction

Lecture 2: Manipulating Strings – Combine strings together

Lecture 3: Manipulating Strings – Extract text

Chapter 7: Understanding Statistics in R

Lecture 1: Introduction

Lecture 2: Statistics – Draw numbers from probability distributions

Lecture 3: Statistics – Calculate averages, standard deviations and correlations

Lecture 4: Statistics – Compare samples with t-tests and analysis of variance

Chapter 8: Learn and Understand Linear Models

Lecture 1: Introduction

Lecture 2: Linear Models – Explore the data

Lecture 3: Linear Models – Fit multiple regression models

Lecture 4: Linear Models – Fit logistic regression

Lecture 5: Linear Models – Fit Poisson regression

Lecture 6: Linear Models – Analyze survival data

Lecture 7: Linear Models – Assess model quality with residuals

Lecture 8: Linear Models – Compare models

Lecture 9: Linear Models – Judge accuracy

Lecture 10: Linear Models – Estimate uncertainty with the bootstrap

Lecture 11: Linear Models – Choose variables using stepwise selection

Chapter 9: Learn More About Models

Lecture 1: Introduction

Lecture 2: Models – Decrease uncertainty with weakly informative priors

Lecture 3: Models – Learn About Fit nonlinear least squares

Lecture 4: Models – Learn About Splines

Lecture 5: Models – Learn About GAMs

Lecture 6: Models – Fit decision trees to make a random forest

Chapter 10: Learn and Understand Time Series

Lecture 1: Introduction

Lecture 2: Time Series – Fit and assess ARIMA models

Lecture 3: Time Series – Learn How to Use VAR for multivariate time series

Lecture 4: Time Series – Learn How to Use GARCH for better volatility modeling

Chapter 11: Learn About Clustering

Lecture 1: Clustering – Partition data

Lecture 2: Clustering – Robustly cluster, even with categorical data, with PAM

Lecture 3: Clustering – Perform hierarchical clustering

Chapter 12: knitr – Slideshows & Reports

Lecture 1: Introduction

Instructors

Jason Robinson
Data scientist and teacher

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 0 votes

4 stars: 2 votes

5 stars: 10 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!