Practical Linear Regression in R for Data Science in R
Practical Linear Regression in R for Data Science in R, available at $44.99, has an average rating of 4.4, with 32 lectures, 5 quizzes, based on 41 reviews, and has 7046 subscribers.
You will learn about Analyse and visualize data using Linear Regression Learn different types of linear regressions (1-dimensional and multi-dimensional models, logistic regressions, ANOVA, etc) Learn how to interpret and explain machine learning models Plot the graph of results of Linear Regression to visually analyze the results Assumptions of linear regression hypothesis testing Do feature selection and transformations to fine tune machine learning models Fully understand the basics of Machine Learning & Linear Regression Models from theory to practice Learn how to deal with the categorical data in your regression modeling and correlation between variables Learn the basics of R-programming This course is ideal for individuals who are The course is ideal for professionals who need to use regression analysis & machine learning in their field or Everyone who would like to learn Data Science Applications In The R & R Studio Environment It is particularly useful for The course is ideal for professionals who need to use regression analysis & machine learning in their field or Everyone who would like to learn Data Science Applications In The R & R Studio Environment.
Enroll now: Practical Linear Regression in R for Data Science in R
Summary
Title: Practical Linear Regression in R for Data Science in R
Price: $44.99
Average Rating: 4.4
Number of Lectures: 32
Number of Quizzes: 5
Number of Published Lectures: 32
Number of Published Quizzes: 5
Number of Curriculum Items: 37
Number of Published Curriculum Objects: 37
Original Price: $22.99
Quality Status: approved
Status: Live
What You Will Learn
- Analyse and visualize data using Linear Regression
- Learn different types of linear regressions (1-dimensional and multi-dimensional models, logistic regressions, ANOVA, etc)
- Learn how to interpret and explain machine learning models
- Plot the graph of results of Linear Regression to visually analyze the results
- Assumptions of linear regression hypothesis testing
- Do feature selection and transformations to fine tune machine learning models
- Fully understand the basics of Machine Learning & Linear Regression Models from theory to practice
- Learn how to deal with the categorical data in your regression modeling and correlation between variables
- Learn the basics of R-programming
Who Should Attend
- The course is ideal for professionals who need to use regression analysis & machine learning in their field
- Everyone who would like to learn Data Science Applications In The R & R Studio Environment
Target Audiences
- The course is ideal for professionals who need to use regression analysis & machine learning in their field
- Everyone who would like to learn Data Science Applications In The R & R Studio Environment
Master Linear Regression in R: Practical Hands-On Learning
Welcome to this comprehensive course on Practical Linear Regression in R. In this course, you will dive deep into one of the most common and popular techniques in Data Science and Machine Learning: Linear Regression. You will gain both theoretical knowledge and practical skills related to different types of linear regression models. By the end of this course, you will have a complete understanding of how to apply and implement linear models in R, conduct model diagnostics, assess model fit, evaluate model performance, and make predictions.
Linear regression, despite its simplicity, is a fundamental machine learning model with profound depth, making it a valuable skill that you’ll return to throughout your career. It serves as an excellent introductory course for those taking their initial steps into the fields of:
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Machine Learning
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Deep Learning
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Data Science
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Statistics
Course Highlights:
5 Comprehensive Sections Covering Theory and Practice:
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Gain a thorough understanding of Machine Learning and Linear Regression Models, covering theory and practice.
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Apply linear regression modeling in R for various applications.
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Learn how to correctly implement, test, and evaluate linear regression models.
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Engage in programming, data science exercises, and an independent project in R.
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Master the art of assessing model fit, selecting suitable linear models for your data, and making predictions.
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Explore different types of linear regressions, including 1-dimensional and multi-dimensional models, logistic regressions, ANCOVA, and more.
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Understand how to handle categorical data in regression modeling and analyze variable correlations.
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Acquire essential R-programming skills.
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Access all scripts used throughout the course, facilitating your learning journey.
No Prerequisites Needed:
This course is designed for learners with no prior knowledge of R, statistics, or machine learning. You’ll begin with the fundamental concepts of Linear Regression and gradually progress to more complex assignments.
Practical Learning and Implementable Solutions:
Unlike other training resources, each lecture is structured to enhance your Data Science and Machine Learning skills in a demonstrable and easy-to-follow manner, providing you with practical solutions you can apply immediately.
Ideal for Professionals:
This course is tailored for professionals seeking to use cluster analysis, unsupervised machine learning, and R in their field.
Hands-On Exercises:
The course includes practical exercises, offering precise instructions and datasets for running Machine Learning algorithms using R tools.
Join This Course Today:
Unlock the potential of Linear Regression in R with this hands-on learning experience. Enroll now and elevate your Data Science and Machine Learning skills to new heights!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Introduction to Regression Analysis and Linear Regression
Lecture 3: What is Machine Leraning and it's main types?
Chapter 2: Software used in this course R-Studio and Introduction to R
Lecture 1: What is R and RStudio?
Lecture 2: How to install R and RStudio in 2020
Lecture 3: Lab: Get started with R in RStudio
Lecture 4: Read Data into R
Chapter 3: R Crash Course – get started with R-programming in R-Studio
Lecture 1: Introduction to Section
Lecture 2: Lab: Installing Packages and Package Management in R
Lecture 3: Lab: Variables in R and assigning Variables in R
Lecture 4: Overview of data types and data structures in R
Lecture 5: Lab: data types and data structures in R
Lecture 6: Vectors' operations in R
Lecture 7: Dataframes: overview in R
Lecture 8: Functions in R – overview
Chapter 4: Linear Regression in R
Lecture 1: Getting started with linear regression
Lecture 2: Lab: your first linear regression model
Lecture 3: Correlation in Regression Analysis in R: Lab
Lecture 4: How to know if the model is best fit for your data – An overview
Lecture 5: Linear Regression Diagnostics
Lecture 6: AIC and BIC
Lecture 7: Evaluation of Performance of Regression-based Prediction Model
Lecture 8: Lab: Predict with linear regression model & RMSE as in-sample error
Lecture 9: Prediction model evaluation with data split: out-of-sample RMSE
Chapter 5: More types of linear regression models in R
Lecture 1: Lab: Multiple linear regression – model estimation in R
Lecture 2: Lab: Multiple linear regression – prediction in R
Lecture 3: Lab: Multiple linear regression with interaction in R
Lecture 4: Lab: Regression with Categorical Variables: Dummy Coding Essentials in R
Lecture 5: ANOVA – Categorical variables with more than two levels in linear regressions
Lecture 6: GLM Preview: Logistic Regression Model & Accuracy Assessment
Lecture 7: Lab: Receiver operating characteristic (ROC) curve and AUC
Lecture 8: BONUS
Instructors
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Kate Alison
GIS & Data Science
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 0 votes
- 3 stars: 5 votes
- 4 stars: 13 votes
- 5 stars: 22 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!
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