Practical Linear Regression in R for Data Science in R
Learn Practical Linear Regression in R - Basics of machine learning, deep learning, statistics & Artificial Intellegence
Ratings 4.28 / 5.00
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
Description
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:
Machine Learning
Deep Learning
Data Science
Statistics
Course Highlights:
5 Comprehensive Sections Covering Theory and Practice:
Gain a thorough understanding of Machine Learning and Linear Regression Models, covering theory and practice.
Apply linear regression modeling in R for various applications.
Learn how to correctly implement, test, and evaluate linear regression models.
Engage in programming, data science exercises, and an independent project in R.
Master the art of assessing model fit, selecting suitable linear models for your data, and making predictions.
Explore different types of linear regressions, including 1-dimensional and multi-dimensional models, logistic regressions, ANCOVA, and more.
Understand how to handle categorical data in regression modeling and analyze variable correlations.
Acquire essential R-programming skills.
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!
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
