Practical Linear Regression in R for Data Science in R
What you'll 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
Requirements
- Availabiliy computer and internet & strong interest in the topic
Description
Practical Linear Regression in R - Hands-On
This course teaches you about the most common & popular technique used in Data Science & Machine Learning: Linear Regression. You will learn the theory as well as applications of different types of linear regression models. At the end of the course, you will completely understand and know how to apply & implement in R linear models, how to run model's diagnostics, and how to know if the model is the best fit for your data, how to check the model's performance and to make predictions.
Linear regression is the simplest machine learning (and thus deep learning) model you can learn, yet there is so much depth that you'll be returning to it for years to come. That's why it's a great introductory course if you're interested in taking your first steps in the fields of:
machine learning
deep learning
data science
statistics
THIS COURSE HAS 5 SECTIONS COVERING EVERY ASPECT OF LINEAR REGRESSION: BOTH THEORY TO PRACTICE
Fully understand the basics of Machine Learning & Linear Regression Models from theory to practice
Harness applications of linear regression modeling in R
Learn how to apply correctly linear regression models and test them in R
Complete programming & data science exercises and an independent project in R
Learn how to test the model's fit, how to select the most suitable linear models for your data, and make predictions
Learn different types of linear regressions (1-dimensional and multi-dimensional models, logistic regressions, ANCOVA, etc)
Learn how to deal with the categorical data in your regression modeling and correlation between variables
Learn the basics of R-programming
Get a copy of all scripts used in the course
and MORE
NO PRIOR R OR STATISTICS/MACHINE LEARNING / R KNOWLEDGE REQUIRED:
You’ll start by absorbing the most valuable Linear Regression basics, and techniques and slowly moving to more complex assignments.
My course will help you implement the methods using real data obtained from different sources. Thus, after completing my Machine Learning course in R, you’ll easily use different data streams and data science packages to work with real data in R.
This course is different from other training resources. Each lecture seeks to enhance your Data Science & Machine Learning in a demonstrable and easy-to-follow manner and provide you with practically implementable solutions.
The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning, and R in their field.
One important part of the course is the practical exercises. You will be given some precise instructions and datasets to run Machine Learning algorithms using the R tools.
JOIN MY COURSE NOW!
Who this course is for:
- 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
Instructor
- 4.3 Instructor Rating
- 2,828 Reviews
- 76,332 Students
- 34 Courses
I am a passionate data science expert and educator. I do regular teaching and training all over the world. I have many satisfied students! And now I will be glad if I can teach also you these interesting, highly applied, and exciting topics!
For GIS & Remote Sensing students:
Order of how to take my courses:
Option 1: Take all individual courses that contain more details and more labs in the following order:
1. Get started with GIS & Remote Sensing in QGIS #Beginners
2. Remote Sensing in QGIS: Fundamentals of Image Analysis 2020
3. Core GIS: Land Use and Land Cover & Change Detection in QGIS
4. Machine Learning in GIS: Understand the Theory and Practice
5. Machine Learning in GIS: Land Use/Land Cover Image Analysis
6. Machine Learning in ArcGIS: Map Land Use/ Land Cover in GIS
7. Object-based image analysis & classification in QGIS/ArcGIS
8. ArcGIS: Learn Deep Learning in ArcGIS to advance GIS skills
8. Google Earth Engine for Big GeoData Analysis: 3 Courses in 1
10. Google Earth Engine for Machine Learning & Change Detection
11. QGIS & Google Earth Engine for Environmental Applications
12. Advanced Remote Sensing Analysis in QGIS and on cloud
Option 2: Take my combi-courses that contain summarized information from the above courses, though in fewer details (labs, videos):
1. Geospatial Data Analyses & Remote Sensing: 4 Classes in 1
2. Machine Learning in GIS and Remote Sensing: 5 Courses in 1
3. Google Earth Engine for Big GeoData Analysis: 3 Courses in 1
4. Google Earth Engine for Machine Learning & Change Detection
5. Advanced Remote Sensing Analysis in QGIS and on cloud