Regression Analysis in R for Data Science: from Zero to Hero
What you'll learn
- Your comprehensive guide to Regression Analysis & supervised machine learning using R-programming language
- Graphically representing data in R before and after analysis
- It covers the theory and applications of supervised machine learning with the focus on regression analysis using the R-programming language in R-Studio
- Implement Ordinary Least Square (or simple linear) regression, Random FOrest Regression, Decision Trees, Logistic regression and others using R
- Perform model's variable selection and assess regression model's accuracy
- Build machine learning based regression models and test their performance in R
- Compare different different machine learning models for regression tasks in R
- Learn how to select the best statistical & machine learning model for your task
- Learn when and how machine learning models should be applied
- Carry out coding exercises & your independent project assignment
- Availabiliy computer and internet & strong interest in the topic
Regression Analysis for Machine Learning & Data Science in R
My course will be your hands-on guide to the theory and applications of supervised machine learning with the focus on regression analysis using the R-programming language.
Unlike other courses, it offers NOT ONLY the guided demonstrations of the R-scripts but also covers theoretical background that will allow you to FULLY UNDERSTAND & APPLY REGRESSION ANALYSIS (Linear Regression, Random Forest, KNN, etc) in R (many R packages incl. caret package will be covered) for supervised machine learning and prediction tasks.
This course also covers all the main aspects of practical and highly applied data science related to Machine Learning (i.e. regression analysis). Thus, if you take this course, you will save lots of time & money on other expensive materials in the R based Data Science and Machine Learning domain.
THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF MACHINE LEARNING: BOTH THEORY & PRACTISE
Fully understand the basics of Regression Analysis (parametric & non-parametric methods) & supervised Machine Learning from theory to practice
Harness applications of parametric and non-parametric regressions in R
Learn how to apply correctly regression models and test them in R
Learn how to select the best statistical & machine learning model for your task
Carry out coding exercises & your independent project assignment
Learn the basics of R-programming
Get a copy of all scripts used in the course
NO PRIOR R OR STATISTICS/MACHINE LEARNING / R KNOWLEDGE REQUIRED:
You’ll start by absorbing the most valuable Regression Analysis & R-programming basics, and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
My course will help you implement the methods using real data obtained from different sources. Thus, after completing my Regression Analysis for Machine Learning in R course, you’ll easily use different data streams and data science packages to work with real data in R.
In case it is your first encounter with R, don’t worry, my course a full introduction to the R & R-programming in this course.
This course is different from other training resources. Each lecture seeks to enhance your Regression modeling and Machine Learning skills in a demonstrable and easy-to-follow manner and provide you with practically implementable solutions. You’ll be able to start analyzing different streams of data for your projects and gain appreciation from your future employers with your improved machine learning skills and knowledge of cutting edge data science methods.
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 & supervised machine learning in their field
- Everyone who would like to learn Data Science Applications In The R & R Studio Environment
- Everyone who would like to learn theory and implementation of Regression Analysis & Machine Learning On Real-World Data
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
Ich bin ein erfahrener Berater und Experte in Data Science. Ich habe mein MSc in Informatik an der TH Köln und MBA an der Universität Durham (UK) erlangt und habe mich später im Fachbereich Informatik promoviert. Als erfahrene Trainer mit mehr als 15 Jahren Berufserfahrung möchte ich meine Leidenschaft, praktische Erfahrungen und Kenntnisse in den Themen Big Data, Data Science, Data Analytics und IT management mit den anderen teilen und die praktische Kompetenzen von meinen Studenten auf ein sehr hohes Niveau bringen.