Data Science in R: Regression & Classification Analysis
What you'll learn
- Your comprehensive guide to Regression Analysis & Classification for machine learning using R-programming language
- It covers theory and applications of supervised machine learning with the focus on regression & classification analysis
- Implement Machine Learning Techniques/Classification Such As Random Forests, SVM etc in R
- Build machine learning based regression & classification models and test their robustness in R
- Perform model's variable selection and assess regression model's accuracy
- Evaluate Model Performance & Learn The Best Practices For Evaluating Machine Learning Model Accuracy
- Compare different different machine learning models in R
- Learn R-programming from scratch: R crash course is included that you could start R-programming for machine learning
- Graphically representing data in R before and after analysis
- Availability computer and internet & strong interest in the topic
Regression Analysis and Classification 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 a focus on regression analysis and classification 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 apply and understand REGRESSION ANALYSIS and CLASSIFICATION (Linear Regression, Random Forest, KNN, etc) in R. We will cover many R packages incl. caret package for supervised machine learning 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 supervised Machine Learning for Regression Analysis and classification tasks
Harness applications of parametric and non-parametric regressions & classification methods in R
Learn how to apply correctly regression & classification models and test them in R
Learn how to select the best 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 MAchine Learning & 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 & Classification 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 cluster analysis, unsupervised machine learning and R 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 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