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Regression Analysis in R for Data Science: from Zero to Hero

Learn Complete Hands-On Regression Analysis in R for Machine Learning, Statistical Analysis, Data Science, Deep Learning
3.9
3.9/5
(43 reviews)
8,498 students
Created by

8.8

Classbaze Grade®

9.5

Freshness

6.8

Popularity

9.4

Material

Learn Complete Hands-On Regression Analysis in R for Machine Learning
Platform: Udemy
Video: 4h 30m
Language: English
Next start: On Demand

Best Regression Analysis classes:

Classbaze Rating

Classbaze Grade®

8.8 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness

9.5 / 10
This course was last updated on 11/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Popularity

6.8 / 10
We analyzed factors such as the rating (3.9/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Material

9.4 / 10
Video Score: 8.2 / 10
The course includes 4h 30m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 5 hours 01 minutes of 30 Regression Analysis courses on Udemy.
Detail Score: 10.0 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.9 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
18 resources.
1 exercises.
0 test.

In this page

About the course

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
•and MORE
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!

What can you learn from this course?

✓ 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

What you need to start the course?

• Availabiliy computer and internet & strong interest in the topic

Who is this course is made 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

Are there coupons or discounts for Regression Analysis in R for Data Science: from Zero to Hero ? What is the current price?

The course costs $14.99. And currently there is a 50% discount on the original price of the course, which was $29.99. So you save $15 if you enroll the course now.
The average price is $15.1 of 30 Regression Analysis courses. So this course is 1% cheaper than the average Regression Analysis course on Udemy.

Will I be refunded if I'm not satisfied with the Regression Analysis in R for Data Science: from Zero to Hero course?

YES, Regression Analysis in R for Data Science: from Zero to Hero has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any financial aid for this course?

Currently we could not find a scholarship for the Regression Analysis in R for Data Science: from Zero to Hero course, but there is a $15 discount from the original price ($29.99). So the current price is just $14.99.

Who will teach this course? Can I trust Kate Alison?

Kate Alison has created 34 courses that got 2,880 reviews which are generally positive. Kate Alison has taught 76,591 students and received a 4.3 average review out of 2,880 reviews. Depending on the information available, we think that Kate Alison is an instructor that you can trust.
GIS & Data Science
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
Browse all courses by on Classbaze.

8.8

Classbaze Grade®

9.5

Freshness

6.8

Popularity

9.4

Material

Platform: Udemy
Video: 4h 30m
Language: English
Next start: On Demand

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