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
Requirements
- Availabiliy computer and internet & strong interest in the topic
Description
Master Regression Analysis in R for Machine Learning & Data Science
Welcome to this comprehensive course on Regression Analysis for Machine Learning & Data Science in R. This course is designed to be your hands-on guide to understanding, applying, and mastering supervised machine learning techniques, with a primary focus on regression analysis using the R-programming language.
Course Highlights:
Theory and Practical Applications:
This course stands out by offering more than just guided demonstrations of R-scripts. It dives deep into the theoretical background, providing you with a comprehensive understanding of regression analysis. You'll not only apply machine learning models but also gain the knowledge required to fully comprehend and utilize regression analysis techniques such as Linear Regression, Random Forest, K-Nearest Neighbors (KNN), and more using R. We will cover various R packages, including the caret package, to enrich your skill set.
Comprehensive Coverage:
This course covers all essential aspects of practical data science related to Machine Learning, specifically focusing on regression analysis. By enrolling in this course, you'll save both time and money, as you won't need to invest in expensive materials related to R-based Data Science and Machine Learning.
Course Outline:
The course spans 8 sections, ensuring comprehensive coverage of both theory and practice. You'll:
Fully understand the basics of Regression Analysis, including parametric and non-parametric methods.
Apply parametric and non-parametric regression techniques in R.
Learn to accurately implement regression models and assess them in R.
Discover how to select the most suitable statistical and machine learning models for your specific tasks.
Engage in coding exercises and an independent project assignment.
Acquire fundamental R-programming skills.
Gain access to all scripts used throughout the course.
No Prior Knowledge Required:
This course is tailored for individuals with no prior knowledge of R, statistics, or machine learning. It starts with foundational concepts and gradually progresses to more complex topics.
Practical Learning and Implementable Solutions:
Unlike other training resources, each lecture aims to enhance your Regression modeling and Machine Learning skills through practical and easy-to-follow methods, providing you with solutions that you can readily apply.
Ideal for Professionals:
This course is ideal for professionals who need to incorporate cluster analysis, unsupervised machine learning, and R into their work.
Hands-On Exercises:
Practical exercises are a significant part of this course. You'll receive precise instructions and datasets to run Machine Learning algorithms using R tools.
Join This Course Today:
Unlock the potential of Regression Analysis in R and elevate your Machine Learning and Data Science skills. Enroll now to embark on your learning journey!
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
Instructors
- 4.3 Instructor Rating
- 6,061 Reviews
- 96,221 Students
- 34 Courses
Welcome to the World of Geospatial & Data Science Education!
Are you ready to embark on an exciting journey into the realms of GIS, Remote Sensing, Machine Learning, and Data Science? I'm your dedicated instructor, a passionate data science expert and educator, committed to providing you with a world-class education in these highly applied and captivating fields.
About Me:
With a wealth of experience in teaching and training around the globe, I've had the privilege of educating numerous students who have achieved remarkable success. Now, I'm thrilled at the opportunity to share my expertise with you.
For Aspiring GIS & Remote Sensing Enthusiasts:
I understand that choosing the right path to acquire knowledge is crucial, and I've designed a structured learning journey for you. You have two options to tailor your educational experience according to your needs and preferences.
Option 1: In-Depth Exploration
If you're keen on delving deep into each topic with comprehensive details and hands-on labs, here's the recommended order for taking my individual courses:
- Get started with GIS & Remote Sensing in QGIS #Beginners
- Remote Sensing in QGIS: Fundamentals of Image Analysis 2020
- Core GIS: Land Use and Land Cover & Change Detection in QGIS
- Machine Learning in GIS: Understand the Theory and Practice
- Machine Learning in GIS: Land Use/Land Cover Image Analysis
- Machine Learning in ArcGIS: Map Land Use/ Land Cover in GIS
- Object-based image analysis & classification in QGIS/ArcGIS
- ArcGIS: Learn Deep Learning in ArcGIS to advance GIS skills
- QGIS & Google Earth Engine for Environmental Applications
- Advanced Remote Sensing Analysis in QGIS and on cloud
- Explore specialized courses focused on specific Remote Sensing applications in my course list.
Option 2: Comprehensive Combi-Courses
For a more consolidated approach, where you receive summarized information from the individual courses, along with fewer details (labs and videos), you can opt for the following combi-courses:
- QGIS Mega Course: GIS and Remote Sensing - Beginner to Expert
- Geospatial Data Analyses & Remote Sensing: 4 Classes in 1
- Machine Learning in GIS and Remote Sensing: 5 Courses in 1
- Google Earth Engine for Big GeoData Analysis: 3 Courses in 1
- Google Earth Engine for Machine Learning & Change Detection
Data Science with Geospatial Analysis Bundle:
This bundle comprises a selection of courses that will empower you to excel in the world of Data Science while leveraging the insights gained from geospatial analysis:
- Geospatial Data Analyses & Remote Sensing: 4 Classes in 1
- Machine Learning in GIS and Remote Sensing: 5 Courses in 1
- Google Earth Engine for Big GeoData Analysis: 3 Courses in 1
- Google Earth Engine for Machine Learning & Change Detection
Your Journey Starts Here:
No matter which path you choose, you're embarking on an enriching educational journey that will equip you with the knowledge and skills needed to excel in the world of GIS, Remote Sensing, Machine Learning, and Data Science. Let's get started on this exciting adventure together!
Join me in unlocking the endless possibilities of geospatial analysis, remote sensing, machine learning, and data science. Enroll in your preferred course or bundle today.
- 4.2 Instructor Rating
- 1,388 Reviews
- 45,760 Students
- 10 Courses
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.