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
Requirements
- Availability computer and internet & strong interest in the topic
Description
Master Regression Analysis and Classification in R: Elevate Your Machine Learning Skills
Welcome to this comprehensive course on Regression Analysis and Classification for Machine Learning and Data Science in R. Get ready to delve into the world of supervised machine learning, specifically focusing on regression analysis and classification using the R-programming language.
What Sets This Course Apart:
Unlike other courses, this one not only provides guided demonstrations of R-scripts but also delves deep into the theoretical background. You'll gain a profound understanding of Regression Analysis and Classification (Linear Regression, Random Forest, KNN, and more) in R. We'll explore various R packages, including the caret package, for supervised machine learning tasks.
This course covers the essential aspects of practical data science, particularly Machine Learning related to regression analysis. By enrolling in this course, you'll save valuable time and resources typically spent on expensive materials related to R-based Data Science and Machine Learning.
Course Highlights:
8 Comprehensive Sections Covering Theory and Practice:
Gain a thorough understanding of supervised Machine Learning for Regression Analysis and classification tasks.
Apply parametric and non-parametric regression and classification methods effectively in R.
Learn how to correctly implement and test regression and classification models in R.
Master the art of selecting the best machine-learning model for your specific task.
Engage in coding exercises and an independent project assignment.
Acquire essential R-programming skills.
Access all scripts used throughout the course, facilitating your learning journey.
No Prerequisites Needed:
Even if you have no prior experience with R, statistics, or machine learning, this course is designed to be your complete guide. You will start with the fundamental concepts of Machine Learning and R-programming, gradually building up your skills. The course employs hands-on methods and real-world data, ensuring a smooth learning curve.
Practical Learning and Implementable Solutions:
This course is distinct from other training resources. Each lecture is structured to enhance your Regression modeling and Machine Learning skills, offering a clear and easy-to-follow path to practical implementation. You'll gain the ability to analyze diverse data streams for your projects, enhancing your value to future employers with your advanced machine-learning skills and knowledge of cutting-edge data science methods.
Ideal for Professionals:
This course is tailored for professionals who need to leverage cluster analysis, unsupervised machine learning, and R in their field.
Hands-On Exercises:
The course includes practical exercises, offering precise instructions and datasets for running Machine Learning algorithms using R tools.
Join This Course Today:
Seize the opportunity to become a master of Regression Analysis and Classification in R. Enroll now and unlock the potential of your Machine Learning and Data Science skills!
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
Instructor
- 4.3 Instructor Rating
- 5,191 Reviews
- 92,880 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.