Regression Analysis in R for Data Science: from Zero to Hero
Regression Analysis in R for Data Science: from Zero to Hero, available at $59.99, has an average rating of 4.75, with 50 lectures, 5 quizzes, based on 54 reviews, and has 9583 subscribers.
You will learn about 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 This course is ideal for individuals who are The course is ideal for professionals who need to use regression analysis & supervised machine learning in their field or Everyone who would like to learn Data Science Applications In The R & R Studio Environment or Everyone who would like to learn theory and implementation of Regression Analysis & Machine Learning On Real-World Data It is particularly useful for The course is ideal for professionals who need to use regression analysis & supervised machine learning in their field or Everyone who would like to learn Data Science Applications In The R & R Studio Environment or Everyone who would like to learn theory and implementation of Regression Analysis & Machine Learning On Real-World Data.
Enroll now: Regression Analysis in R for Data Science: from Zero to Hero
Summary
Title: Regression Analysis in R for Data Science: from Zero to Hero
Price: $59.99
Average Rating: 4.75
Number of Lectures: 50
Number of Quizzes: 5
Number of Published Lectures: 50
Number of Published Quizzes: 5
Number of Curriculum Items: 55
Number of Published Curriculum Objects: 55
Original Price: $27.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- 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
Target Audiences
- 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
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:
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Fully understand the basics of Regression Analysis, including parametric and non-parametric methods.
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Apply parametric and non-parametric regression techniques in R.
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Learn to accurately implement regression models and assess them in R.
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Discover how to select the most suitable statistical and machine learning models for your specific tasks.
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Engage in coding exercises and an independent project assignment.
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Acquire fundamental R-programming skills.
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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!
Course Curriculum
Chapter 1: Introduction to the course, Machine Learning & Regression Analysis
Lecture 1: Introduction
Lecture 2: Introduction to Regression Analysis
Lecture 3: What is Machine Leraning and it's main types?
Lecture 4: Overview of Machine Leraning in R
Chapter 2: Software used in this course R-Studio and Introduction to R
Lecture 1: Introduction to Section 2
Lecture 2: What is R and RStudio?
Lecture 3: How to install R and RStudio in 2020
Lecture 4: Lab: Install R and RStudio in 2020
Lecture 5: Introduction to RStudio Interface
Lecture 6: Lab: Get started with R in RStudio
Chapter 3: R Crash Course – get started with R-programming in R-Studio
Lecture 1: Introduction to Section 3
Lecture 2: Lab: Installing Packages and Package Management in R
Lecture 3: Variables in R and assigning Variables in R
Lecture 4: Lab: Variables in R and assigning Variables in R
Lecture 5: Overview of data types and data structures in R
Lecture 6: Lab: data types and data structures in R
Lecture 7: Vectors' operations in R
Lecture 8: Data types and data structures: Factors
Lecture 9: Dataframes: overview
Lecture 10: Functions in R – overview
Lecture 11: Lab: For Loops in R
Lecture 12: Read Data into R
Chapter 4: Linear Regression Analysis for Supervised Machine Learning in R
Lecture 1: Overview of Regression Analysis
Lecture 2: Graphical Analysis of Regression Models
Lecture 3: Your first linear regression model in R
Lecture 4: Lab: Correlation & Linear Regression Analysis in R
Lecture 5: How to know if the model is best fit for your data – theory
Lecture 6: Lab: Linear Regression Diagnostics
Lecture 7: Lab how to measure the linear model's fit: AIC and BIC
Lecture 8: Evaluation of Prediction Model Performance in Supervised Learning: Regression
Lecture 9: Predict with linear regression model & RMSE as in-sample error
Lecture 10: Prediction model evaluation with data split: out-of-sample RMSE
Chapter 5: More types of regression models
Lecture 1: Lab: Multiple linear regression – model estimation
Lecture 2: Lab: Multiple linear regression – prediction
Lecture 3: Lab: Multiple linear regression with interaction
Lecture 4: Regression with Categorical Variables: Dummy Coding Essentials in R
Lecture 5: ANOVA – Categorical variables with more than two levels in linear regressions
Chapter 6: Non-Linear Regression Analysis in R: Polynomial & Spline regression, GAMs
Lecture 1: Nonlinear Regression Essentials in R: Polynomial and Spline Regression Models
Lecture 2: Lab: Polynomial regression in R
Lecture 3: Lab: Log transformation in R
Lecture 4: Lab: Spline regression in R
Lecture 5: Lab: Generalized additive models in R
Chapter 7: Non-Parametric Regression Analysis in R: Random Forest, Decision Trees and more
Lecture 1: Classification and Decision Trees (CART): Theory
Lecture 2: Lab: Decision Trees in R
Lecture 3: Random Forest: Theory
Lecture 4: Lab: Random Forest in R
Lecture 5: Lab: Machine Learning Models' Comparison & Best Model Selection
Lecture 6: Introduction to Model Selection Essentials in R
Lecture 7: Your Final Project
Lecture 8: BONUS
Instructors
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Kate Alison
GIS & Data Science -
Georg Müller
Data Science Experte
Rating Distribution
- 1 stars: 2 votes
- 2 stars: 2 votes
- 3 stars: 4 votes
- 4 stars: 8 votes
- 5 stars: 38 votes
Frequently Asked Questions
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