Data Science in R- Regression Classification Analysis

Data Science in R: Regression & Classification Analysis, available at $59.99, has an average rating of 3.9, with 46 lectures, 7 quizzes, based on 37 reviews, and has 8164 subscribers.

You will learn about 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 models variable selection and assess regression models 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 This course is ideal for individuals who are The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning and R 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 Machine Learning On Real-World Data It is particularly useful for The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning and R 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 Machine Learning On Real-World Data.

Enroll now: Data Science in R: Regression & Classification Analysis

Summary

Title: Data Science in R: Regression & Classification Analysis

Price: $59.99

Average Rating: 3.9

Number of Lectures: 46

Number of Quizzes: 7

Number of Published Lectures: 46

Number of Published Quizzes: 7

Number of Curriculum Items: 53

Number of Published Curriculum Objects: 53

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will 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 models variable selection and assess regression models 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

Who Should Attend

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

Target Audiences

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

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What is Machine Leraning and its main types?

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: Lab: Install R and RStudio in 2020

Lecture 4: 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

Lecture 2: Lab: Installing Packages and Package Management in R

Lecture 3: Lab: Variables in R and assigning Variables in R

Lecture 4: Overview of data types and data structures in R

Lecture 5: Lab: data types and data structures in R

Lecture 6: Vectors operations in R

Lecture 7: Dataframes: overview in R

Lecture 8: Functions in R – overview

Lecture 9: Read Data into R

Chapter 4: Linear Regression in R

Lecture 1: Introduction to Regression Analysis

Lecture 2: Graphical Analysis of Regression Models

Lecture 3: Lab: your first linear regression model

Lecture 4: Correlation in Regression Analysis in R: Lab

Lecture 5: How to know if the model is best fit for your data – An overview

Lecture 6: Linear Regression Diagnostics

Lecture 7: AIC and BIC

Lecture 8: Evaluation of Performance of Regression-based Prediction Model

Lecture 9: Lab: 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 in R

Lecture 1: Lab: Multiple linear regression – model estimation

Lecture 2: Lab: Multiple linear regression – prediction

Lecture 3: Nonlinear Regression Essentials in R: Polynomial and Spline Regression Models

Lecture 4: Lab: Polynomial regression in R

Lecture 5: Lab: Log transformation in R

Lecture 6: Lab: Spline regression in R

Lecture 7: Lab: Generalized additive models in R

Lecture 8: Introduction to Model Selection Essentials in R

Chapter 6: Supervised Machine Learning in R: Classification in R

Lecture 1: Supervised Machine Learning & KNN: Overview

Lecture 2: Overview of functionality of Caret R-package

Lecture 3: Lab: Supervised classification with K Nearest Neighbours algorithm in R

Lecture 4: Theory: Confusion Matrix

Lecture 5: Lab: Calculating Classification Accuracy for logistic regression model

Lecture 6: Lab: Receiver operating characteristic (ROC) curve and AUC

Chapter 7: Working With Non-Parametric and Non-Linear Data (Supervised Machine Learning)

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: Final Project Assignment

Lecture 7: BONUS

Instructors

Kate Alison
GIS & Data Science

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 8 votes

4 stars: 10 votes

5 stars: 18 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!