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Machine Learning in R & Predictive Models | 3 Courses in 1

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Machine Learning in R & Predictive Models | 3 Courses in 1, available at $64.99, has an average rating of 4.53, with 74 lectures, 5 quizzes, based on 149 reviews, and has 20065 subscribers.

You will learn about Your complete guide to unsupervised & supervised machine learning and predictive modeling using R-programming language It covers both theoretical background of MACHINE LERANING & and predictive modeling as well as practical examples in R and R-Studio Fully understand the basics of Machine Learning, Cluster Analysis & Predictive Modelling Highly practical data science examples related to supervised machine learning, clustering & prediction modelling in R Learn R-programming from scratch: R crash course is included that you could start R-programming for machine learning Be Able To Harness The Power of R For Practical Data Science Compare different different machine learning algorithms for regression & classification modelling Apply statistical and machine learning based regression & classification models to real data Build machine learning based regression & classification models and test their robustness in R Learn when and how machine learning & predictive models should be correctly applied Test your skills with multiple coding exercices and final project that you will ommplement independently Implement Machine Learning Techniques/Classification Such As Random Forests, SVM etc in R You'll have a copy of the scripts used in the course for your reference to use in your 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: Machine Learning in R & Predictive Models | 3 Courses in 1

Summary

Title: Machine Learning in R & Predictive Models | 3 Courses in 1

Price: $64.99

Average Rating: 4.53

Number of Lectures: 74

Number of Quizzes: 5

Number of Published Lectures: 74

Number of Published Quizzes: 5

Number of Curriculum Items: 79

Number of Published Curriculum Objects: 79

Original Price: $59.99

Quality Status: approved

Status: Live

What You Will Learn

  • Your complete guide to unsupervised & supervised machine learning and predictive modeling using R-programming language
  • It covers both theoretical background of MACHINE LERANING & and predictive modeling as well as practical examples in R and R-Studio
  • Fully understand the basics of Machine Learning, Cluster Analysis & Predictive Modelling
  • Highly practical data science examples related to supervised machine learning, clustering & prediction modelling in R
  • Learn R-programming from scratch: R crash course is included that you could start R-programming for machine learning
  • Be Able To Harness The Power of R For Practical Data Science
  • Compare different different machine learning algorithms for regression & classification modelling
  • Apply statistical and machine learning based regression & classification models to real data
  • Build machine learning based regression & classification models and test their robustness in R
  • Learn when and how machine learning & predictive models should be correctly applied
  • Test your skills with multiple coding exercices and final project that you will ommplement independently
  • Implement Machine Learning Techniques/Classification Such As Random Forests, SVM etc in R
  • You'll have a copy of the scripts used in the course for your reference to use in your 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

Welcome to the Ultimate Machine Learning Course in R

If you’re looking to master the theory and application of supervised & unsupervised machine learning and predictive modeling using R, you’ve come to the right place. This comprehensive course merges the content of three separate courses: R Programming, Machine Learning, and Predictive Modeling, to provide you with a holistic understanding of these topics.

What Sets This Course Apart?

Unlike other courses, this one goes beyond mere script demonstrations. We delve into the theoretical foundations, ensuring that you not only learn how to use R-scripts but also fully comprehend the underlying concepts. By the end, you’ll be equipped to confidently apply Machine Learning & Predictive Models (including K-means, Random Forest, SVM, and logistic regression) in R. We’ll cover numerous R packages, including the caret package.

Comprehensive Coverage

This course covers every essential aspect of practical data science related to Machine Learning, spanning classification, regression, and unsupervised clustering techniques. By enrolling, you’ll save valuable time and resources that might otherwise be spent on costly materials in the field of R-based Data Science and Machine Learning.

Unlock Career Opportunities

In today’s age of big data, companies worldwide rely on R for in-depth data analysis, aiding both business and research endeavors. By becoming proficient in supervised & unsupervised machine learning and predictive modeling in R, you can set yourself apart in your field and propel your career to new heights.

Course Highlights:

  • Thoroughly grasp the fundamentals of Machine Learning, Cluster Analysis, and Prediction Models, moving seamlessly from theory to practice.

  • Apply supervised machine learning techniques for classification and regression, as well as unsupervised machine learning techniques for cluster analysis in R.

  • Learn the correct application of prediction models and how to rigorously test them within the R environment.

  • Complete programming and data science tasks through an independent project centered on Supervised Machine Learning in R.

  • Implement Unsupervised Clustering Techniques such as k-means Clustering and Hierarchical Clustering.

  • Acquire a solid foundation in R-programming.

  • Gain access to all the scripts used throughout the course and more.

No Prerequisites Needed

Even if you have no prior knowledge of R, statistics, or machine learning, this course is designed to be beginner-friendly. We start with the most fundamental Machine Learning, Predictive Modeling, and Data Science basics, gradually building your skills through hands-on exercises. Whether you’re a novice or need a refresher, this course provides a comprehensive introduction to R and R programming.

A Different Approach

This course stands out from other training resources. Each lecture strives to enhance your Machine Learning and modeling skills through clear and practical demonstrations. You’ll gain the tools and knowledge to analyze various data streams for your projects, earning recognition from future employers for your improved machine learning skills and expertise in cutting-edge data science methods.

Ideal for Professionals

This course is perfect for professionals seeking to use cluster analysis, unsupervised machine learning, and R in their respective fields. Whether you’re looking to advance your career or tackle specific data science challenges, this course equips you with the skills and practical experience needed to excel.

Hands-On Practical Exercises

A key component of this course is hands-on practical exercises. You’ll receive precise instructions and datasets to run Machine Learning algorithms using R tools, ensuring you gain valuable experience in applying what you’ve learned.

Join this Course Now

Don’t miss out on this opportunity to elevate your Machine Learning and Predictive Modeling skills. Enroll in this comprehensive course today and take the first step toward mastering these critical data science techniques in R.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Motivation for the course: Why to use Machine Learning for Predictions?

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 2021

Lecture 4: Lab: Install R and RStudio in 2021

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: Fundamentals of predictive modelling with Machine Learning: Thoery

Lecture 1: Overview of prediction process

Lecture 2: Components of the prediction models and trade-offs in prediction

Lecture 3: Lab: your first prediction model in R

Lecture 4: Overfitting, sample errors in Machine Learning modelling in R

Lecture 5: Lab: Overfitting, sample errors in Machine Learning modelling in R

Lecture 6: Study design for predictive modelling with Machine Learning

Lecture 7: Type of Errors and how to measure them

Lecture 8: Cross Validation in Machine Learning Models

Lecture 9: Data Selection for Machine Learning models

Chapter 5: Unsupervised Machine Learning and Cluster Analysis in R

Lecture 1: Unsupervised Learning & Clustering: theory

Lecture 2: Hierarchical Clustering: Example

Lecture 3: Hierarchical Clustering: Lab

Lecture 4: Hierarchical Clustering: Merging points

Lecture 5: Heat Maps: theory

Lecture 6: Heat Maps: Lab

Lecture 7: Example K-Means Clustering in R: Lab

Lecture 8: K-means clustering: Application to email marketing

Lecture 9: Heatmaps to visualize K-Means Results in R: Examplery Lab

Lecture 10: Selecting the number of clusters for unsupervised Clustering methods (K-Means)

Lecture 11: How to assess a Clustering Tendency of the dataset

Lecture 12: Assessing the performance of unsupervised learning (clustering) algorithms

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

Lecture 1: Overview of functionality of Caret R-package

Lecture 2: Supervised Machine Learning & KNN: Overview

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

Lecture 4: Theory: Confusion Matrix

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

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

Chapter 7: Supervised Machine Learning in R: Linear Regression Analysis

Lecture 1: Overview of 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 Prediction Model Performance in Supervised Learning: Regression

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 8: More types of regression models in R

Lecture 1: Lab: Multiple linear regression – model estimation

Lecture 2: Lab: Multiple linear regression – prediction

Lecture 3: Non-linear 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

Chapter 9: 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: Introduction to Model Selection Essentials in R

Lecture 7: Final Project Assignment

Chapter 10: BONUS

Lecture 1: BONUS

Instructors

Rating Distribution

  • 1 stars: 3 votes
  • 2 stars: 8 votes
  • 3 stars: 15 votes
  • 4 stars: 28 votes
  • 5 stars: 95 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!

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