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

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Kate Alison

7:37:10

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01 - Introduction

001 Introduction.mp4

02:27

002 Motivation for the course Why to use Machine Learning for Predictions.mp4

08:47

003 What is Machine Leraning and its main types.mp4

09:27

004 Overview of Machine Leraning in R.mp4

01:40

02 - Software used in this course R-Studio and Introduction to R

001 Introduction to Section 2.mp4

00:34

002 What is R and RStudio.mp4

02:43

003 How to install R and RStudio in 2021.mp4

03:41

004 Lab Install R and RStudio in 2021.mp4

05:34

005 Introduction to RStudio Interface.mp4

07:48

006 Lab Get started with R in RStudio.mp4

09:39

03 - R Crash Course - get started with R-programming in R-Studio

001 Introduction to Section 3.mp4

01:04

002 Lab Installing Packages and Package Management in R.mp4

04:20

003 Variables in R and assigning Variables in R.mp4

02:19

004 Lab Variables in R and assigning Variables in R.mp4

01:45

005 Overview of data types and data structures in R.mp4

08:20

006 Lab data types and data structures in R.mp4

09:58

007 Vectors operations in R.mp4

07:38

008 Data types and data structures Factors.mp4

02:32

009 Dataframes overview.mp4

03:22

010 Functions in R - overview.mp4

04:50

011 Lab For Loops in R.mp4

03:57

012 Read Data into R.mp4

05:15

Files.zip

04 - Fundamentals of predictive modelling with Machine Learning Thoery

001 Overview of prediction process.mp4

17:55

002 Components of the prediction models and trade-offs in prediction.mp4

13:56

003 Lab your first prediction model in R.mp4

06:44

004 Overfitting, sample errors in Machine Learning modelling in R.mp4

14:21

005 Lab Overfitting, sample errors in Machine Learning modelling in R.mp4

04:31

006 Study design for predictive modelling with Machine Learning.mp4

10:42

007 Type of Errors and how to measure them.mp4

10:12

008 Cross Validation in Machine Learning Models.mp4

07:09

009 Data Selection for Machine Learning models.mp4

04:19

Files.zip

05 - Unsupervised Machine Learning and Cluster Analysis in R

001 Unsupervised Learning & Clustering theory.mp4

05:31

002 Hierarchical Clustering Example.mp4

07:34

003 Hierarchical Clustering Lab.mp4

02:08

004 Hierarchical Clustering Merging points.mp4

02:40

005 Heat Maps theory.mp4

08:33

006 Heat Maps Lab.mp4

04:30

007 Example K-Means Clustering in R Lab.mp4

05:32

008 K-means clustering Application to email marketing.mp4

14:37

009 Heatmaps to visualize K-Means Results in R Examplery Lab.mp4

04:22

010 Selecting the number of clusters for unsupervised Clustering methods (K-Means).mp4

09:35

011 How to assess a Clustering Tendency of the dataset.mp4

05:28

012 Assessing the performance of unsupervised learning (clustering) algorithms.mp4

07:18

Files.zip

06 - Supervised Machine Learning in R Classification in R

001 Overview of functionality of Caret R-package.mp4

04:51

002 Supervised Machine Learning & KNN Overview.mp4

04:03

003 Lab Supervised classification with K Nearest Neighbours algorithm in R.mp4

08:24

005 Theory Confusion Matrix.mp4

05:41

006 Lab Calculating Classification Accuray for logistic regression model.mp4

09:54

007 Lab Receiver operating characteristic (ROC) curve and AUC.mp4

06:13

Files.zip

07 - Supervised Machine Learning in R Linear Regression Analysis

001 Overview of Regression Analysis.mp4

10:36

002 Graphical Analysis of Regression Models.mp4

05:41

003 Lab your first linear regression model.mp4

08:07

004 Correlation in Regression Analysis in R Lab.mp4

02:40

005 How to know if the model is best fit for your data - An overview.mp4

02:21

006 Linear Regression Diagnostics.mp4

07:12

007 AIC and BIC.mp4

01:49

008 Evaluation of Prediction Model Performance in Supervised Learning Regression.mp4

02:32

009 Lab Predict with linear regression model & RMSE as in-sample error.mp4

04:23

010 Prediction model evaluation with data split out-of-sample RMSE.mp4

05:00

Files.zip

08 - More types of regression models in R

001 Lab Multiple linear regression - model estimation.mp4

08:49

002 Lab Multiple linear regression - prediction.mp4

03:13

003 Non-linear Regression Essentials in R Polynomial and Spline Regression Models.mp4

04:55

004 Lab Polynomial regression in R.mp4

10:14

005 Lab Log transformation in R.mp4

03:17

006 Lab Spline regression in R.mp4

06:58

007 Lab Generalized additive models in R.mp4

06:44

Files.zip

09 - Working With Non-Parametric and Non-Linear Data (Supervised Machine Learning)

001 Classification and Decision Trees (CART) Theory.mp4

04:02

002 Lab Decision Trees in R.mp4

06:32

003 Random Forest Theory.mp4

03:59

004 Lab Random Forest in R.mp4

12:30

006 Lab Machine Learning Models' Comparison & Best Model Selection.mp4

14:40

008 Introduction to Model Selection Essentials in R.mp4

02:00

009 Final Project Assignment.mp4

02:20

Files.zip

10 - BONUS

001 BONUS.mp4

02:13

Description

Supervised & unsupervised machine learning in R, clustering in R, predictive models in R by many labs, understand theory

What You'll 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 is this 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

What You Need to Know?

Availability computer and internet & strong interest in the topic

More details

Description
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.
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

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Machine Learning

Kate Alison

Instructor's Courses

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