Machine Learning in R & Predictive Models | 3 Courses in 1
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:
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Thoroughly grasp the fundamentals of Machine Learning, Cluster Analysis, and Prediction Models, moving seamlessly from theory to practice.
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Apply supervised machine learning techniques for classification and regression, as well as unsupervised machine learning techniques for cluster analysis in R.
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Learn the correct application of prediction models and how to rigorously test them within the R environment.
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Complete programming and data science tasks through an independent project centered on Supervised Machine Learning in R.
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Implement Unsupervised Clustering Techniques such as k-means Clustering and Hierarchical Clustering.
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Acquire a solid foundation in R-programming.
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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.
Software used in this course R-Studio and Introduction to R
R Crash Course - get started with R-programming in R-Studio
Fundamentals of predictive modelling with Machine Learning: Thoery
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13
Introduction to Section 3 -
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Lab: Installing Packages and Package Management in R
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15
Variables in R and assigning Variables in R
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16
Lab: Variables in R and assigning Variables in R
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17
Overview of data types and data structures in R
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18
Lab: data types and data structures in R
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19
Vectors' operations in R
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20
Data types and data structures: Factors
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21
Dataframes: overview
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22
Functions in R - overview
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23
Lab: For Loops in R
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24
Read Data into R
Unsupervised Machine Learning and Cluster Analysis in R
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25
Overview of prediction process
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26
Components of the prediction models and trade-offs in prediction
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27
Lab: your first prediction model in R
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28
Overfitting, sample errors in Machine Learning modelling in R
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29
Lab: Overfitting, sample errors in Machine Learning modelling in R
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30
Study design for predictive modelling with Machine Learning
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31
Type of Errors and how to measure them
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32
Cross Validation in Machine Learning Models
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33
Data Selection for Machine Learning models
Supervised Machine Learning in R: Classification in R
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34
Unsupervised Learning & Clustering: theory
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35
Hierarchical Clustering: Example
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36
Hierarchical Clustering: Lab
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37
Hierarchical Clustering: Merging points
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38
Heat Maps: theory
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39
Heat Maps: Lab
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40
Example K-Means Clustering in R: Lab
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41
K-means clustering: Application to email marketing
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42
Heatmaps to visualize K-Means Results in R: Examplery Lab
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43
Selecting the number of clusters for unsupervised Clustering methods (K-Means)
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44
How to assess a Clustering Tendency of the dataset
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45
Assessing the performance of unsupervised learning (clustering) algorithms
Supervised Machine Learning in R: Linear Regression Analysis
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46
Overview of functionality of Caret R-package
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47
Supervised Machine Learning & KNN: Overview
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48
Lab: Supervised classification with K Nearest Neighbours algorithm in R
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49
Classification with the KNN-algorithm -
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Theory: Confusion Matrix
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51
Lab: Calculating Classification Accuray for logistic regression model
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52
Lab: Receiver operating characteristic (ROC) curve and AUC
More types of regression models in R
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53
Overview of Regression Analysis
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54
Graphical Analysis of Regression Models
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55
Lab: your first linear regression model
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56
Correlation in Regression Analysis in R: Lab
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57
How to know if the model is best fit for your data - An overview
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58
Linear Regression Diagnostics
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59
AIC and BIC
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60
Evaluation of Prediction Model Performance in Supervised Learning: Regression
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61
Lab: Predict with linear regression model & RMSE as in-sample error
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62
Prediction model evaluation with data split: out-of-sample RMSE
Working With Non-Parametric and Non-Linear Data (Supervised Machine Learning)
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63
Lab: Multiple linear regression - model estimation
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64
Lab: Multiple linear regression - prediction
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65
Non-linear Regression Essentials in R: Polynomial and Spline Regression Models
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66
Lab: Polynomial regression in R
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67
Lab: Log transformation in R
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68
Lab: Spline regression in R
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69
Lab: Generalized additive models in R
BONUS
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70
Classification and Decision Trees (CART): Theory
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71
Lab: Decision Trees in R
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72
Random Forest: Theory
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73
Lab: Random Forest in R
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Parametrise Random Forest model
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75
Lab: Machine Learning Models' Comparison & Best Model Selection
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Predict using the best model
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77
Introduction to Model Selection Essentials in R
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78
Final Project Assignment
