Machine Learning with R (Udemy.com)

Master R Programming for Machine Learning & Statistical Learning

Data is everywhere, and top organizations urgently need professionals who can turn raw datasets into predictive, data-driven decisions. According to SAS, mastering analytics and machine learning gives you a massive career advantage by sharpening your problem-solving abilities, opening doors to high-demand technical roles, and unlocking opportunities in cutting-edge fields like the Internet of Things (IoT) and Smart Cities.

This bite-sized, practical course focuses on Machine Learning and Statistical Learning using R, mapping directly to the Modeling and Evaluation stages of the industry-standard CRISP-DM framework.

Why Take This Course?

• Hands-On Predictive Modeling: Master key supervised and unsupervised machine learning algorithms in R, including Decision Trees, Naïve Bayes, KNN, Neural Networks, Linear Regression, and Clustering.

• Rigorous Model Evaluation: Learn how to split datasets into train/test sets, generate predictions, and evaluate performance for both classification and regression tasks.

• CRISP-DM Alignment: Ground your predictive analytics in real-world data science workflows from model building to final evaluation.

• Certification Ready: Final required course to prepare you for the exam at EMHAcademy to earn your official SVBook Certified Data Miner using R credential.

Recommended Learning Path

To maximize your learning experience and complete the SVBook Certified Data Miner using R track, take these courses in sequence:

1. Create Your Calculator: Learn R Programming Basics Fast (R Basics)

2. Applied Statistics using R with Data Processing (Data Understanding & Data Preparation)

3. Advanced Data Visualizations using R with Data Processing (Data Understanding & Data Preparation)

4. Machine Learning with R (This Course - Modeling & Evaluation)

Prerequisite Note: Basic familiarity with R syntax is recommended. Beginners should start with "Create Your Calculator" first.

What You Will Learn

Data Mining Process & Workflow Setup

• Understanding the CRISP-DM Framework (Modeling & Evaluation stages)

• Downloading, loading, and reading datasets into R

Supervised Machine Learning (Regression & Classification)

• Linear Regression: Building and predicting continuous outcomes with linear regression models

• Decision Trees: Splitting train/test sets, training ID3 Decision Trees, and generating predictions

• K-Nearest Neighbors (KNN): Distance-based pattern classification in R

• Naïve Bayes: Probabilistic classification algorithms

• Neural Networks: Training Multi-layer Perceptron models in R

Unsupervised Learning (Clustering)

• K-Means Clustering: Partitioning data into spatial clusters

• Agglomerative Clustering: Building hierarchical tree-based clusters in R

Model Evaluation & Selection

• Algorithm selection strategies ("What Algorithm to Use?")

• Classification Evaluation: Confusion matrices, accuracy, and error evaluation metrics

• Regression Evaluation: Assessing model fit and predictive accuracy for continuous targets

Requirements

• Basic knowledge of R programming (variables, vectors, basic syntax).

• A computer (Windows, Mac, or Linux) with R and RStudio installed.

Who This Course Is For

• Aspiring Data Scientists, Analysts, and R Programmers entering the machine learning space.

• Students and professionals who want to master the Modeling and Evaluation stages of the CRISP-DM lifecycle.

• Anyone preparing for the SVBook Certified Data Miner using R credential.