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Overview Of learn machine learning with R
Machine learning is one of the fastest-growing skill areas in the UK, according to UK Tech Nation and GOV.UK reports, the demand for data professionals continues to grow across finance, healthcare, retail, and technology industries. Moreover, organisations increasingly seek talent capable of analysing data, building predictive models, and making informed data-driven decisions.
This machine learning with R course offers a beginner-friendly yet practical introduction to core predictive-modelling techniques. In addition, learners explore linear regression, logistic regression, multiple regression, dummy variables, and stepwise elimination — all using R, one of the most widely used statistical programming languages. Consequently, the course helps students and professionals gain real-world analytical confidence.
⭐What You’ll Learn (Quick Highlights)
1️⃣ Understand how linear and logistic regression work
2️⃣ Build predictive models from scratch using R
3️⃣ Analyse datasets with multiple variables
4️⃣ Apply backward elimination and model optimisation
5️⃣ Use plots, training sets, and evaluation methods effectively
🧠📊 Fun, visual, and practical — ideal for students and beginners entering the world of data science.
By the end of this course, you’ll know how to:
Build and interpret linear regression models confidently.
Use logistic regression to classify and predict outcomes.
Handle datasets with dummy variables and multiple predictors.
Apply backward elimination to improve model accuracy.
Course Features:
Developed by expert data science instructors
CPD-accredited machine learning qualification
Lifetime access to all lessons
24/7 tutor support
Mobile, tablet, and desktop compatible
Description of learn machine learning with R
This machine learning with R course provides a step-by-step guide to building predictive models using real datasets. Moreover, it introduces essential concepts such as regression, feature selection, model evaluation, and plotting — all explained clearly for beginners. Therefore, learners gain hands-on experience working directly inside the R environment.
In addition, the course demonstrates how to clean data, eliminate redundant variables, create multiple regression models, and optimise prediction accuracy. Furthermore, each section includes real examples such as salary prediction and year-based forecasting. Consequently, this training is ideal for aspiring data analysts, business professionals, and students entering the UK data job market.
Curriculum Overview:
⭐ Section 1 – Linear Regression & Logistic Regression
Working on Linear Regression — Learn how linear models predict continuous outcomes.
Equation — Understand the mathematical foundation behind regression.
Making the Regression of the Algorithm — Build your first prediction formula.
Basic Types of Algorithms — Explore essential algorithm categories.
Predicting the Salary of the Employee — Apply linear regression to a real dataset.
Making of Simple Linear Regression Model — Construct and interpret your first model.
Plotting Training Set and Work — Visualise model performance with R plots.
⭐ Section 2 – Understanding the Dataset
Multiple Linear Regression — Build models with multiple predictors.
Dummy Variable Concept — Work with categorical data using dummy variables.
Predictions Over Year — Forecast outcomes over time using regression.
Difference Between Reference Elimination — Understand how reference coding works.
Working of the Model — Analyse and troubleshoot model behaviour.
Working on Another Dataset — Practise modelling with a new dataset.
Backward Elimination Approach — Use stepwise selection to improve accuracy.
Making of the Model with Full and Null — Compare complete vs. null models to choose the best version.
Why Choose Us?
- This course is accredited by the CPD Quality Standards.
- Lifetime access to the whole collection of the learning materials.
- Online test with immediate results.
- Enroling in the course has no additional cost.
- You can study and complete the course at your own pace.
- Study for the course using any internet-connected device, such as a computer, tablet, or mobile device.
Certificate of Achievement
Upon successful completion of this course, learners will receive a CPD-certified PDF certificate for £9.99 or a hard copy for £14.99.
This certificate strengthens your profile for roles in data analysis, machine learning, business analytics, and more.
Who Is This learn machine learning with R Course For ?
Students entering data science
Professionals wanting data-analysis skills
Career changers exploring tech fields
Business analysts and marketers
Anyone wanting a simple, practical intro to ML
Requirements of learn machine learning with R Course
No previous programming experience needed
Basic numeracy and logical thinking
A computer with R installed
Internet access and a digital device
Career Path
Data Analyst
Junior Data Scientist
Business Analyst
Forecasting & Modelling Assistant
Research Analyst
Learners may progress to advanced R, Python, AI, and machine learning certifications.
Order Your Certificate
To order CPD Quality Standard Certificate, we kindly invite you to visit the following link:
FAQs -
Basic R helps but we start from essentials in RStudio.
You’ll use tidyverse for data wrangling and ggplot2 for visuals.
Light algebra and stats (means, variance, train/test split) are reviewed as you go.
Linear/logistic regression, k-NN, decision trees, random forests, gradient boosting (xgboost), and SVMs.
We use tidymodels (recipes + workflows + parsnip) for clean pipelines and tuning.
Cross-validation, grid search, and feature engineering are included.
For regression: RMSE/MAE and residual diagnostics.
For classification: accuracy, precision/recall, F1, ROC-AUC, and confusion matrices.
You’ll build comparison tables and plots to justify model choice.
Install R and RStudio (free).
Setup includes tidyverse, tidymodels, xgboost, ranger, e1071 (script provided).
A starter R Project lets you run labs immediately.
Course Curriculum
| Section 01: Linear Regression and Logistic Regression | |||
| Working on Linear Regression | 00:16:00 | ||
| Equation | 00:12:00 | ||
| Making the Regression of the Algorithm | 00:06:00 | ||
| Basic Types of Algorithms | 00:13:00 | ||
| predicting the Salary of the Employee | 00:16:00 | ||
| Making of Simple Linear Regression Model | 00:08:00 | ||
| Plotting Training Set and Work | 00:17:00 | ||
| Section 02: Understanding Dataset | |||
| Multiple Linear Regression | 00:13:00 | ||
| Dummy Variable Concept | 00:07:00 | ||
| Predictions Over Year | 00:10:00 | ||
| Difference Between Reference Elimination | 00:10:00 | ||
| Working of the Model | 00:13:00 | ||
| Working on Another Dataset | 00:14:00 | ||
| Backward Elimination Approach | 00:16:00 | ||
| Making of the Model with Full and Null | 00:12:00 | ||
| Order Your Certificate | |||
| Order Your Certificate | 00:00:00 | ||
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