ML-R.AE1
Machine Learning with R
No more textbook confusion. Learn applied machine learning with R and finally connect the dots between data, models, and real results.
- 13 Lecciones interactivas y 44 topics mapped to the official exam objectives
Intermediate A tu propio ritmo · 1 año de acceso
13Lecciones interactivas
44Topics
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
Most people know R but freeze when it’s time to apply it to machine learning. That’s where our Machine Learning with R training changes the game.
This isn’t just another tutorial or theory dump. You will gain practical knowledge and real world datasets to build predictive models and solve problems that actually exist. Consider this as a mirror world learning of what today’s data scientists face in their day-to-day routine.
By the end, you won’t just know machine learning, you’ll be ready to use it. Whether you're aiming to land a role in data science, add ML projects to your portfolio, or just get better at turning data into decisions, this machine learning with R training gives you the confidence and skills to stand out.
Perfect for anyone serious about data science with R and machine learning.And tired of courses that overpromise and underdeliver.
- Real-World Machine Learning Skills: Learn how to build, test, and tune machine learning models using real datasets.
- Mastery of R for Machine Learning: Go beyond the basics and actually apply R programming to solve real-world problems.
- A Complete ML Workflow, Step-by-Step: From data prep to prediction, you’ll understand how machine learning works in the real world.
- Portfolio-Worthy Projects: Build hands-on projects that prove you can turn raw data into smart decisions. Great for resumes, interviews, and LinkedIn.
- Confidence to Solve Real Problems: Whether it’s churn prediction, classification, or clustering, you’ll know how to choose the right model and make it work.
- Job-Ready Knowledge: You won’t just learn machine learning, you’ll think like a data scientist and have the skills to back it up.
Course Highlights
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13 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
13 Lecciones interactivas · 44 topics01 Introduction 1 topics +
- What Does This Course Cover?
02 What Is Machine Learning? 4 topics +
- Discovering Knowledge In Data
- Machine Learning Techniques
- Model Selection
- Model Evaluation
03 Introduction to R and RStudio 4 topics +
- Welcome To R
- R And RStudio Components
- Writing And Running An R Script
- Data Types In R
04 Managing Data 4 topics +
- The tidyverse
- Data Collection
- Data Exploration
- Data Preparation
05 Linear Regression 5 topics +
- Bicycle Rentals And Regression
- Relationships Between Variables
- Simple Linear Regression
- Multiple Linear Regression
- Case Study: Predicting Blood Pressure
06 Logistic Regression 4 topics +
- Prospecting For Potential Donors
- Classification
- Logistic Regression
- Case Study: Income Prediction
07 k-Nearest Neighbors 3 topics +
- Detecting Heart Disease
- k-Nearest Neighbors
- Case Study: Revisiting The Donor Dataset
08 Naïve Bayes 3 topics +
- Classifying Spam Email
- NAÏVE Bayes
- Case Study: Revisiting The Heart Disease Detection Problem
09 Decision Trees 3 topics +
- Predicting Build Permit Decisions
- Decision Trees
- Case Study: Revisiting The Income Prediction Problem
10 Evaluating Performance 3 topics +
- Estimating Future Performance
- Beyond Predictive Accuracy
- Visualizing Model Performance
11 Improving Performance 2 topics +
- Parameter Tuning
- Ensemble Methods
12 Discovering Patterns with Association Rules 4 topics +
- Market Basket Analysis
- Association Rules
- Discovering Association Rules
- Case Study: Identifying Grocery Purchase Patterns
13 Grouping Data with Clustering 4 topics +
- Clustering
- k-Means Clustering
- Segmenting Colleges With -Means Clustering
- Case Study: Segmenting Shopping Mall Customers
03 / Preguntas frecuentes
Preguntas antes de empezar
What is better between Python and R for machine learning?+
Python is more common, but R shines in data analysis and stats-heavy ML tasks. It’s loaded with ML libraries and perfect for building models fast.
What makes R a better language for machine learning?+
R is built for data analysis. It has powerful libraries for statistics and machine learning, making it a go-to for data scientists. With libraries like caret and mlr, R is very useful when effective visualizing is required.
Do companies really use R for machine learning?+
Yes. R is widely used in industries like finance, healthcare, and academia where data-driven decisions matter most.
What kinds of projects will I build in the Machine Learning with R course?+
You’ll work on practical, resume-worthy projects like classification, regression, clustering, and recommendation systems. These projects are based on real-world scenarios to help you build skills that employers actually look for.
Can I take this course if I only know the basics of R?+
Yes, absolutely. This course is designed for learners with a basic understanding of R. You don’t need to be an expert; just someone comfortable with R syntax and ready to get hands-on. Everything else, from data preprocessing to building ML models, is explained clearly and practically.
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- 1 año de acceso completo
- Certificado de finalización
No se requiere tarjeta de crédito