Complete Machine Learning with R Studio - ML for 2025
Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language - R studio
4.58 (2569 reviews)
267 372
students
12 hours
content
May 2025
last update
$79.99
regular price
What you will learn
Learn how to solve real life problem using the Machine learning techniques
Machine Learning models such as Linear Regression, Logistic Regression, KNN etc.
Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc.
Understanding of basics of statistics and concepts of Machine Learning
How to do basic statistical operations and run ML models in R
Indepth knowledge of data collection and data preprocessing for Machine Learning problem
How to convert business problem into a Machine learning problem
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Our Verdict
Complete Machine Learning with R Studio - ML for 2025 provides a solid foundation for mastering various machine learning techniques in R. The course offers a real-life problem-solving perspective, which is essential when transitioning to practical use cases. However, it lacks sufficient quizzes and hands-on practice sessions. Course updates with more exercises will help students strengthen their grasp of ML algorithms presented throughout the lectures. Additionally, incorporating detailed explanations for applied mathematical concepts in advanced topics and ensuring error-free coding demonstrations can further improve this already valuable resource.
What We Liked
- Comprehensive coverage of machine learning techniques in R
- Real-life problem-solving approach with business problem conversion guidance
- Detailed explanations of various ML models such as Linear Regression, Decision Trees, XGBoost, and SVM
Potential Drawbacks
- Lack of hands-on practice sessions and quizzes for better retention
- Some repetition in the course content across modules
- Minor coding errors that may cause confusion during implementation
- Limited focus on the mathematical principles behind certain higher concepts
Related Topics
2503534
udemy ID
10/08/2019
course created date
01/10/2019
course indexed date
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