- Promoted by: Anonymous
- Platform: Udemy
- Category: Other IT & Software
- Language: English
- Instructor: Peyman Hessari
- Duration: 6 hour(s) 28 minute(s)
- Student(s): 969
- Rate 2.5 Of 5 From 1 Votes
- Expires on: 2025/12/31
-
Price:
19.990
Machine Learning, RapidMiner
Unlock your potential with a Free coupon code
for the "Machine Learning Modelling with RapidMiner" course by Peyman Hessari on Udemy.
This course, boasting a 2.5-star rating from 1 reviews
and with 969 enrolled students, provides comprehensive training in Other IT & Software.
Spanning approximately
6 hour(s)
28 minute(s)
, this course is delivered in English
and we updated the information on December 27, 2025.
To get your free access, find the coupon code at the end of this article. Happy learning!
This intuitive program comprehensively introduces machine learning fundamentals and practical AI application development using RapidMiner.
You’ll gain hands-on experience in building, training, and evaluating machine learning models with RapidMiner.
The course covers a wide range of machine learning models, including both supervised and unsupervised techniques, such as linear regression, neural networks, decision trees, ensemble techniques, neural networks, clustering, dimensionality reduction, and recommender systems.
In addition, you'll develop the skills to evaluate and fine-tune models, enhance performance through data-driven techniques, and more.
By the end of this program, you will have a strong grasp of core machine learning concepts and practical skills, enabling you to confidently and quickly apply algorithms to solve complex, real-world challenges.
After completing this course, you will be capable of:
• Work with RapidMiner to build machine learning models.
• Build and train supervised machine learning models for prediction in regression and classification tasks.
• Build and train a neural network.
• Utilize machine learning development best practices to ensure that your models generalize well to new and unseen data.
• Build and use decision trees and ensemble methods.
• Use unsupervised learning algorithms such as clustering and dimensionality reduction.
• Build recommender systems with rank-based techniques, collaborative filtering approach (user-user, item-item, matrix decomposition, ...), and content-based methods.