[100% Off] Machine Learning Modelling With Rapidminer
Machine Learning, RapidMiner
What you’ll learn
- Build Supervised Machine Learning Models (Regression and Classification) without coding
- Build Unsupervised Machine Learning Models (Clustering and Dimensionality Reduction) without coding
- Build and train Neural Network to perform Regression and Classification
- Build and train Decision Trees and tree ensemble methods such as Bagging, Boosting, and Random Forest
- Build Recommendation Systems with a collaborative filtering and content-based algorithms
- Build and train a Convolutional Neural Network
- Natural Language Processing without Coding
- Make accurate prediction without coding
Requirements
- No prior knowledge of programming is required.
- Prior experience with machine learning is not necessary.
Description
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.
Author(s): Peyman Hessari
