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, matric decomposition, …), and content-based method.

This course is intended for enthusiasts of data science and machine learning without any prior programming experience.,This course is intended for managers and executives who need a deep understanding of data science and machine learning topics but do not have the time to learn how to code.