Dive into Machine Learning with RapidMiner: A Hands-On Course

Join this Free Udemy Course on Machine Learning with RapidMiner. Transform your AI skills today!

The course “Machine Learning Modelling with RapidMiner” offers an engaging introduction to the world of machine learning and AI through a practical lens. With a user-friendly interface, RapidMiner enables you to grasp fundamental concepts and apply them effectively without delving into complex programming jargon. This course is designed for beginners and enthusiasts who wish to understand machine learning in a structured manner, allowing you to comfortably explore its various facets.

Throughout this comprehensive program, you will gain hands-on experience with an array of machine learning techniques. You’ll learn to build, train, and evaluate models using both supervised methods, like linear regression and decision trees, and unsupervised techniques, such as clustering and dimensionality reduction. The course’s thorough approach ensures that by the end, you will have a robust understanding of how to utilize machine learning algorithms to tackle real-world problems.

In addition to the technical skills, you will learn vital practices for model evaluation and fine-tuning, empowering you to enhance your models’ performance. With Real-life applications and insights shared throughout the course, you’ll emerge ready to implement your knowledge with confidence, equipping you for success in data science and machine learning domains.

What you will 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

Course Content:

  • Sections: 10
  • Lectures: 72
  • Duration: 6h 28m

Requirements:

  • No prior knowledge of programming is required.
  • Prior experience with machine learning is not necessary.

Who is it for?

  • 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.

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