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Discover the versatility of R for data mining with this collection of real-world dataset analysis techniques

Advanced Data Mining projects with R

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Discover the versatility of R for data mining with this collection of real-world dataset analysis techniques

  • Video Duration:02 Hours
  • Cost: $ 124.99

Course Details

Advanced Data Mining Projects with R takes you one step ahead in understanding the most complex data mining algorithms and implementing them in the popular R language. Follow up to our course Data Mining Projects in R, this course will teach you how to build your own recommendation engine. You will also implement dimensionality reduction and use it to build a real-world project. Going ahead, you will be introduced to the concept of neural networks and learn how to apply them for predictions, classifications, and forecasting. Finally, you will implement ggplot2, plotly and aspects of geomapping to create your own data visualization projects.By the end of this course, you will be well-versed with all the advanced data mining techniques and how to implement them using R, in any real-world scenario.

Who all can attend

Data analysts and data scientists with some knowledge of R, who need a helping hand in developing complex data mining projects are the ideal audience for this video course. They should have prior knowledge of basic statistics and some experience with the basic data mining techniques and algorithms.

What you will learn from this course

  • Create predictive models in order to build a recommendation engine
  • Implement various dimension reduction techniques to handle large datasets
  • Acquire knowledge about the neural network concept drawn from computer science and its applications in data mining

Course Content

  1. Clustering with E-commerce Data
    • The Course Overview
    • Understanding Customer Segmentation
    • Clustering Methods – K means and Hierarchical
    • Clustering Methods – Model Based, Other and Comparison

  2. Building a Retail Recommendation Engine
    • What Is Recommendation?
    • Application of Methods and Limitations of Collaborative Filtering
    • Practical Project

  3. Dimensionality Reduction
    • Why Dimensionality Reduction?
    • Practical Project around Dimensionality Reduction
    • Parametric Approach to Dimension Reduction

  4. Applying Neural Network to Healthcare Data
    • Introduction to Neural Networks
    • Understanding the Math Behind the Neural Network
    • Neural Network Implementation in R
    • Neural Networks for Prediction
    • Neural Networks for Classification
    • Neural Networks for Forecasting
    • Merits and Demerits of Neural Networks

Contact Us

Instructor-led online training is also available for the same course.

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