Grade 8th- Data Mining

By Fasih Categories: Class 8, Senior
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About Course

This course aims to extend your knowledge and experience of practical data mining, following on from Data Mining with Weka.

We’ll talk about “big data” and how to deal with that in Weka (you’ll process a dataset with 10 million instances). You’ll learn about mining text. You’ll look at filtering using supervised and unsupervised filters. You’ll learn about discretization and sampling. You’ll learn about attribute selection. You’ll learn about classification rules, rules vs. trees, association rules, clustering, cost-sensitive evaluation and classification. You already know how to use the Explorer, and we’re going to start by showing you Weka’s other interfaces.

Again, the aim of the course is to dispel the mystery that surrounds data mining. After completing it you will be even better equipped to mine your own data using more powerful methods. Most importantly, you’ll understand what it is that you’re doing!

  • Class Duration:  40 minutes
  • Frequency :Five days a week
  •  Language:English
  •  Skill Level:Intermediate
  •  Chapters covered:14
  •  Topics covered:76
  •  Videos Tutorials:215
  •  Certificate: Yes
  •  Supporting Worksheet: 32
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Course Content

Introduction to Data Mining
This is structured as a five week course: Week 1: Exploring Weka’s interfaces, and working with big data Week 2: Discretization and text classification Week 3: Classification rules, association rules, and clustering Week 4: Selecting attributes, and counting the cost Week 5: Neural networks, learning curves, and performance optimization.

Week 1
In Week 1 you will explore Weka’s other interfaces: the Experimenter, which allows you to run experiments that compare different methods; the Knowledge Flow interface, which lets you set up a graphical workflow for your data mining project; the Command Line interface, which accepts more complex commands to Weka. And you will learn about “big data” and how to deal with it in Weka. At the end of the week you’ll be equipped to use all Weka’s facilities, including stream-oriented processing for massive datasets.

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