Introduction to Data Mining
3.8 (3 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
12 students enrolled

Introduction to Data Mining

Introduction to Data Mining + Real Project Example of using CRISP-DM in Weka
3.8 (3 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
12 students enrolled
Last updated 4/2020
English
English [Auto-generated]
Current price: $12.99 Original price: $19.99 Discount: 35% off
16 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 2.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
Training 5 or more people?

Get your team access to 4,000+ top Udemy courses anytime, anywhere.

Try Udemy for Business
What you'll learn
  • data-mining
  • data science
  • weka
  • modeling algorithms
  • machine learning
  • artificial intelligence
  • classification
  • clustering
  • visualization
  • preprocessing data
  • understanding data
  • business understanding
  • evaluation of models
Requirements
  • Basic IT familiarity
Description

Data Mining is one of the sexiest jobs today! Not only is it a high-paying job, but it is also a very interesting one!

This course covers the most important topics drawn from my experience as a computer science engineer who worked on multiple data mining projects.

This course contains 4 main sections.

  • The first section contains the fundamentals of data mining and the explanation of basic concepts.

  • The second section dives deeper into the various algorithms used in Data Mining.

  • The third section will give you an overview of how to work with Weka, including preprocessing, classification and clustering.

  • The third section is an example of a real project solution using the CRISP-DM methodology in data mining software Weka

Who this course is for:
  • IT students interested in Data Mining
  • Students interested in Weka
  • Students interested in Data Science
  • Students interested in Machine Learning
  • Students interested in Artificial Intelligence
Course content
Expand all 30 lectures 02:24:58
+ Data Mining Basic Concepts
10 lectures 39:55
CRISP-DM
3 questions
Data Mining from a Business Perspective
08:21
Statistics in relation to Data Mining
04:16
Business Intelligence
05:11
Types of Business Intelligence Solutions
01:37
Big Data
02:48
Web Mining
03:03
Data types
03:24
+ CRISP-DM Steps
11 lectures 40:33
Preparing the data
02:35
Modeling - Machine Learning Methods
04:10
Deep Learning + SVM
04:52
Empirical Learning
02:57
Types of Learning
03:46
Modeling - Decision Trees
05:50
Modeling - Bayesian Methods
02:48
Modeling - Genetic Algorithms
02:57
Modeling - Neural Networks
04:08
Evaluation of Models
03:08
Modeling - Instance based
03:22
Algorithms
3 questions
+ Weka
4 lectures 40:34
Introduction to Weka
13:02
Preprocessing the Data in Weka
12:40
Classification in Weka
08:27
Clustering in Weka
06:25
+ Real Project Example in Weka based on CRISP-DM
4 lectures 22:26
Starting the project in Weka
09:37
Business Understanding
03:09
Data Understanding and Preparation
07:14
Modeling and Evaluation
02:26