Data Mining and AI Algorithms
Last updated 10/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.07 GB | Duration: 3h 25m
Without any Tool
What you'll learn
Mining Association Rules
Clustering Technique
Classification Technique
Simulation
Applications
Requirements
Basic Mathematics
Description
This course discusses some of the most important Data Mining Techniques without use of any programming language or tool. These techniques provide the foundation of intelligent system. The focus is on learning the concepts in simple way and understanding how these techniques can change the way we do our work. This knowledge is necessary to be able to take advantage of AI implementation in the organization.This course focusses on conceptual discussions and tries to go deep into the concepts. Small datasets have been used so that one can do the computations manually and get the feel of various algorithms. We have discussed some fine points of Association Mining and Clustering here. Few questions related to Simulation too have been discussed without having much discussion about the techniques. As we are building this course, we are giving priority to some immediate requirements.We first aim to do the three important data mining techniques -- Clustering, Association and Classification in detail. This course is being developed. At present, it is meeting partial requirement of a set of students in a particular course. If you are not from that course and enrol here, you get the feel of what we aim at. We will built it over coming few months.You can ask questions. Or, if there is any urgent need, please message me through the system. Those needs can be taken care on priority.
Overview
Section 1: Data Mining - Going Deep up to Hidden Valuable Patterns
Lecture 1 Hidden Valuable Pattern
Section 2: Mining Association Rules
Lecture 2 Association Rules - Apriori Algorithm
Lecture 3 Threshold Probability and Support
Lecture 4 Revision Questions Set A with Solutions
Section 3: Clustering
Lecture 5 K-Means Clustering
Lecture 6 Change in Cluster -- An Example
Lecture 7 Practice Question Set A with Solution
Section 4: Exercises
Lecture 8 Association Rules Exercise
Lecture 9 K-Means Clustering Exercise
Section 5: Simulation
Lecture 10 Understanding Uncertainty and Probability
Lecture 11 Discrete Variable Simulation using Random Number Table
Lecture 12 Few Questions with Solutions
Section 6: Examination Questions with Answers
Lecture 13 Simulation Question
Lecture 14 Clustering Question
Lecture 15 Association Question
Students and Professionals looking for conceptual discussions on Data Mining
Homepage
https://www.udemy.com/course/data-mining-and-ai-algorithms/Fikper
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