Data Mining – Unsupervised Learning

سرفصل های دوره

Data Mining - Unsupervised Learning

1 - Introduction

1 - Introduction about Tutor

2 - About Analytics

2 - Agenda and Stages Of Analytics

3 - What is Diagnoistic Analytics

4 - What is Predictive Analytics

5 - What is Prescriptive Analytics

6 - What is CRISPMLQ

3 - Business Understanding Phase

7 - Business Understanding Define Scope Of Application

8 - Business Understanding Define Success Criteria

9 - Business Understanding Use Cases

4 - Data Understanding Phase Data Types

10 - Agenda Data Understanding

11 - Introduction to Data Understanding

12 - Data Types Continuous Vs Discrete

13 - Categorical Data Vs Count Data

14 - Pratical Data Understanding using Realtime Examples

15 - Scale of Measurement

16 - Quantitave Vs Qualitative

17 - Structure Vs Unstructured Data

5 - Data Understanding Phase Data Collection

18 - What is Data Collection

19 - Understanding Primary Data Sources

20 - Understanding Secondary Data Sources

21 - Understanding Data Collection Using Survey

22 - Understanding Data Collection Using DoE

23 - Understanding possible errors in Data Collection Stage

24 - Understanding Bias and Fairness

6 - Understanding Basic Statistics

25 - Introduction to CRISPMLQ Data preparation & Agenda

26 - What is Probability

27 - What is Random Variable

28 - Understanding Probability and its ApplicationProbabiity Discussion

7 - Data Preparation Phase Exploratory Data Analysis EDA

29 - Understanding Normal Distribution

30 - What is Inferential Statistics

31 - Understanding Standard Normal Distribution & what is Z Scores

32 - Understanding Measures of central tendency First moment business decession

33 - Understanding Measures of Dispersion Second moment business decision

34 - Understanding Box PlotDiff Bw Percentile and Quantile and Quartile

35 - Understanding Graphical TechniquesQQPlot

36 - Understanding about Bivariate Scatter Plot

8 - Python Installation and Setup

37 - Python Installation

38 - Anakonda Installation

39 - Understand about Anakonda Navigator Spyder & Python Libraries

40 - Understanding about Jupyter and Google Colab

9 - Data Preparation Phase Data Cleansing Type Casting

41 - Recap Of Concepts

42 - Understanding Data Cleansing Typecasting

43 - Understanding Data Cleansing Typecasting Using Python

10 - Data Preparation Phase Data Cleansing Handling Duplicates

44 - Understanding Handling Duplicates

45 - Understanding Handling Duplicates using Python

11 - Data Preparation Phase Data CleansingOutlier Analysis Treatment

46 - Understanding Outlier Analysis Treatment

47 - Understanding Outlier Analysis Treatment using Python

12 - Data Mining Clustering Segmentation using Python

48 - Overview Of Clustering Segmentation

49 - Distance Between Clusters

50 - Hierarchical Clustering Process

51 - Learning Clustering Using Python

13 - Dimension Reduction Techniques

52 - About Dimension Reduction & its Applications

53 - Dimension Reduction Techniques

14 - Network Analytics

54 - Elements of a Network

55 - About Google PageRank Algorithm

56 - Network Based Similarity Metrics

57 - Network related Properties

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