Supervised Learning – Regression Models

سرفصل های دوره

Supervised Learning - Regression Models

1 - Introduction

1 - Introduction About Tutor

2 - Introduction About Basics

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

18 - Big Data vs Non Big Data

5 - Data Understanding Phase Data Collection

19 - What is Data Collection

20 - Understanding Primary Data Sources

21 - Understanding Secondary Data Sources

22 - Understanding Data Collection Using Survey

23 - Understanding Data Collection Using DoE

24 - Understanding possible errors in Data Collection Stage

25 - Understanding Bias and Fairness

6 - Understanding Basic Statistics

26 - Introduction to CRISPMLQ Data preparation & Agenda

27 - What is Probability

28 - What is Random Variable

29 - Understanding Probability and its Application Probability Discussion

7 - Data Preparation Phase Exploratory Data Analysis EDA

30 - Understanding Normal Distribution

31 - What is Inferential Statistics

32 - Understanding Standard Normal Distribution & what is Z Scores

33 - Understanding Measures of central tendency First moment business decision

34 - Understanding Measures of Dispersion Second moment business decision

35 - Understanding Box PlotDiff Bw Percentile and Quantile and Quartile

36 - Understanding Graphical TechniquesQQPlot

37 - Understanding about Bivariate Scatter Plot

8 - Python Installation and Setup

38 - Python Installation

39 - Anakonda Installation

40 - Understand about Anakonda Navigator Spyder & Python Libraries

41 - Understanding about Jupyter and Google Colab

9 - Data Preparation Phase Data Cleansing Type Casting

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 - Learning Clustering Using Python

13 - Dimension Reduction Technique

51 - About Dimension Reduction & its Applications

14 - Network Analysis

52 - Elements of a Network

53 - About Google PageRank Algorithm

54 - Network Based Similarity Metrics

55 - Network related Properties

15 - Traditional ML Models Naive Bayes

56 - Introduction to Naive Bayes

57 - Use Cases of Naive Bayes

16 - Decision Tree

58 - About Decision Tree and its Use Case

17 - About Stacking

59 - What is Stacking

18 - About Boosting

60 - Introduction about Boosting

19 - Introduction About Regression Analysis

61 - Introduction About Regression Analysis

20 - Lets know More In About What are the Regression Models

62 - About Simple Liner Regression and its Use Cases

63 - About Multiple Linear Regression

64 - About Simple Logistic and Multiple Logistic Regression

65 - About Multimonial Regression

66 - About Ordinal Regression

67 - About Negative BiNomial Regression

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