Supervised Learning - Regression Models

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Elearning Moocs

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1 - Introduction

1 - Introduction About Tutor.mp4

03:15

2 - Introduction About Basics

2 - Agenda and stages of Analytics.mp4

01:03

3 - What is Diagnoistic Analytics.mp4

01:21

4 - What is Predictive Analytics.mp4

01:57

5 - What is Prescriptive Analytics.mp4

11:41

6 - What is CRISPMLQ.mp4

03:08

3 - Business Understanding Phase

7 - Business Understanding Define Scope Of Application.mp4

18:44

8 - Business Understanding Define Success Criteria.mp4

08:13

9 - Business Understanding Use Cases.mp4

09:59

4 - Data Understanding Phase Data Types

10 - Agenda Data Understanding.mp4

00:49

11 - Introduction to Data Understanding.mp4

06:18

12 - Data Types Continuous Vs Discrete.mp4

11:18

13 - Categorical Data Vs Count Data.mp4

06:45

14 - Pratical Data Understanding using Realtime Examples.mp4

11:15

15 - Scale of Measurement.mp4

03:34

16 - Quantitave Vs Qualitative.mp4

05:04

17 - Structure Vs Unstructured Data.mp4

13:04

18 - Big Data vs Non Big Data.mp4

09:44

5 - Data Understanding Phase Data Collection

19 - What is Data Collection.mp4

04:12

20 - Understanding Primary Data Sources.mp4

22:15

21 - Understanding Secondary Data Sources.mp4

13:31

22 - Understanding Data Collection Using Survey.mp4

06:46

23 - Understanding Data Collection Using DoE.mp4

07:15

24 - Understanding possible errors in Data Collection Stage.mp4

16:21

25 - Understanding Bias and Fairness.mp4

05:17

6 - Understanding Basic Statistics

26 - Introduction to CRISPMLQ Data preparation & Agenda.mp4

02:08

27 - What is Probability.mp4

05:33

28 - What is Random Variable.mp4

12:00

29 - Understanding Probability and its Application Probability Discussion.mp4

13:17

7 - Data Preparation Phase Exploratory Data Analysis EDA

30 - Understanding Normal Distribution.mp4

15:42

31 - What is Inferential Statistics.mp4

10:41

32 - Understanding Standard Normal Distribution & what is Z Scores.mp4

28:17

33 - Understanding Measures of central tendency First moment business decision.mp4

26:45

34 - Understanding Measures of Dispersion Second moment business decision.mp4

10:54

35 - Understanding Box PlotDiff Bw Percentile and Quantile and Quartile.mp4

06:17

36 - Understanding Graphical TechniquesQQPlot.mp4

08:41

37 - Understanding about Bivariate Scatter Plot.mp4

35:36

8 - Python Installation and Setup

38 - Python Installation.mp4

06:08

39 - Anakonda Installation.mp4

07:01

40 - Understand about Anakonda Navigator Spyder & Python Libraries.mp4

24:31

41 - Understanding about Jupyter and Google Colab.mp4

08:41

9 - Data Preparation Phase Data Cleansing Type Casting

42 - Understanding Data Cleansing Typecasting.mp4

10:32

43 - Understanding Data Cleansing Typecasting using python.mp4

15:42

10 - Data Preparation Phase Data Cleansing Handling Duplicates

44 - Understanding Handling Duplicates.mp4

10:48

45 - Understanding Handling Duplicates using Python.mp4

25:26

11 - Data Preparation Phase Data CleansingOutlier Analysis Treatment

46 - Understanding Outlier Analysis Treatment.mp4

18:06

47 - Understanding Outlier Analysis Treatment using Python.mp4

27:31

12 - Data Mining Clustering Segmentation using Python

48 - Overview Of Clustering Segmentation.mp4

15:19

49 - Distance Between Clusters.mp4

22:19

50 - Learning Clustering Using Python.mp4

14:17

13 - Dimension Reduction Technique

51 - About Dimension Reduction & its Applications.mp4

12:49

14 - Network Analysis

52 - Elements of a Network.mp4

05:10

53 - About Google PageRank Algorithm.mp4

05:18

54 - Network Based Similarity Metrics.mp4

12:26

55 - Network related Properties.mp4

07:15

15 - Traditional ML Models Naive Bayes

56 - Introduction to Naive Bayes.mp4

12:21

57 - Use Cases of Naive Bayes.mp4

09:29

16 - Decision Tree

58 - About Decision Tree and its Use Case.mp4

13:19

17 - About Stacking

59 - What is Stacking.mp4

12:33

18 - About Boosting

60 - Introduction about Boosting.mp4

06:05

19 - Introduction About Regression Analysis

61 - Introduction About Regression Analysis.mp4

18:23

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

62 - About Simple Liner Regression and its Use Cases.mp4

09:38

63 - About Multiple Linear Regression.mp4

23:58

64 - About Simple Logistic and Multiple Logistic Regression.mp4

12:16

65 - About Multimonial Regression.mp4

18:02

66 - About Ordinal Regression.mp4

12:22

67 - About Negative BiNomial Regression.mp4

16:21

Description

Supervised Learning - Regression Models

What You'll Learn?

Understanding the purpose and applications of regression models in various fields, such as economics, finance.
Exploring the basic concept of simple linear regression, where one dependent variable is modeled against a single independent variable
Understanding how to fit polynomial functions to data, allowing for nonlinear relationships between variables.
Extending the concepts of linear regression to multiple independent variables and learning how to interpret the coefficients of each predictor.

Who is this for?

Students and Researchers: Those studying or conducting research in fields like statistics, economics, social sciences, and data science, where regression analysis is a fundamental tool.

Data Analysts and Data Scientists: Professionals who work with data and want to gain a deeper understanding of regression techniques to analyze relationships between variables and make predictions.

Individuals working in business analytics, marketing, finance, or any other domain where data-driven decision-making is crucial.

Those with programming skills who want to expand their knowledge to include regression modeling for data analysis and prediction tasks.

What You Need to Know?

Basic Mathematics: You should be comfortable with algebra, calculus (especially derivatives and integrals), and basic statistics.

Probability and Statistics: Familiarity with probability theory and basic statistical concepts is important for understanding the underlying principles of regression modeling.

Linear Algebra: Basic knowledge of linear algebra is helpful, as regression models often involve matrix operations and understanding concepts like vectors and matrices.

Programming Skills: Some regression modeling courses might require programming knowledge in a statistical language such as R or Python. Proficiency in data manipulation, visualization, and basic statistical analysis in these languages will be beneficial.

More details

Description
The Comprehensive Regression Models course is designed to provide students with an in-depth understanding of regression analysis, one of the most widely used statistical techniques for analyzing relationships between variables. Through a combination of theoretical foundations, practical applications, and hands-on exercises, this course aims to equip students with the necessary skills to build, interpret, and validate regression models effectively. Students will gain a solid grasp of regression concepts, enabling them to make informed decisions when dealing with complex data sets and real-world scenarios. This course is intended for advanced undergraduate and graduate students, as well as professionals seeking to enhance their statistical knowledge and analytical abilities.
To ensure students can fully engage with the course material, a strong background in statistics and basic knowledge of linear algebra is recommended. Prior exposure to introductory statistics and familiarity with data analysis concepts (e.g., hypothesis testing, descriptive statistics) will be advantageous.
The Comprehensive Regression Models course empowers students to become proficient analysts and decision-makers in their academic and professional pursuits, making informed choices based on evidence and data-driven insights. Armed with this valuable skillset, graduates of this course will be better positioned to contribute meaningfully to research, policy-making, and problem-solving across various domains, enhancing their career prospects and their ability to drive positive change in the world.
Course Objectives:
Understand the Fundamentals: Students will be introduced to the fundamentals of regression analysis, including the different types of regression (e.g., linear, multiple, logistic, polynomial, etc.), assumptions, and underlying mathematical concepts. Emphasis will be placed on the interpretation of coefficients, the concept of prediction, and assessing the goodness-of-fit of regression models.
Regression Model Building: Participants will learn the step-by-step process of building regression models. This involves techniques for variable selection, handling categorical variables, dealing with collinearity, and model comparison. Students will be exposed to both automated and manual methods to ensure a comprehensive understanding of the model building process.
Model Assessment and Validation: Evaluating the performance and validity of regression models is crucial. Students will explore diagnostic tools to assess model assumptions, identify outliers, and check for heteroscedasticity.
Interpreting and Communicating Results: Being able to interpret regression results accurately and effectively communicate findings is essential. Students will learn how to interpret coefficients, measure their significance, and communicate the practical implications of the results to various stakeholders in a clear and concise manner.
Advanced Topics in Regression: The course will delve into advanced topics, including time series regression, nonlinear regression, hierarchical linear models, and generalized linear models. Students will gain insights into when and how to apply these techniques to tackle real-world challenges.
Real-world Applications: Throughout the course, students will be exposed to real-world case studies and examples from various disciplines such as economics, social sciences, healthcare, and engineering. This exposure will enable students to apply regression analysis in different contexts and understand the relevance of regression models in diverse scenarios.
Statistical Software: Hands-on experience is a critical aspect of this course. Students will work with popular statistical software packages (e.g., R, Python, or SPSS) to implement regression models and perform data analysis. By the end of the course, participants will have gained proficiency in using these tools for regression modeling.
Course Conclusion:
In conclusion, the Comprehensive Regression Models course offers an in-depth exploration of regression analysis, providing students with the necessary tools and knowledge to utilize this powerful statistical technique effectively. Throughout the course, students will gain hands-on experience with real-world datasets, ensuring they are well-equipped to apply regression analysis to a wide range of practical scenarios. By mastering regression techniques, students will be prepared to contribute to various fields, such as research, business, policy-making, and more, making data-informed decisions that lead to positive outcomes. Whether pursuing further studies or entering the workforce, graduates of this course will possess a valuable skillset that is highly sought after in today's data-driven world. As the demand for data analysis and predictive modeling continues to grow, this course will empower students to become proficient analysts and problem solvers, capable of making a significant impact in their respective domains.
By the end of this course, participants will be able to:
Understand the theoretical underpinnings of various regression models and their assumptions.
Build and validate regression models using appropriate techniques and tools.
Interpret regression results and communicate findings to different stakeholders.
Apply regression analysis to solve complex problems in diverse fields.
Confidently use statistical software for data analysis and regression modeling.
Who this course is for:
Students and Researchers: Those studying or conducting research in fields like statistics, economics, social sciences, and data science, where regression analysis is a fundamental tool.
Data Analysts and Data Scientists: Professionals who work with data and want to gain a deeper understanding of regression techniques to analyze relationships between variables and make predictions.
Individuals working in business analytics, marketing, finance, or any other domain where data-driven decision-making is crucial.
Those with programming skills who want to expand their knowledge to include regression modeling for data analysis and prediction tasks.

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360DigiTMG was established in 2013 as the training arm of Innodatatics Inc., USA, an IT services firm that creates innovative solutions for fundamental business issues. 360DigiTMG is a prominent training school recognized by CIO Review as one of the "20 Most Promising Data Analytics Solution Providers - 2018." The school is a Skim Bantuan Latihan (SBL) scheme-approved institution by the Human Resources Development Fund (HRDF) of Malaysia's Ministry of Human Resources.360DigiTMG provides courses in big data analytics, machine learning, data science, artificial intelligence, deep learning, internet of things, robotic process automation, Amazon Web Services (cloud computing), data visualization (business intelligence), Tableau, digital marketing, risk management, quality management, agile methodology, project management, and many others.360DigiTMG adds a holistic, global market view to its programme with headquarters in the United States and influence in India, Malaysia, East Asia, Australia, the Middle East, the United Kingdom, and the Netherlands.

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