Linear Regression vs Logistic Regression | Data Science Training | Edureka

This Edureka video on Linear Regression Vs Logistic Regression covers the basic concepts of linear and logistic models. The following topics are covered in this session:
(01:05) Types of Machine Learning
(03:09) Regression Vs Classification
(05:47) What is Linear Regression?
(09:22) What is Logistic Regression?
(13:26) Linear Regression Use Case
(15:02) Logistic Regression Use Case
(16:18) Linear Regression Vs Logistic Regression

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About the Course

Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.

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Why Learn Data Science?

Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.

After the completion of the Data Science course, you should be able to:

1. Gain insight into the 'Roles' played by a Data Scientist

2. Analyze Big Data using R, Hadoop and Machine Learning

3. Understand the Data Analysis Life Cycle

4. Work with different data formats like XML, CSV and SAS, SPSS, etc.

5. Learn tools and techniques for data transformation

6. Understand Data Mining techniques and their implementation

7. Analyze data using machine learning algorithms in R

8. Work with Hadoop Mappers and Reducers to analyze data

9. Implement various Machine Learning Algorithms in Apache Mahout

10. Gain insight into data visualization and optimization techniques

11. Explore the parallel processing feature in R



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Who should go for this course?

The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'

2. Analytics Managers who are leading a team of analysts

3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics

4. Business Analysts who want to understand Machine Learning (ML) Techniques

5. Information Architects who want to gain expertise in Predictive Analytics

6. 'R' professionals who want to captivate and analyze Big Data

7. Hadoop Professionals who want to learn R and ML techniques

8. Analysts wanting to understand Data Science methodologies.

[afolabiadetoun]

You are a good teacher. Not a video that uses only sound, clicking slides and assumes everyone is a professional. You are different. Explanation matters, thank you and remain blessed.

[royeden]

Wow... Crystal clear explanation .. Please continue the good work.... Explanation on SVM, decision trees and basic statistics would be great... Thanks a lot.

[MhmdGhdbn]

Solid work. Thank you so much for making this subject crystal clear.

[dakotaballard]

Your explanation was clear and helpful, thank you!

[Moiez]

Your explanation is better than a professors at MIT. I'm serious. I'm taking the " applied data science" 12-week program online from MIT and you did a much better job.

[saifdakhani]

very easy to understand ...Thnank you ma'am !

[SAURABHGUPTA_DINOSAUR]

So far the best video on the difference between linear and logistic regression.

[sumerujain]

thank you ma'am for your wonderful session which helped me lot...

[QaAutomationAlchemist]

Good and clear content.
Excellent voice quality.
Nice flow of topics.

[AmanKumar-wuej]

Awesome everything becomes crystal clear

[A]

Good and clear content, Excellent voice quality.

Nice flow of topics.Crisp and clear

[confessionsofthedoodle]

very clear! something i couldn't understand fully during my class, i now understood the concept in a 20min video.

[drashyakushwah]

Thank you soo much.. this clear all my doubts👌👏

[shawsr]

Thank you. Your teaching is excellent.

[aditirawat]

the content is so appropriate and that for free.. Edureka team great work!!

[giuliapetitto]

Simple and excellent explanation. Thanks!

[Niki__]

My question is, what is the difference between binary classification and logistic regression?

[azad]

Thankyou for the video. It was so much clear to understand.

[stevemungai]

simple and clear. real world explanation.

[ayushagrawal]

Great video first of all, thanks a lot
Query1 - While Logistic regression throws a categorical output, does it have to be always binary?
Query2 - Are there any constraints or special treatment on the type of inputs (continuous, categorical, binary categorical etc) for either linear or logistic regression?