Logistic Regression in R | Machine Learning Algorithms | Data Science Training | Edureka

This Logistic Regression Tutorial shall give you a clear understanding as to how a Logistic Regression machine learning algorithm works in R. Towards the end, in our demo, we will be predicting which patients have diabetes using Logistic Regression!

In this Logistic Regression Tutorial video you will understand:

1) The 5 Questions asked in Data Science
2) What is Regression?
3) Logistic Regression - What and Why?
4) How does Logistic Regression Work?
5) Demo in R: Diabetes Use Case
6) Logistic Regression: Use Cases

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#LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience

How it Works?

1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!

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About the Course

Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling 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. Analyse 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. Analyse 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

Customer Reviews:

[akshaydalvi]

This is the best video on Logistic Regression out there. Explained so beautifully and crystal clear. Loved it, Start to end. Keep on doing great work Hemant!

[smrazaabidi]

Really awesome, I had been stumbling upon different online courses regarding logit but not satisfied. Its really amazing, I am simply wordless. Your tone, voice, presentation and you beautifully impart complex information to audiences. Nailed it. Keep it up. You made my PhD interesting. :))

[md.hasanuzzaman]

This channel has the best tutorials for learning statistical topics with different Softwares. Awesome go ahead.

[VoyageSnippets]

Great video! I clearly understood LR now.

[abhishekdhyade]

The information about the ROCR curve was amazing. Thanks a lot. This tutorial is still helpful even after 3 years..!!

[belkassem]

Very clear explanation. Thank you Sir.

[utubeshake]

The explanation is very clear. A great learning material. Would be great if you get a chance to deep dive into data preparation and handling missing values and unclean data. Also some insights on how to deal with multicollinearity would help. But overall, a great starting point. Loved it!

[getachewaga]

Very useful video for those who want to learn logistic regression in the ML domain. Many thanks. Clear presentation with a step-by-step explanation of points. But some questions: in the model building process, can we say our final objective is to determine the threshold or what is it really? What would be the importance of this model for clinical diagnosis? Would it replace lab diagnosis or?

[brishtigayen]

Awesome..its totally mind blowing....its crystal clear now....logistic regression..👍👍

[shreyanshjain]

sir u explained very well and all these edureka lectures are so good all my doubts are clear... Thanks alot

[markostojkov]

Good explanation basically you do simple gradient descent and plug in the values in the logit formula and get probability

[vladamolevitch]

remarquably clear and informative, congratulations!

[twiggy]

Thank you very much. It is super easy to understand. Even I am newbie in R, but have basic coding knowledge I could use it for my research thanks to this tutorial.Thank you!

[deepkish]

Great video. You make R seem so easy. Step by step explanation was clear

[PawanSingh-iumi]

this is very good lecture. Step by step learning with demo. understood the concept in a good easy way !!

[indumathinagarajan]

Great work! I clearly understood the logistic regression. Thank you very much!

[madhabpatro]

thank u edureka & sir, for helping me to clear all my doubts related to logistic regression . excellent teaching . keep doing from Odisha ❤

[bollywoodhits-oldiesandnew]

This is simply amazing ! Very well done and many thanks !

[holisolsupplychainmanageme]

Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a dichotomous variable (in which there are only two possible outcomes).

[nicwals]

Great tutorial, thank you for sharing!