- Home
- Courses
- Machine Learning
- ML-03: LGR, SVM, NBC and KNN
ML-03: LGR, SVM, NBC and KNN
This will cover the four major Supervised Learning Techniques
Overview
This will cover the four major Supervised Learning Techniques:
- Logistic Regression (LGR)
- Support Vector Machines (SVM)
- Naïve Bayes Classifier (NBC)
- K-Nearest Neighbors (KNN)
Language: R and MS Excel
Program Duration: 4 days of 2.5 hr each
Pre-requisites:
- Attended our SFDS-01 (Basic Statistics with R) program, OR
- Should pass a short online exam (MCQ type) conducted by us which will test basic statistical concepts and R
Audience: folks in ML and Data Science, as well as those who want to enter these fields
ML-03: LGR, SVM, NBC and KNN – Detailed Program Content
LGR
- Odds Ratio and Logit function
- Code
SVM
- Hyperplane and Maximal Margin Classifier
- Algorithm and code
NBC:
- Explanation based on Bayes’ Theorem
- Algorithm and code
KNN
- Deciding the number of neighbors k
- Comparison of KNN with above methods
- Algorithm and code
Curriculum
Instructor
Ramdas Menon has an M.Tech in chemical engineering from IIT-M, and was a Six Sigma Black Belt at GE and Pfizer before setting up Ekaagra. He holds two certifications from the ASQ - CSSBB and CRE - and has conducted programs on Statistical Methods including DOE at several corporates such as Huntsman, Pidilite, TVS Srichakra, Dr Reddys and MRF from 2008 onwards.
Reviews
You May Like
Artificial Intelligence for Anyone Interested (AI4AI)
This gives an overview of Artificial Intelligence and Machine Learning for beginners
Statistical Fundamentals of Data Science Part-1 (SFDS-01)
Learn to analyze and visualize data in R and gain proficiency in descriptive statistics, probability, hypothesis testing, and regression.
Regression (ML-01)
The course covers Regression in great detail.
Statistics for Non Statisticians (SFNS)
Sat 6 July 2024 Timings 1100-1330 IST via Google Meet Sat 6 July 2024 Timings 1100-1330 IST via Google Meet Do you feel utterly...
Design of Experiments Part-1 (DOE-01): Basic DOE with R
This course covers the fundamentals of the design and analysis of experiments (DoE). Using these principles, you will learn to critically analyze experimental data...

Course Features
- Lectures 0
- Quizzes 0
- Duration 4 days
- Skill level All levels
- Language English
- Students 60
- Assessments Yes