Table of contents

  1. Introduction
  2. Problem statement
  3. ML formulation
  4. Performance Metric
  5. Understanding the data
  6. Data Preparation
  7. Modeling
  8. Training
  9. Prediction of segmentation maps on test data
  10. Future Works
  11. References

1. Introduction


Table of Contents

  1. Overview
  2. Prerequisites
  3. Business Problem
  4. Data Analysis
  5. ML Formulation
  6. Performance Metric
  7. Data preparation
  8. Modeling
  9. Comparison of models
  10. Model Deployment
  11. Future Works
  12. Profile
  13. References

1. Overview


What is Logistic regression?

  • To classify an email into spam or not spam
  • To predict whether a patient has cancer or not

Geometric Intuition


Table of Contents

  1. Overview
  2. Business Problem
  3. Dataset Analysis
  4. Mapping the Real Problem into ML problem
  5. Performance Metric
  6. Exploratory Data Analysis
  7. Feature Engineering
  8. Data preprocessing
  9. Modeling
  10. Future improvements
  11. Results
  12. References

Overview

Vinithavn

Machine Learning| Data science

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store