If you are familiar with machine learning, particularly deep learning, you might have heard of the term transfer learning. What is transfer learning? In this blog, we are going to discuss about

  1. What is transfer learning
  2. How to use transfer learning
  3. Benefits and limitations of transfer learning

Need for transfer learning

As the word suggests, transfer learning is a process of transferring the learning or knowledge acquired from a particular task. How is this applicable in deep learning?

In machine learning, for performing every task, we need to train a model right? …


If you are reading this blog, you will probably be familiar with machine learning or will be interested in learning the same. Machine learning is a subfield of artificial intelligence, where it makes the systems to learn from data and make them capable of taking decisions with minimal human intervention. Now generally, we use the word “model” to indicate this intelligent system. Now, suppose we have a model which is designed to perform a particular task. This task can be anything like, for example, classifying the emails as not spam and spam, or an image classification problem. …

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

What is Object Detection?

Given an image, we humans can identify the objects present in that image. For example, we can detect whether the image has a car, trees, people, etc. If we can analyze the images and detect the objects, can we also teach machines to do the same?

The answer is yes. With the rise of deep learning and computer vision, we can automate object detection. We can build deep learning and computer vision…

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

Image captioning is one of the most important and challenging tasks in deep learning. It is the process of generation of a textual description for an image. For example, consider the below images.

What is Logistic regression?

Logistic regression is a simple classification algorithm where the output or the dependent variable is categorical. For example:

  • To predict whether a patient has cancer or not

Logistic regression uses a logistic function for this purpose and hence the name. This algorithm can be thought of as a regression problem even though it does classification. Because instead of just giving the class, logistic regression can tell us the probability of a data point belonging to each of the classes. We will see the details in the coming sessions.

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



This was a competition conducted by Kaggle where we need to build a model to detect troubling events from aircrew’s physiological data.

Aviation fatality means the death of one or more persons inside or outside of an aircraft, spacecraft, or any other aerospace vehicle that occurs during a flight operation or any other operation involving that vehicle. …


Machine Learning| Data science

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