Author Archives: Ajitesh Kumar

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog,

Bias-Variance Trade-off in Machine Learning: Examples

Bias variance concepts and interview questions

Last updated: 5th Dec, 2023 The bias-variance trade-off is a fundamental concept in machine learning that presents a challenging dilemma for data scientists. It relates to the problem of simultaneously minimizing two sources of error that prevent supervised learning algorithms from generalizing beyond their training data. These two sources of error are related to Bias and Variance. Bias related errors refer to the error due to overly simplistic assumptions in the learning algorithm. Variance related errors refer to the error due to too much complexity in the learning algorithm. In this post, you will learn about the concepts of bias & variance in relation to the machine learning (ML) models. …

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Posted in Data Science, Interview questions, Machine Learning. Tagged with , , .

Different Types of CNN Architectures Explained: Examples

VGG16 CNN Architecture

Last updated: 4th Dec, 2023. In the fast-paced world of computer vision and image processing, the problem of image classification consistently stands out: the ability to effectively recognize and classify images. As we continue to digitize and automate our world, the demand for systems that can understand and interpret visual data is growing at an unprecedented rate. The challenge is not just about recognizing images – it’s about doing so accurately and efficiently. Traditional machine learning methods often fall short, struggling to handle the complexity and high dimensionality of image data. This is where Convolutional Neural Networks (CNNs) comes to rescue. And, there are different types of CNN architectures based …

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Posted in Deep Learning, Machine Learning. Tagged with , .

MongoDB – Commands to Check the Status of MongoDB Database

This article represents different commands which can be used to check the status of MongoDB database on Linux/Ubuntu. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. MongoDB Status Check Commands The following represents some of the commands that can be used to check the status of MongoDB database. Note that mongod represents the daemon process of MongDB databass and, primarily, used to manage database access. It is recommended to check the log file (/var/log/mongo/mongo.log) to get details. Following are some of the commands which can be used to get the status of Mongodb: service mongod status: Displays the status …

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Posted in NoSQL. Tagged with , .

Logit vs Probit Models: Differences, Examples

Logit vs probit models

Logit and Probit models are both types of regression models commonly used in statistical analysis, particularly in the field of binary classification. This means that the outcome of interest can only take on two possible values / classes. In most cases, these models are used to predict whether or not something will happen in form of binary outcome. For example, a bank might want to know if a particular borrower might default on loan or otherwise. In this blog post, we will explain what logit and probit models are, and we will provide examples of how they can be used. As data scientists, it is important to understand the concepts …

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Posted in Data Science, Machine Learning, statistics. Tagged with , .

Linear Regression Cost Function: Python Example

Cost function in linear regression

Linear regression is a foundational algorithm in machine learning and statistics, used for predicting numerical values based on input data. Understanding the cost function in linear regression is crucial for grasping how these models are trained and optimized. In this blog, we will understand different aspects of cost function used in linear regression including how it does help in building a regression model having high performance. What is a Cost Function in Linear Regression? In linear regression, the cost function quantifies the error between predicted values and actual data points. It is a measure of how far off a linear model’s predictions are from the actual values. The most commonly …

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Posted in Data Science, Machine Learning, Python. Tagged with , , .

KNN vs Logistic Regression: Differences, Examples

Difference between K-Nearest Neighbors (KNN) and Logistic Regression algorithms

In this blog, we will learn about the differences between K-Nearest Neighbors (KNN) and Logistic Regression, two pivotal algorithms in machine learning, with the help of examples. The goal is to understand the intricacies of KNN’s instance-based learning and Logistic Regression‘s probability modeling for binary and multinomial outcomes, offering clarity on their core principles. We will also navigate through the practical applications of K-NN and logistic regression algorithms, showcasing real-world examples in various business domains like healthcare and finance. Accompanying this, we’ll provide concise Python code samples, guiding you through implementing these algorithms with datasets. This dual focus on theory and practicality aims to equip you with both the understanding …

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Posted in Data Science, Machine Learning, Python. Tagged with , , .

6 Types of Brainstorming Techniques for Ideas Generation

Mind mapping brainstorming ideas

Last updated: 1st Dec, 2023 Generating innovative and creative ideas is a key component of success in many fields, from business and marketing to science, technology, and the arts. However, the process of coming up with new and unique ideas can be challenging, especially when faced with deadlines, limited resources, or creative blocks. This is where brainstorming or mindstorming comes into picture. Fortunately, there are several different types of brainstorming techniques that can help individuals and teams generate great ideas and innovate. While brainstorming is one of the most effective techniques out there, not all brainstorming sessions are created equal. The question that is frequently asked is how to brainstorm for effective …

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Python – How to Create Scatter Plot with IRIS Dataset


Last updated: 1st Dec, 2023 In this blog post, we will be learning how to create a Scatter Plot with the IRIS dataset using Python. The IRIS dataset is a collection of data that is used to demonstrate the properties of various statistical models. It contains information about 50 observations on four different variables: Petal Length, Petal Width, Sepal Length, and Sepal Width. As data scientists, it is important for us to be able to visualize the data that we are working with. Scatter plots are a great way to do this because they show the relationship between two variables. In this post, we learn how to plot IRIS dataset …

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Posted in Data Science, Python. Tagged with , , .

Chebyshev’s Theorem: Formula & Examples

chebyshev theorem for standard deviation

Chebyshev’s theorem is a fundamental concept in statistics that allows us to determine the probability of data values falling within a certain range defined by mean and standard deviation. This theorem makes it possible to calculate the probability of a given dataset being within K standard deviations away from the mean. It is important for data scientists, statisticians, and analysts to understand this theorem as it can be used to assess the spread of data points around a mean value. What is Chebyshev’s Theorem? Chebyshev’s Theorem, also known as Chebyshev’s Rule, states that in any probability distribution, the proportion of outcomes that lie within k standard deviations from the mean …

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Posted in Data Science, statistics. Tagged with , .

AIC in Logistic Regression: Formula, Example

Model evaluation using AIC in Logistic Regression

Have you as a data scientist ever been challenged by choosing the best logistic regression model for your data? As we all know, the difference between a good and the best model while training machine learning model can be subtle yet impactful. Whether it’s predicting the likelihood of an event occurring or classifying data into distinct categories, logistic regression provides a robust framework for analysts and researchers. However, the true power of logistic regression is harnessed not just by building models, but also by selecting the right model. This is where the Akaike Information Criterion (AIC) comes into play. In this blog, we’ll delve into different aspects of AIC, decode …

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Posted in Data Science, Machine Learning, Python, R. Tagged with , .

Linear Regression T-test: Formula, Example

Linear regression line slope 0

Last updated: 29th Nov, 2023 Linear regression is a popular statistical method used to model the relationship between a dependent variable and one or more independent variables. In linear regression, the t-test is a statistical hypothesis testing technique that is used to test the hypothesis related to linearity of the relationship between the response variable and different predictor variables. In this blog, we will discuss linear regression and t-test and related formulas and examples. For a detailed read on linear regression, check out my related blog – Linear regression explained with real-life examples. T-tests are used in linear regression to determine if a particular variable is statistically significant in the …

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Posted in Data Science, Python, R, statistics. Tagged with , , , .

Types of SQL Joins: Differences, SQL Code Examples

SQL Joins explained using Sets

Structured Query Language (SQL) is one of the most important and widely used tools for data manipulation. It allows users to interact with databases, query and manipulate data, and create reports. One of SQL’s most important features is its ability to join tables together in order to enrich, compare and analyze related data. These joins are termed as inner join, outer join, left join and right join. In this article, we will discuss the different types of joins available in SQL, their differences and provide examples of how each can be used. What is SQL Join? SQL Joins are a technique used in Structured Query Language (SQL) to combine two …

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Posted in Data, Data analytics, Database. Tagged with , .

30+ Logistic Regression Interview Questions & Answers

machine learning interview questions

Last updated: 29th Nov, 2023 This page lists down the practice tests / interview questions and answers for Logistic regression in machine learning. Those wanting to test their machine learning knowledge in relation with logistic regression would find these practice tests very useful. The goal for these practice tests is to help you check your knowledge in logistic regression machine learning models from time-to-time. More importantly, when you are preparing for interviews, these practice tests are intended to be handy enough. Those going for freshers / intern interviews in the area of machine learning would also find these practice tests / interview questions to be very helpful. These test primarily focus on …

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Posted in Data Science, Interview questions, Machine Learning. Tagged with , , .

Standard Deviation vs Standard Error: Formula, Examples

standard error plot

Understanding the differences between standard deviation and standard error is crucial for anyone involved in statistical analysis or research. These concepts, while related, serve different purposes in the realm of statistics. In this blog, we will delve into their differences, applications in research, formulas, and practical examples. Introduction to Standard Deviation & Standard Error At the heart of statistical analysis lies the need to understand and quantify variability. This is where standard deviation and standard error come into play. What’s standard deviation? Standard Deviation is a measure that reflects the amount of variation or dispersion within a dataset. It indicates how much individual data points deviate from the mean (average) …

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Posted in Data Science, statistics. Tagged with , .

Coefficient of Variation vs Standard Deviation

Coefficient of Variation Formula

Last updated: 28th Nov, 2023 Understanding the difference between coefficient of variation (CV) and standard deviation (SD) is essential for statisticians and data scientists. While both concepts measure variability in a dataset, they are calculated differently and can be used in different scenarios for better understanding. Here, we will explore the coefficient of variation vs standard deviation differences to gain a better understanding of how to use them. Coefficient of Variation vs Standard Deviation Coefficient of Variation (CV) is a measure that is used to compare the amount of variation in a dataset relative to its mean value. It is calculated by taking the standard deviation divided by the mean, then …

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Posted in Data Science, statistics. Tagged with , .

Difference: Binary vs Multiclass vs Multilabel Classification

Multilayer classifier to tag image with cat, dog, rooster and a donkey

Last updated: 28th Nov, 2023 There are three main types of classification algorithms when dealing with machine learning classification problems: Binary, Multiclass, and Multilabel. In this blog post, we will discuss the differences between them and how they can be used to solve different problems. Binary classifiers can only classify data into two categories, while multiclass classifiers can classify data into more than two categories. Multilabel classifiers assign or tag the data to zero or more categories. Let’s take a closer look at each type! Binary classification & examples Binary classification is a type of supervised machine learning problem that requires classifying data into two mutually exclusive groups or categories. …

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Posted in Data Science, Deep Learning, Machine Learning. Tagged with , .