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

Credit Risk Modeling & Machine Learning Use Cases

credit risk modeling and machine learning use cases

Have you ever wondered how banks and financial institutions decide who to lend money to, or how much to lend? The secret lies in credit risk modeling, a sophisticated approach that evaluates the likelihood of a borrower defaulting on their loan. Through in-depth analysis of historical data and borrower’s credit behavior, these models play a pivotal role in guiding lending decisions, managing risks, and ultimately, driving profitability. In the face of growing financial complexities, traditional methods are often insufficient. That’s where machine learning comes into play that helps better anticipate credit risk. By automating the identification of patterns within data, patterns that often go unnoticed by human analysis, machine learning …

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

Underwriting & Machine Learning Models Examples

underwriting and machine learning models examples

Are you curious about how AI / machine learning is revolutionizing the underwriting process? Have you ever wondered how machine learning models are reshaping risk assessment and decision-making in industries like insurance, lending, and securities? Underwriting has long been a critical process for assessing risks and making informed decisions, but with the advent of machine learning, the possibilities have expanded exponentially. By harnessing the immense capabilities of machine learning algorithms and the abundance of data available, organizations can extract actionable insights, achieve higher accuracy, and streamline their underwriting practices like never before. In this blog, we will learn about how machine learning models can be used effectively for underwriting processes, …

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Posted in Machine Learning. Tagged with .

Matplotlib Bar Chart Python / Pandas Examples


Are you looking to learn how to create bar charts / bar plots / bar graph using the combination of Matplotlib and Pandas in Python? Bar charts are one of the most commonly used visualizations in data analysis, enabling us to present categorical data in a visually appealing and intuitive manner. Whether you’re a beginner data scientist or an intermediate-level practitioner seeking to enhance your visualization skills, this blog will provide you with practical examples and hands-on guidance to create compelling bar charts / bar plots using Matplotlib libraries in Python. You will also learn how to leverage the data manipulation capabilities of Pandas to prepare the data for visualization, …

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Posted in Data Science, Python. Tagged with , .

One-hot Encoding Concepts & Python Examples

One-hot encoding concepts and python examples

Have you ever encountered categorical variables in your data analysis or machine learning projects? These variables represent discrete qualities or characteristics, such as colors, genders, or types of products. While numerical variables can be directly used as inputs for machine learning algorithms, categorical variables require a different approach. One common technique used to convert categorical variables into a numerical representation is called one-hot encoding, also known as dummy encoding. When working with machine learning algorithms, categorical variables need to be transformed into a numerical representation to be effectively used as inputs. This is where one-hot encoding comes to rescue. In this post, you will learn about One-hot Encoding concepts and …

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

Differences: Azure OpenAI vs OpenAI – Examples

azure openai vs openai differences

As the field of AI continues to evolve, the collaboration between Azure and OpenAI has brought forth a powerful combination of generative AI capabilities and enterprise-grade security. In this blog post, we will explore the differences between Azure OpenAI and OpenAI, with a focus on the benefits of Azure OpenAI in terms of security and model compatibility. What’s Azure, OpenAI & Azure OpenAI? Azure is Microsoft’s cloud computing platform that provides a comprehensive suite of services, tools, and infrastructure for building, deploying, and managing applications and services. It offers a wide range of capabilities, including virtual machines, storage solutions, databases, AI services, and more. OpenAI is a leading artificial intelligence …

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Posted in Azure, OpenAI. Tagged with , .

GPT Models In-context Learning: Examples

Have you ever wondered how AI models like OpenAI GPT-3 (Generative Pretrained Transformers-3) can generate impressively human-like text? Enter the realm of in-context learning that gives GPT-3 its conversational abilities and makes it extraordinary. In this blog, we’re going to learn the concepts of in-context learning, its different forms, and how GPT-3 uses it to revolutionize the way we interact with AI. What’s In-context Learning? In-context learning is at the heart of these large language models (LLMs), enabling GPT models to understand/comprehend and create text that closely resembles human speech, based on the instructions and examples they’re provided. As the model learns about the context based on the examples provided …

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Posted in Azure, ChatGPT, Generative AI, OpenAI. Tagged with , , , .

OpenAI GPT-3 Models List: Explained with Examples

GPT-3 model overview

In the ever-evolving landscape of natural language processing (NLP), OpenAI’s GPT-3 models have garnered significant attention for how they could understand and generate human-like text. Different GPT-3 models discussed in this blog can be accessed using APIs and OpenAI Playground. In this blog post, we will delve into the OpenAI GPT-3 models and provide a comprehensive list, along with explanations and examples of their capabilities. Whether you are an experienced data scientist or a curious generative ai enthusiast, understanding these models is crucial in making the most of the NLP capabilities of OpenAI GPT-3 models. GPT-3 Models Details & Examples The GPT-3 models offer different levels of power and speed …

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Posted in ChatGPT, Generative AI, OpenAI. Tagged with , .

Difference between Parametric vs Non-Parametric Models

When working with machine learning models, data scientists often come across a fundamental question: What sets parametric and non-parametric models apart? This is also one of the most frequent questions asked in the interviews. Machine learning models can be parametric or non-parametric. Parametric models are those that require the specification of some parameters before they can be used to make predictions, while non-parametric models do not rely on any specific parameter settings and therefore often produce more accurate results. These two distinct approaches play a crucial role in predictive modeling, each offering unique advantages and considerations. This blog post discusses parametric vs non-parametric machine learning models with examples along with …

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Posted in Data Science, Machine Learning. Tagged with .

What & When: List, Tuple & Set in Python – Examples

List, Tuple and Set in Python - When to use

When working with Python programming, data structures play a crucial role in organizing and manipulating data efficiently. Among several data structures available, lists, tuples, and sets are three fundamental ones that every Python programmer/developer should understand. Lists, tuples, and sets are unique in terms of their properties and functionality, making them most appropriate for different scenarios. Not only are these data structures most frequently used in everyday programming tasks, but they are also frequently asked about in interviews with data analysts and data scientists. Therefore, grasping the concepts of lists, tuples, and sets becomes essential. In this blog, we will delve deeper into the specifics of lists, tuples, and sets, …

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BI Analyst Career Path / Roadmap

BI Analyst Career Path Career Roadmap

Are you interested in a career that combines data analysis, technology, and business strategy? Look no further than the role of a Business Intelligence (BI) Analyst. In this blog post, we will explore the career path/career roadmap of a BI Analyst, highlighting the various job titles, and discussing the skills and responsibilities associated with this in-demand profession. What is Business Intelligence? Business Intelligence (BI) refers to the process of collecting, analyzing, and interpreting data to gain valuable insights that drive informed business decisions. It is an umbrella term that encompasses the tools, methodologies, and processes used to transform raw data into meaningful information and actionable insights. It involves the collection, …

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Posted in Business Intelligence, Career Planning, Data Visualization, jobs. Tagged with , , .

Ridge Regression Concepts & Python example

Ridge regression cost function 2

Ridge regression is a type of linear regression that penalizes ridge coefficients. This technique can be used to reduce the effects of multicollinearity in ridge regression, which may result from high correlations among predictors or between predictors and independent variables. In this tutorial, we will explain ridge regression with a Python example. What is Ridge Regression? Ridge regression is a powerful technique in machine learning that addresses the issue of overfitting in linear models. In linear regression, we aim to model the relationship between a response variable and one or more predictor variables. However, when there are multiple variables that are highly correlated, the model can become too complex and …

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

Microsoft Designer: Transforming Design with AI

microsoft designer for AI-powered designs

Are you looking for a hassle-free and smart way to make impressive designs without any graphic design expertise? Then Microsoft’s newly launched tool, Microsoft Designer, may just be the answer. The realm of digital design welcomes a new entrant that promises to revolutionize the way we create – Microsoft’s latest offering, Microsoft Designer. Drawing parallels with the popular tool Canva, Microsoft Designer is an innovative platform that brings effortless design capabilities to your fingertips. This user-friendly, AI-powered design platform caters to an extensive array of users, from entrepreneurs and marketers to educators and students, and even to social media enthusiasts. Whether you are a seasoned professional looking to expedite your …

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Posted in Generative AI, Tools. 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 mongodĀ represents the daemon process of MongDB databass and, primarily, used to manage database access. Following are some of the commands which can be used to get the status of Mongodb: service mongod status: Displays the status of MongodB service as like the screenshot given below. systemctl status mongod: …

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Posted in NoSQL. Tagged with , .

Bayesian Machine Learning Applications Examples

bayesian machine learning appplications examples

Have you ever wondered how machines can make decisions with uncertainty? What if there was anĀ  approach in machine learning that not only learned from data but also quantified and managed uncertainty in a principled way? Enter the realm of Bayesian machine learning. Bayesian machine learning is one of the most powerful modeling technique in predictive analytics. It marries the probabilistic reasoning with machine learning algorithms. Bayes’ theorem, which was first introduced by Reverend Thomas Bayes in 1763, provides a way to infer probabilities from observations. Bayesian machine learning has become increasingly popular because it can be used for real-world applications such as spam filtering (NLP), credit card fraud detection, …

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Azure Machine Learning Studio: Getting Started

Azure machine learning studio Tutorial

Azure Machine Learning Studio is a powerful cloud-based platform that brings the world of machine learning to your fingertips. Whether you’re a data scientist, a developer, or a business professional, Azure Machine Learning Studio provides a user-friendly and collaborative environment to build, train, and deploy machine learning models with ease. This blog post serves as a quick tutorial to help you get started with Azure Machine Learning Studio. From setting up your workspace to exploring key features and best practices, we will walk you through the essential steps to embark on your machine learning journey. Azure ML Studio – Machine Learning Pipeline Before we can proceed with the tasks in …

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Posted in Azure, Machine Learning. Tagged with , .

Coefficient of Variation vs Standard Deviation

Coefficient of Variation Formula

Understanding the difference between coefficient of variation and standard deviation 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 differences between these two measures to gain a better understanding of how to use them. What is Coefficient of Variation? 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 multiplying by 100. CV can be interpreted as the …

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Posted in Data Science, statistics. Tagged with , .