# Author Archives: Ajitesh Kumar

## Machine Learning in Finance: Concepts & Examples

Machine learning has found its way into finance and is being used in various ways to improve the industry. Finance has always been a data-driven industry, and in recent years, machine learning has become an increasingly important tool for making sense of that data. In this blog post, we will explore some of these use cases and explain how machine learning is helping to make finance more efficient. We will also provide examples to help illustrate how machine learning is being used in finance. By the end of this post, you will have a better understanding of the many ways machine learning is benefiting finance and why it is such …

## Machine Learning with Alteryx: Examples

Alteryx is a self-service data analytics software platform that enables users to easily prep, blend, and analyze data all in one place. It is a powerful tool that can be used in a variety of machine learning scenarios. It can be used to clean and prepare data, and develop, evaluate and deploy machine learning (ML) models. It offers a variety of features and tools that can be used to preprocess data, choose algorithms, train models, and evaluate results. In this blog post, we will discuss some of the ways that Alteryx can be used in machine learning. We will also provide examples of how to use Alteryx in machine learning scenarios. …

## Logistic Regression Explained with Python Example

In this blog post, we will discuss the logistic regression machine learning algorithm with a python example. Logistic regression is a type of regression algorithm that is used to predict the probability of occurrence of an event. It is often used in machine learning applications. In this tutorial, we will use python to implement logistic regression for binary classification problems. What is Logistic Regression? Logistic regression is a machine learning algorithm used for classification problems. It is a supervised learning algorithm that can be used to predict the probability of occurrence of an event. The logistic regression algorithm is used to map the input data to a probability, unlike linear …

## Digital Transformation Strategy: What, Why & How?

Digital transformation is a digital strategy that aims to change the way an organization operates. It’s not just about digital marketing anymore – digital transformation includes all aspects of digital engagement from customer service, product development, and delivery, operations, etc. And it requires a holistic approach to digital transformation without any silos or strategic gaps in between departments. In this blog post, we will cover what digital transformation is and why organizations should take advantage of this strategy. We’ll also look at how digital transformation is happening in different industries. What is digital transformation? Digital transformation is a digital strategy that aims to change the way an organization operates and …

## Hate Speech Detection Using Machine Learning

Hate speech is a big problem on the internet. It can be found on social media, in comment sections, and even in online forums. Detecting hate speech is important because it can have harmful effects on society. In this blog post, we will discuss the latest techniques for detecting hate speech using machine learning algorithms. We will also provide examples of how these algorithms work. What is hate speech? Hate speech can be defined as any speech that targets a group of people based on their race, religion, ethnicity, national origin, sexual orientation, or gender identity. Hate speech is often used to spread hate and bigotry. It can also be …

## Analytical thinking & Reasoning: Real-life Examples

Analytical thinking and analytical reasoning are two concepts that are often misunderstood. Many people think that they are the same thing, but this is not the case. In fact, analytical thinking and analytical reasoning are two very different things. Analytical thinking is an important aspect of analytical skills. Most of us do not realize how to use analytical thinking and often end up solving the problem incorrectly or half-heartedly. As data analysts or data scientists, it would be of utmost importance to acquire this skill well. In this blog post, we will learn these concepts with the help of some real-life examples. What is analytical thinking? Analytical thinking is the …

## Difference between Data Science & Data Analytics

What’s the difference between data science and data analytics? Many people use these terms interchangeably, but there is a big distinction between the two fields. Data science is more focused on understanding and deriving insights from data, while data analytics is more focused on using pre-determined algorithms to make decisions or take action. In this blog post, we’ll explore the differences between data science and data analytics in greater detail, with examples of each. The following are key topics in relation to the difference between data science and data analytics: Different forms / purposes Different techniques Different Skillsets Different tools Different forms: Data Science & Data analytics Data science is …

## Machine Learning Use Cases for Climate Change

Climate change is a serious issue facing the world. The climate changes which are already affecting our planet can be seen in rising sea levels, melting ice caps and glaciers, more severe storms and hurricanes, more droughts, and wildfires increased precipitation in some areas of the world while other regions experience less rainfall. It’s important that we do what we can to reduce climate change risks by reducing greenhouse gas emissions into the atmosphere as well as adapting to climate impacts. Artificial intelligence (AI), machine learning (ML)/ deep learning (DL), advanced analytics have been widely used for decades across different industries such as finance, healthcare, etc., but their use cases …

## Machine Learning with Graphs: Free online course(Stanford)

Are you interested in learning the concepts of machine learning with Graphs? Stanford University is offering a free online course in machine learning titled Machine Learning with Graphs (CS224W). The lecture videos will be available on Canvas for all the enrolled Stanford students. The lecture slides and assignments will be posted online as the course progresses. This class will be offered next in Fall 2022. After completing this course, you will be able to apply machine learning methods to a variety of real-world problems. The course titled Machine learning with Graphs, will teach you how to apply machine learning methods to graphs and networks. Complex data can be represented as …

## Differences Between MLOps, ModelOps, AIOps, DataOps

In this blog post, we will talk about MLOps, AIOps, ModelOps and Dataops and difference between these terms. MLOps stands for Machine Learning Operations, AIOps stands for Artificial Intelligence-Operations (AI for IT operations), DataOps stands for Data operations and ModelOps stands for model operations. As data analytics stakeholders, it is important to understand the differences between MLOps, AIOps, Dataops, and ModelOps. For setting up AI/ML practice, it is important to plan to set up teams and practices around AIOps, MLOps/ModelOps and DataOps. What is MLOps? MLOps (or ML Operations) refers to the process of managing your ML workflows. It’s a subset of ModelOps that focuses on operationalizing ML models that …

## Business Analytics Team Structure: Roles/ Responsibilities

Business analytics is a business function that has been around for years, but it’s only recently gained traction as one of the most important business functions. Organizations are now realizing how business analytics can help them increase revenue and improve business operations. But before you bring on a business analytics team, you need to determine if your company needs full-time or part-time team members or both. It might seem logical to hire full-time staff members just because they’re in demand, but this isn’t always necessary. If your business operates without any external data sets and doesn’t have complex reporting and advanced analytics needs then it may be more cost-effective to …

## Linear Regression Interview Questions for Data Scientists

This page lists down 40 regression (linear/univariate, multiple/multilinear/multivariate) interview questions (in form of objective questions) which may prove to be helpful for Data Scientists / Machine Learning enthusiasts. Those appearing for interviews for machine learning/data scientist freshers/intern/beginners positions would also find these questions very helpful and handy enough to quickly brush up / check your knowledge and prepare accordingly. Practice Tests on Regression Analysis These interview questions are split into four different practice tests with questions and answers which can be found on following page: Linear, Multiple regression interview questions and answers – Set 1 Linear, Multiple regression interview questions and answers – Set 2 Linear, Multiple regression interview questions …

## Ordinary Least Squares Method: Concepts & Examples

Ordinary least squares (OLS) is a linear regression technique used to find the best-fitting line for a set of data points. It is a popular method because it is easy to use and produces decent results. In this blog post, we will discuss the basics of OLS and provide some examples to help you understand how it works. As data scientists, it is very important to learn the concepts of OLS before using it in the regression model. What is the ordinary least squares (OLS) method? The ordinary least squares (OLS) method can be defined as a linear regression technique that is used to estimate the unknown parameters in a …

## R-squared & Adjusted R-squared: Differences, Examples

There are two measures of the strength of linear regression models: adjusted r-squared and r-squared. While they are both important, they measure different aspects of model fit. In this blog post, we will discuss the differences between adjusted r-squared and r-squared, as well as provide some examples to help illustrate their meanings. As a data scientist, it is of utmost importance to understand the differences between adjusted r-squared and r-squared in order to select the most appropriate linear regression model out of different regression models. What is R-squared? R-squared is a measure of what proportion of the variance in the value of the dependent or response variable is explained by …

## R-squared, R2 in Linear Regression: Concepts, Examples

In linear regression, R-squared (R2) is a measure of how close the data points are to the fitted line. It is also known as the coefficient of determination. In this post, you will learn about the concept of R-Squared in relation to assessing the performance of multilinear regression machine learning model with the help of some real-world examples explained in a simple manner. Before doing a deep dive, you may want to access some of the following blog posts in relation to concepts of linear regression: Linear regression explained with real-world examples Linear regression hypothesis testing: concepts, examples Linear regression t-test: formula, examples Interpreting f-statistics in linear regression: formula, examples What …

## Interpreting f-statistics in linear regression: Formula, Examples

In this blog post, we will take a look at the concepts and formula of f-statistics in linear regression models and understand with the help of examples. F-test and F-statistics are very important concepts to understand if you want to be able to properly interpret the summary results of training linear regression machine learning models. We will start by discussing the importance of f-statistics in building linear regression models and understand how they are calculated based on the formula of f-statistics. We will, then, understand the concept with some real-world examples. As data scientists, it is very important to understand both the f-statistics and t-statistics and how they help in …