Author Archives: Ajitesh Kumar
Loss Function vs Cost Function vs Objective Function: Examples
Among the terminologies used in training machine learning models, the concepts of loss function, cost function, and objective function often cause a fair amount of confusion, especially for aspiring data scientists and practitioners in the early stages of their careers. The reason for this confusion isn’t unfounded, as these terms are similar / closely related, often used interchangeably, and yet, they are different and serve distinct purposes in the realm of machine learning algorithms. Understanding the differences and specific roles of loss function, cost function, and objective function is more than a mere exercise in academic rigor. By grasping these concepts, data scientists can make informed decisions about model architecture, …
Data Science Competitions on Different Online Platforms
Data science / Machine Learning is an ever-evolving field, and competitions provide a great way for beginners / practitioners to hone their skills, solve real-world problems, enhance their resumes / CVs and even earn rewards. Here’s a roundup of some notable machine learning / data science / AI competition platforms, each offering unique opportunities. Each of these data science competition platforms offers unique opportunities and challenges, making them ideal for both beginners and expert data scientists at various stages of their careers to learn, compete, and contribute to a wide array of problems.
Mastering Data Quality KPI Dashboards: Concepts, Examples
In the digital age, where data is often likened to the new oil, ensuring its quality is not just an operational necessity but a strategic imperative. In every organization, from small startups to global enterprises, the ability to trust and accurately interpret data can be the difference between insightful business decisions and costly missteps. This is where data quality Key Performance Indicators (KPIs) and their visualization through dashboards become pivotal. In this blog, we aim to navigate you through the multifaceted world of data quality, focusing on understanding, designing, and implementing effective KPI dashboards. Whether you’re a data analyst, a business intelligence professional, or just someone passionate about data-driven decision-making, …
Large Language Models (LLMs) & Semantic Search: Examples
Have you ever marveled at how typing a few words into a search engine yields exactly the information you’re looking for from the vast expanse of the web? This is largely thanks to the advancements in semantic search, bolstered by technologies like Large Language Models (LLMs). Semantic search, which focuses on understanding the intent and contextual meaning behind queries, benefits from LLMs to provide more accurate and relevant results. However, it’s important to note that traditional search engines also rely on a sophisticated mix of algorithms, indexing, and ranking systems. LLMs complement these systems by enhancing their ability to interpret complex queries, making your search experience more intuitive and effective. …
Online Scatter Plot Maker – Works with Your Excel Data
This online free tool, scatter plot creator, is designed to make data scatter plot visualization simpler, more efficient, and more integrated into your reporting. Scatter plots are invaluable for examining the relationship between two variables, identifying trends, outliers, and patterns. Whether you’re a data scientist, researcher, or data enthusiast, this tool will enable you to visualize your data effectively using scatter plots. The following are key features of this online scatter plot maker tool: Create Scatter Plots
Independent Samples T-test: Formula, Examples, Calculator
Last updated: 21st Dec, 2023 As a data scientist, you may often come across scenarios where you need to compare the means of two independent samples. In such cases, a two independent samples t-test, also known as unpaired two samples t-test, is an essential statistical tool that can help you draw meaningful conclusions from your data. This test allows you to determine whether the difference between the means of two independent samples is statistically significant or due to chance. In this blog, we will cover the concept of independent samples t-test, its formula, real-world examples of its applications and the Python & Excel example (using scipy.stats.ttest_ind function). We will begin …
Introducing Our New Data Science & AI Trends Page
We are thrilled to announce the launch of our dedicated Data Science and AI Trends page at VitalFlux.com! This new resource is designed to be a one-stop hub for data scientists, AI enthusiasts, and anyone passionate about staying at the forefront of technological innovation. What You’ll Find Our Data Science & AI Trends page is more than just a collection of articles; it’s a dynamic resource that aggregates the most insightful and current information from various high-impact sources. Here’s a sneak peek at what you can expect: Web Pages Stay informed with our selection of web pages from leading research institutions, tech news outlets, and individual thought leaders in the …
Z-test vs T-test: Formula, Examples
Last updated: 18th Dec, 2023 When it comes to statistical tests, z-test and t-test are two of the most commonly used. But what is the difference between z-test and t-test? And when to use z-test vs t-test? In this relation, we also wonder about z-statistics vs t-statistics. And, the question arises around what’s the difference between z-statistics and t-statistics. In this blog post, we will answer all these questions and more! We will start by explaining the difference between z-test and t-test in terms of their formulas. Then we will go over some examples so that you can see how each test is used in practice. As data scientists, it …
Python – Replace Missing Values with Mean, Median & Mode
Last updated: 18th Dec, 2023 Have you found yourself asking question such as how to deal with missing values in data analysis stage? When working with Python, have you been troubled with question such as how to replace missing values in Pandas data frame? Well, missing values are common in dealing with real-world problems when the data is aggregated over long time stretches from disparate sources, and reliable machine learning modeling demands for careful handling of missing data. One strategy is imputing the missing values, and a wide variety of algorithms exist spanning simple interpolation (mean, median, mode), matrix factorization methods like SVD, statistical models like Kalman filters, and deep …
Standard Deviation of Population vs Sample
Last updated: 18th Dec, 2023 Have you ever wondered what the difference between standard deviation of population and a sample? Or why and when it’s important to measure the standard deviation of both? In this blog post, we will explore what standard deviation is, the differences between the standard deviation of population and samples, and how to calculate their values using their formula and Python code example. By the end of this post, you should have a better understanding of standard deviation in general and why it’s important to calculate it for both populations and samples. Check out my related post – coefficient of variation vs standard deviation. What is …
Tool – Machine Learning Algorithm Cheat Sheet
Here is a comprehensive and user-friendly tool designed to bridge the gap between complex machine learning concepts and practical understanding. Whether you’re a student, educator, data scientist, or just a curious learner, this tool is your go-to resource for quick insights into some of the most popular and widely used machine learning algorithms. From Linear Regression to more advanced techniques like XGBoost and Principal Component Analysis, the plugin offers a succinct summary of each algorithm, including its definition, typical use cases, and applicable Python and R libraries. Select a Machine Learning Algorithm Select a machine learning algorithm from the drop-down to view and learn the details. Select a Feature Scaling …
One-Sample T-Test Calculator
Here are two different methods of calculating t-statistics for one-sample t-test. In method 1, you enter the dataset. In method 2, you provide the sample mean, sample standard deviation and sample size. Here are the common set of inputs. One of the input field is the hypothesized mean, which is the population mean you expect or hypothesizes before conducting the test. This value is crucial for comparison against the sample mean. By default, it is set to 0, but you can modify it based on their hypothesis. The significance level, another critical input, is pre-set at 0.05 but can be adjusted. This level determines the threshold for statistical significance. In …
One Sample T-test: Formula & Examples
Last updated: 16th Dec, 2023 In statistics, the t-test is often used in research when the researcher wants to know if there is a significant difference between the mean of sample and the population, or whether there is a significant difference between the means of two groups (unpaired / independent or paired). There are three types of t-tests: the one sample t-test, two samples or independent samples t-test, and paired samples t-test. In this blog post, we will focus on the one sample t-test and explain with formula and examples. As data scientists, it is important for us to understand the concepts of t-test and how to use it in …
Linear Regression vs. Polynomial Regression: Python Examples
In the realm of predictive modeling and data science, regression analysis stands as a cornerstone technique. It’s essential for understanding relationships in data, forecasting trends, and making informed decisions. This guide delves into the nuances of Linear Regression and Polynomial Regression, two fundamental approaches, highlighting their practical applications with Python examples. What are Linear and Polynomial Regression? In this section, we will learn about what are linear and polynomial regression. What is Linear Regression? Linear Regression is a statistical method used in predictive analysis. It’s a straightforward approach for modeling the relationship between a dependent variable (often denoted as y) and one or more independent variables (denoted as x). In …
Linear Regression vs Logistic Regression: Python Examples
Last updated: 15th Dec, 2023 In the ever-evolving landscape of machine learning, two regression algorithms stand out for their simplicity and effectiveness: Linear Regression and Logistic Regression. But what exactly are these algorithms, and how do they differ from each other? At first glance, logistic regression and linear regression might seem very similar – after all, they share the word “regression.” However, the devil, as they say, is in the details. Each method is uniquely tailored to solve specific types of problems, and understanding these subtleties is key to unlocking their full potential. Linear regression and logistic regression are both machine learning algorithms used for modeling relationships between variables but …
Linear Regression in Machine Learning: Python Examples
Last updated: 15th Dec, 2023 In this post, the linear regression concepts in machine learning is explained with multiple real-life examples. Two types of regression models (simple/univariate and multiple/multivariate linear regression) are taken up for sighting examples. In addition, Python code examples are used for demonstrating training of simple linear and multiple linear regression models. In case you are a machine learning or data science beginner, you may find this post helpful enough. You may also want to check a detailed post – What is Machine Learning? Concepts & Examples. What is Linear Regression? Linear regression is a machine learning concept that is used to build or train the models …
I found it very helpful. However the differences are not too understandable for me