# Author Archives: Ajitesh Kumar

## Convex optimization explained: Concepts & Examples

Prescriptive analytics plays a significant role in helping organizations make informed decisions by recommending the best course of action given a specific situation. Unlike descriptive and predictive analytics, which focus on understanding past data and predicting future outcomes, prescriptive analytics aims to optimize decision-making processes. Optimization solutions are a key component of prescriptive analytics, enabling decision-makers to find the most efficient and effective strategies to achieve their goals. Convex Optimization is a special class of optimization problems that deals with minimizing (or maximizing) convex functions over convex sets. Convex functions and sets exhibit specific mathematical properties that make them particularly well-suited for optimization. In the context of prescriptive analytics, convex …

## Linear Regression Explained with Real Life Example

In this post, the linear regression concept in machine learning is explained with multiple real-life examples. Both types of regression models (simple/univariate and multiple/multivariate linear regression) are taken up for sighting examples. 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 on what is machine learning – What is Machine Learning? Concepts & Examples. Before going into the details, lets look at a small poem which can help us remember the concept of linear regression. Hope you like it. Linear Regression, a machine learning delight Fitting a line, to make predictions right …

## Why & When to use Eigenvalues & Eigenvectors?

Eigenvalues and eigenvectors are important concepts in linear algebra that have numerous applications in data science. They provide a way to analyze the structure of linear transformations and matrices, and are used extensively in many areas of machine learning, including feature extraction, dimensionality reduction, and clustering. In simple terms, eigenvalues and eigenvectors are the building blocks of linear transformations. Eigenvalues represent the scaling factor by which a vector is transformed when a linear transformation is applied, while eigenvectors represent the directions in which the transformation occurs. In this post, you will learn about why and when you need to use Eigenvalues and Eigenvectors? As a data scientist/machine learning Engineer, one must …

## Z-score or Z-statistics: Concepts, Formula & Examples

Z-score, also known as the standard score or Z-statistics, is a powerful statistical concept that plays a vital role in the world of data science. It provides a standardized method for comparing data points from different distributions, allowing data scientists to better understand and interpret the relative positioning of individual data points within a dataset. Z-scores represent a statistical technique of measuring the deviation of data from the mean. It is also used with Z-test which is a hypothesis testing statistical technique (one sample Z-test or two samples Z-test). As a data scientist, it is of utmost importance to be well-versed with the z-score formula and its various applications. Having …

## Histogram Plots using Matplotlib & Pandas: Python

Histograms are a graphical representation of the distribution of data. In Python, there are several ways to create histograms. One popular method is to use the Matplotlib library. In this tutorial, we will cover the basics of Histogram Plots and how to create different types of Histogram plots using the popular Python libraries, Matplotlib and Pandas. We will also explore some real-world examples to demonstrate the usefulness of Histogram Plots in various industries and applications. As data scientists, it is important to learn how to create visualizations to communicate our findings. Histograms are one way to do this effectively. What are Histogram plots? Histogram plots are a way of representing …

## Machine Learning – Sensitivity vs Specificity Difference

Machine learning (ML) models are increasingly being used to learn from data and make decisions or predictions based on that learning. When it comes to evaluating the performance of these ML models, there are several important metrics to consider. One of the most important metrics is the accuracy of the model, which is typically measured using sensitivity and specificity. These two metrics are critical in determining the effectiveness of a machine learning model In this post, we will try and understand the concepts behind machine learning model evaluation metrics such as sensitivity and specificity. The post also describes the differences between sensitivity and specificity. You may want to check out another …

## Descriptive Statistics – Key Concepts & Examples

Descriptive statistics is a branch of statistics that deals with the analysis of data. It is concerned with summarizing and describing the characteristics of a dataset. It is one of the most fundamental tool for data scientists to understand the data as they get started working on the dataset. In this blog post, I will cover the key concepts of descriptive statistics, including measures of central tendency, measures of spread and statistical moments. What’s Descriptive Statistics & Why do we need it? Descriptive statistics is used to summarize and describe the characteristics of a dataset in terms of understanding its mean & related measures, spread or dispersion of the data …

## Amazon Bedrock to Democratize Generative AI

Amazon Web Services (AWS) has announced the launch of Amazon Bedrock and Amazon Titan foundational models (FMs), making it easier for customers to build and scale generative AI applications with foundation models. According to AWS, they received feedback from their select customers that there are a few big things standing in their way today in relation to different AI use cases. First, they need a straightforward way to find and access high-performing FMs that give outstanding results and are best-suited for their purposes. Second, customers want integration into applications to be seamless, without having to manage huge clusters of infrastructure or incur large costs. Finally, customers want it to be …

## Backpropagation Algorithm in Neural Network: Examples

Artificial Neural Networks (ANN) are a powerful machine learning / deep learning technique inspired by the workings of the human brain. Neural networks comprise multiple interconnected nodes or neurons that process and transmit information. They are widely used in various fields such as finance, healthcare, and image processing. One of the most critical components of an ANN is the backpropagation algorithm. Backpropagation algorithm is a supervised learning technique used to adjust the weights of a Neural Network to minimize the difference between the predicted output and the actual output. In this post, you will learn about the concepts of backpropagation algorithm used in training neural network models, along with Python …

## 6 Brainstorming Techniques for Generating Great Ideas

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. Fortunately, there are several effective brainstorming techniques that can help individuals and teams generate great ideas and overcome obstacles to innovation. When it comes to generating great ideas, brainstorming is one of the most effective techniques out there. But not all brainstorming sessions are created equal. In order for a brainstorming session to be successful, you need to use the right techniques. …

## K-Means Clustering Python Example

Clustering is a popular unsupervised machine learning technique used in data analysis to group similar data points together. The K-Means clustering algorithm is one of the most commonly used clustering algorithms due to its simplicity, efficiency, and effectiveness on a wide range of datasets. In K-Means clustering, the goal is to divide a given dataset into K clusters, where each data point belongs to the cluster with the nearest mean value. The algorithm works by iteratively updating the cluster centroids until convergence is achieved. In this post, you will learn about K-Means clustering concepts with the help of fitting a K-Means model using Python Sklearn KMeans clustering implementation. You will …

## Lasso Regression Explained with Python Example

Lasso regression, also known as L1 regularization, is a linear regression method that uses regularization to prevent overfitting and improve model performance. It works by adding a penalty term to the cost function that encourages the model to select only the most important features and set the coefficients of less important features to zero. This makes Lasso regression a popular method for feature selection and high-dimensional data analysis. In this post, you will learn concepts, advantages and limitations of Lasso regression along with Python Sklearn examples. The other two similar forms of regularized linear regression are Ridge regression and Elasticnet regression which will be discussed in future posts. What’s Lasso Regression? …

## SVM RBF Kernel Parameters: Python Examples

Support vector machines (SVM) are a popular and powerful machine learning technique for classification and regression tasks. SVM models are based on the concept of finding the optimal hyperplane that separates the data into different classes. One of the key features of SVMs is the ability to use different kernel functions to model non-linear relationships between the input variables and the output variable. One such kernel is the radial basis function (RBF) kernel, which is a popular choice for SVMs due to its flexibility and ability to capture complex relationships between the input and output variables. The RBF kernel has two important parameters: gamma and C (also called regularization parameter). …

## Ordinary Least Squares Method: Concepts & Examples

Regression analysis is a fundamental statistical technique used in many fields, from finance to social sciences. It involves modeling the relationship between a dependent variable and one or more independent variables. The Ordinary Least Squares (OLS) method is one of the most commonly used techniques for regression analysis. Ordinary least squares (OLS) is a linear regression technique used to find the best-fitting line for a set of data points by minimizing the residuals (the differences between the observed and predicted values). It does so by estimating the coefficients of a linear regression model by minimizing the sum of the squared differences between the observed values of the dependent variable and …

## PCA Explained Variance Concepts with Python Example

Dimensionality reduction is an important technique in data analysis and machine learning that allows us to reduce the number of variables in a dataset while retaining the most important information. By reducing the number of variables, we can simplify the problem, improve computational efficiency, and avoid overfitting. Principal Component Analysis (PCA) is a popular dimensionality reduction technique that aims to transform a high-dimensional dataset into a lower-dimensional space while retaining most of the information. PCA works by identifying the directions that capture the most variation in the data and projecting the data onto those directions, which are called principal components. However, when we apply PCA, it is often important to …

## PCA vs LDA Differences, Plots, Examples

Dimensionality reduction is an important technique in data analysis and machine learning that allows us to reduce the number of variables in a dataset while retaining the most important information. By reducing the number of variables, we can simplify the problem, improve computational efficiency, and avoid overfitting. Two popular dimensionality reduction techniques are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Both techniques aim to reduce the dimensionality of the dataset, but they differ in their objectives, assumptions, and outputs. But how do they differ, and when should you use one method over the other? As data scientists, it is important to get a good understanding around this concept …