# Author Archives: Ajitesh Kumar

## Bias-Variance Trade-off Concepts & Interview Questions

Bias and variance are two important properties of machine learning models. In this post, you will learn about the concepts of bias & variance in relation to the machine learning (ML) models. Bias refers to how well your model can represent all possible outcomes, whereas variance refers to how sensitive your predictions are to changes in the model’s parameters. The tradeoff between bias and variance is a fundamental problem in machine learning, and it is often necessary to experiment with different model types in order to find the balance that works best for a given dataset. In addition to learning the concepts related to Bias vs variance trade-off, you would …

## Hypothesis Testing Explained with Examples

Hypothesis testing is a statistical technique that helps scientists and researchers test the validity of their claims about real-world/real-life events. Hypothesis testing techniques are often used in statistics and data science to analyze whether the claims about the occurrence of the events are true. This blog post will cover some of the key statistical concepts along with examples in relation to how to formulate hypotheses for hypothesis testing. The knowledge of hypothesis formulation and hypothesis testing holds the key to solving business problems using data science. You may want to check out this post on how hypothesis testing is at the heart of data science – What is data science? In …

## What is Data Science? Concepts & Examples

What is data science? This is a question that many people are asking, and for good reason. Data science is a relatively new field, and it covers a lot of ground. In this blog post, we will discuss what data science is, and we will give some examples of how it can be used to solve problems. Stay tuned, because by the end of this post you will have a clear understanding of what data science is and why it matters! What is Data Science? Before understanding what is data science, let’s understand what is science? Science can be defined as a systematic and logical approach to discovering how things …

## Machine Learning – Sensitivity vs Specificity Difference

In this post, we will try and understand the concepts behind machine learning model evaluation metrics such as sensitivity and specificity which is used to determine the performance of the machine learning models. The post also describes the differences between sensitivity and specificity. The concepts have been explained using the model for predicting whether a person is suffering from a disease or not. You may want to check out another related post titled ROC Curve & AUC Explained with Python examples. What is Sensitivity Sensitivity is a measure of how well a machine learning model can detect positive instances. It is also known as the true positive rate (TPR) or recall. Sensitivity is …

## Stochastic Gradient Descent Python Example

In this post, you will learn the concepts of Stochastic Gradient Descent (SGD) using a Python example. Stochastic gradient descent is an optimization algorithm that is used to optimize the cost function while training machine learning models. The most popular algorithm such as gradient descent takes a long time to converge for large datasets. This is where the variant of gradient descent such as stochastic gradient descent comes into the picture. In order to demonstrate Stochastic gradient descent concepts, the Perceptron machine learning algorithm is used. Recall that Perceptron is also called a single-layer neural network. Before getting into details, let’s quickly understand the concepts of Perceptron and the underlying learning …

## Dummy Variables in Regression Models: Python, R

In linear regression, dummy variables are used to represent the categorical variables in the model. There are a few different ways that dummy variables can be created, and we will explore a few of them in this blog post. We will also take a look at some examples to help illustrate how dummy variables work. We will also understand concepts related to the dummy variable trap. By the end of this post, you should have a better understanding of how to use dummy variables in linear regression models. As a data scientist, it is important to understand how to use linear regression and dummy variables. What are dummy variables in …

## Linear vs Non-linear Data: How to Know

In this post, you will learn the techniques in relation to knowing whether the given data set is linear or non-linear. Based on the type of machine learning problems (such as classification or regression) you are trying to solve, you could apply different techniques to determine whether the given data set is linear or non-linear. For a data scientist, it is very important to know whether the data is linear or not as it helps to choose appropriate algorithms to train a high-performance model. You will learn techniques such as the following for determining whether the data is linear or non-linear: Use scatter plot when dealing with classification problems Use …

## How to deal with Class Imbalance in Python

In this post, you will learn about how to deal with class imbalance by adjusting class weight while solving a machine learning classification problem. This will be illustrated using Sklearn Python code example. What is Class Imbalance? Class imbalance refers to a problem in machine learning where the classes in the data are not equally represented. For example, if there are 100 data points and 90 of them belong to Class A and 10 belong to Class B, then the classes are imbalanced. Class imbalance can lead to problems with training machine learning models because the models may be biased towards the more common class. If there are more examples …

## Linear regression hypothesis testing: Concepts, Examples

In relation to machine learning, linear regression is defined as a predictive modeling technique that allows us to build a model which can help predict continuous response variables as a function of a linear combination of explanatory or predictor variables. While training linear regression models, we need to rely on hypothesis testing in relation to determining the relationship between the response and predictor variables. In the case of the linear regression model, two types of hypothesis testing are done. They are T-tests and F-tests. In other words, there are two types of statistics that are used to assess whether linear regression models exist representing response and predictor variables. They are …

## Differences between Random Forest vs AdaBoost

In this post, you will learn about the key differences between the AdaBoost classifier and the Random Forest algorithm. As data scientists, you must get a good understanding of the differences between Random Forest and AdaBoost machine learning algorithms. Both algorithms can be used for both regression and classification problems. Random forest and Adaboost are two popular machine learning algorithms. Both algorithms can be used for classification and regression tasks. Both Random Forest and AdaBoost algorithm is based on the creation of a Forest of trees. Random Forest is an ensemble learning algorithm that is created using a bunch of decision trees that make use of different variables or features …

## K-Nearest Neighbors Explained with Python Examples

In this post, you will learn about the K-nearest neighbors algorithm with Python Sklearn examples. K-nearest neighbors algorithm is used for solving both classification and regression machine learning problems. Introduction to K-Nearest Neighbors (K-NN) K-nearest neighbors is a supervised machine learning algorithm for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-nearest neighbors are used for classification or regression. The main idea behind K-NN is to find the K nearest data points, or neighbors, to a given data point and then predict the label or value of the given data point based on the labels or values …

## What is Web3.0? Features, Design, Skills, NFTs

What is Web3.0? Web3.0 is the next phase of the internet, which focuses on decentralization and security. It includes new technologies like blockchain, which is revolutionizing how we interact with the internet. To be successful in this new era of the internet, you will need to have a variety of different skills. In this blog post, we will discuss what those skills are and how you can acquire them! What is Web3.0? Web3.0 is the third generation of web development and design. It is a decentralized web that runs on a blockchain platform. Web3.0 is a new way of using the internet where users are in control of their data. …

## Correlation Concepts, Matrix & Heatmap using Seaborn

In this blog post, we’ll be discussing correlation concepts, matrix & heatmap using Seaborn. For those of you who aren’t familiar with Seaborn, it’s a library for data visualization in Python. So if you’re looking to up your data visualization game, stay tuned! We’ll start with the basics of correlation and move on to discuss how to create matrices and heatmaps with Seaborn. Let’s get started! Introduction to Correlation Correlation is a statistical measure that expresses the strength of the relationship between two variables. The two main types of correlation are positive and negative. Positive correlation occurs when two variables move in the same direction; as one increases, so do …

## Hidden Markov Models Explained with Examples

Hidden Markov models (HMMs) are a type of statistical modeling that has been used for several years. They have been applied in different fields such as medicine, computer science, and data science. The Hidden Markov model (HMM) is the foundation of many modern-day data science algorithms. It has been used in data science to make efficient use of observations for successful predictions or decision-making processes. This blog post will cover hidden Markov models with real-world examples and important concepts related to hidden Markov models. What are Markov Models? Markov models are named after Andrey Markov, who first developed them in the early 1900s. Markov models are a type of probabilistic …

## Gaussian Mixture Models: What are they & when to use?

Gaussian mixture models (GMMs) are a type of machine learning algorithm. They are used to classify data into different categories based on the probability distribution. Gaussian mixture models can be used in many different areas, including finance, marketing and so much more! In this blog, an introduction to gaussian mixture models is provided along with real-world examples, what they do and when GMMs should be used. What are Gaussian mixture models (GMM)? Gaussian mixture models (GMM) are a probabilistic concept used to model real-world data sets. GMMs are a generalization of Gaussian distributions and can be used to represent any data set that can be clustered into multiple Gaussian distributions. …

## Different types of Probability Distributions: Examples

In this post, you will learn the definition of 25 different types of probability distributions. Before we get into understanding different types of probability distributions, let’s understand some fundamentals. If you are a data scientist, you would like to go through these distributions. This page could also be seen as a cheatsheet for probability distributions. What are Probability Distributions? Probability distributions are a way of describing how likely it is for a random variable to take on a given value. In other words, they provide a way of quantifying the chances of something happening. Probability distributions are often graphed as histograms, with the possibilities on the x-axis and the probabilities …