# Author Archives: Ajitesh Kumar

## 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. What is Linear Regression? Linear regression is a machine learning concept that is used to build or train the models (mathematical models or equations) for solving supervised learning problems related to predicting continuous numerical value. Supervised learning problems …

## Tensor Broadcasting Explained with Examples

In this post, you will learn about the concepts of Tensor Broadcasting with the help of Python Numpy examples. Recall that Tensor is defined as the container of data (primarily numerical) most fundamental data structure used in Keras and Tensorflow. You may want to check out a related article on Tensor – Tensor explained with Python Numpy examples. Broadcasting of tensor is borrowed from Numpy broadcasting. Broadcasting is a technique used for performing arithmetic operations between Numpy arrays / Tensors having different shapes. In this technique, the following is done: As a first step, expand one or both arrays by copying elements appropriately so that after this transformation, the two tensors have the …

## Regularization in Machine Learning: Concepts & Examples

In machine learning, regularization is a technique used to avoid overfitting. This occurs when a model learns the training data too well and therefore performs poorly on new data. Regularization helps to reduce overfitting by adding constraints to the model-building process. As data scientists, it is of utmost importance that we learn thoroughly about the regularization concepts to build better machine learning models. In this blog post, we will discuss the concept of regularization and provide examples of how it can be used in practice. What is regularization and how does it work? Regularization in machine learning represents strategies that are used to reduce the generalization or test error of …

## Difference: Binary, Multiclass & Multi-label Classification

There are three main types of classification algorithms when dealing with machine learning classification problems: Binary, Multiclass, and Multilabel. In this blog post, we will discuss the differences between them and how they can be used to solve different problems. Binary classifiers can only classify data into two categories, while multiclass classifiers can classify data into more than two categories. Multilabel classifiers assign or tag the data to zero or more categories. Let’s take a closer look at each type! Binary classification & examples Binary classification is a type of supervised machine learning problem that requires classifying data into two mutually exclusive groups or categories. The two groups can be …

## Measure Code Quality using Cyclomatic Complexity

The article talks about how McCabe’s cyclomatic complexity could be used to measure several different aspects of code quality. The objective of this article is to help developers quickly assess code quality by looking at the code. However, let’s try and quickly understand what is cyclomatic complexity and how could it be measured? Thanks for reading it further. And, apologies for spelling mistakes. What is Cyclomatic Complexity? Cyclomatic complexity is a measure of code quality that takes into account the number of independent paths through a piece of code. A high cyclomatic complexity indicates that a piece of code is more difficult to understand and maintain, and is, therefore, more …

## Most Common Machine Learning Tasks

This article represents some of the most common machine learning tasks that one may come across while trying to solve machine learning problems. Also listed is a set of machine learning methods that could be used to resolve these tasks. Please feel free to comment/suggest if I missed mentioning one or more important points. Also, sorry for the typos. You might want to check out the post on what is machine learning?. Different aspects of machine learning concepts have been explained with the help of examples. Here is an excerpt from the page: Machine learning is about approximating mathematical functions (equations) representing real-world scenarios. These mathematical functions are also referred …

## Statistics – Random Variables, Types & Python Examples

Random variables are one of the most important concepts in statistics. In this blog post, we will discuss what they are, their different types, and how they are related to the probability distribution. We will also provide examples so that you can better understand this concept. As a data scientist, it is of utmost importance that you have a strong understanding of random variables and how to work with them. What is a random variable and what are some examples? A random variable is a variable that can take on random values. The key difference between a variable and a random variable is that the value of the random variable …

## Frequentist vs Bayesian Probability: Difference, Examples

In this post, you will learn about the difference between Frequentist vs Bayesian Probability. It is of utmost importance to understand these concepts if you are getting started with Data Science. What is Frequentist Probability? Probability is used to represent and reason about uncertainty. It was originally developed to analyze the frequency of the events. In other words, the probability was developed as frequentist probability. The probability of occurrence of an event, when calculated as a function of the frequency of the occurrence of the event of that type, is called Frequentist Probability. Frequentist probability is a way of assigning probabilities to events that take into account how often those events actually occur. Frequentist …

## Checklist for Effective Code Review

Are you involved in day-to-day code reviews? Would you like to suggest to your team members a checklist that can be used for code reviews? In this blog post, you will learn about key areas to focus on when doing code reviews. Following is a checklist that one could use while doing code review: Functional Suitability: Understand the requirement/use case/user story and ask whether the code you are reviewing meets the requirement or not. This includes the alternate and exception use case flows to be considered for review. Functional suitability is one aspect of code quality that refers to how well the code meets the needs of the user. In …

## What are Features in Machine Learning?

Machine learning is a field of machine intelligence concerned with the design and development of algorithms and models that allow computers to learn without being explicitly programmed. Machine learning has many applications including those related to regression, classification, clustering, natural language processing, audio and video related, computer vision, etc. Machine learning requires training one or more models using different algorithms. Check out this detailed post in relation to learning machine learning concepts – What is Machine Learning? Concepts & Examples. One of the most important aspects of the machine learning model is identifying the features which will help create a great model, the model that performs well on unseen data. …

## SVM Classifier using Sklearn: Code Examples

In this post, you will learn about how to train an SVM Classifier using Scikit Learn or SKLearn implementation with the help of code examples/samples. An SVM classifier, or support vector machine classifier, is a type of machine learning algorithm that can be used to analyze and classify data. A support vector machine is a supervised machine learning algorithm that can be used for both classification and regression tasks. The Support vector machine classifier works by finding the hyperplane that maximizes the margin between the two classes. The Support vector machine algorithm is also known as a max-margin classifier. Support vector machine is a powerful tool for machine learning and has been widely used …

## NFT Use Cases & Applications Examples

What are NFTs? NFTs (non-fungible tokens) are a relatively new type of cryptocurrency that have a wide range of potential applications. They are different from traditional cryptocurrencies like Bitcoin because each individual NFT is unique and cannot be replaced by another token. This makes them perfect for use in a variety of applications, from digital collectibles to decentralized marketplaces. In this blog post, we will explore some of the most interesting NFT use cases and applications. What are some of the popular use cases for NFTs? The following are some of the most common use cases for NFTs: NFTs can be used to represent ownership of digital assets such as …

## Non-fungible tokens (NFTs) & Real-world examples

You may have heard the term “non-fungible tokens (NFT)” but what do they mean? Basically, they are a type of cryptocurrency that is unique and not interchangeable. Unlike regular Bitcoin or Ethereum, which can be divided and traded like shares, non-fungible tokens are indivisible and have their own value. This makes them perfect for use in specific applications like digital art or collectibles. Here we’ll discuss what are NFTs and what are some real-world examples of where non-fungible tokens are being used today. What are Non-fungible tokens (NFT) and how do they work? Non-fungible tokens are unique digital assets. The word non-fungible means that each token is not interchangeable with …

## First Principles Thinking: Concepts & Examples

Can innovation be taught and learned in a methodical manner? Can there be an innovation playbook using which, given a need to create a thing, product, or solve a complex problem, a set of well-defined steps be followed? How has Elon Musk been super successful time and again to create game-changing innovative products that created tremendous value for end-users and society at large? The answers to these questions can be found with a reasoning technique called first principles thinking. The first principles thinking is often associated with Elon Musk, who uses this approach to come up with his business ideas, create innovative product designs, and build winning products that are …

## ESG Metrics & KPIs and ESG Reporting Concepts

This blog post is geared toward Environmental, Social & Governance (ESG) professionals looking to understand different aspects of ESG and some metrics that can be reported via ESG reports as part of their organization’s ESG reporting in relation to representing the sustainability aspect of their business. An understanding of different aspects of ESG can help you in getting started with ESG initiatives and ESG reporting. ESG initiatives can help companies improve their overall sustainability factor while creating a positive impact on environmental, social, and governance issues. Getting started with ESG-related practices in your organization or department (such as procurement) requires a set of ESG initiatives and related performance measures including …

## Hold-out Method for Training Machine Learning Models

The hold-out method for training the machine learning models is a technique that involves splitting the data into different sets: one set for training, and other sets for validation and testing. The hold-out method is used to check how well a machine learning model will perform on the new data. In this post, you will learn about the hold-out method used during the process of training the machine learning model. Do check out my post on what is machine learning? concepts & examples for a detailed understanding of different aspects related to the basics of machine learning. Also, check out a related post on what is data science? When evaluating …