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 …

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Most Common Machine Learning Tasks

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 …

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Statistics – Random Variables, Types & Python Examples

probability-distribution-plot-of-discrete-random-variable

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 …

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Frequentist vs Bayesian Probability: Difference, Examples

difference between bayesian and frequentist probability

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 …

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Checklist for Effective Code Review

code review checklist

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 …

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What are Features in Machine Learning?

Features - Key to 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. …

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SVM Classifier using Sklearn: Code Examples

support vector machine classifier

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 …

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NFT Use Cases & Applications Examples

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 …

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

Non-fungible token or NFT

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 …

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First Principles Thinking: Concepts & Examples

car models first principles thinking 2

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 …

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ESG Metrics & KPIs and ESG Reporting Concepts

ESG KPIs and metrics

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 …

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Hold-out Method for Training Machine Learning Models

Hold-out-method-Training-Validation-Test-Dataset

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 …

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Autoregressive (AR) models with Python examples

Autoregressive (AR) models are a subset of time series models, which can be used to predict future values based on previous observations. AR models use regression techniques and rely on autocorrelation in order to make accurate predictions. This blog post will provide Python code examples that demonstrate how you can implement an AR model for your own predictive analytics project. You will learn about the concepts of autoregressive (AR) models with the help of Python code examples. If you are starting on time-series forecasting, this would be a useful read. Note that time-series forecasting is one of the important areas of data science/machine learning.  For beginners, time-series forecasting is the process of using a model …

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Ridge Regression Concepts & Python example

Ridge regression is a type of linear regression that penalizes ridge coefficients. This technique can be used to reduce the effects of multicollinearity in ridge regression, which may result from high correlations among predictors or between predictors and independent variables. In this tutorial, we will explain ridge regression with a Python example. What is Ridge Regression? Ridge regression is a type of linear regression technique that is used in machine learning to reduce the overfitting of linear models. Recall that Linear regression is a method of modeling data that represents relationships between a response variable and one or more predictor variables. Ridge regression is used when there are multiple variables that …

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Lasso Regression Explained with Python Example

In this post, you will learn concepts of Lasso regression along with Python Sklearn examples. Lasso regression algorithm introduces penalty against model complexity (a large number of parameters) using regularization parameter. The other two similar forms of regularized linear regression are Ridge regression and Elasticnet regression which will be discussed in future posts. In this post, the following topics are discussed: What’s Lasso Regression? Lasso regression is a machine learning algorithm that can be used to perform linear regression while also reducing the number of features used in the model. Lasso stands for least absolute shrinkage and selection operator. Pay attention to the words, “least absolute shrinkage” and “selection”. We will …

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Bias-Variance Trade-off Concepts & Interview Questions

Bias variance concepts and 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 …

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