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 …
Two sample Z-test for Proportions: Formula & Examples
In statistics, a two-sample z-test for proportions is a method used to determine whether two samples are drawn from the same population. This test is used when the population proportion is unknown and there is not enough information to use the chi-squared distribution. The test uses the standard normal distribution to calculate the test statistic. As data scientists, it is important to know how to conduct this test in order to determine whether two proportions are equal. In this blog post, we will discuss the formula and examples of the two-proportion Z-test. What is two proportion Z-test? A two-proportion Z-test is a statistical hypothesis test used to determine whether two …
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 …
Different types of Time-series Forecasting Models
Forecasting is the process of predicting future events based on past and present data. Time-series forecasting is a type of forecasting that predicts future events based on time-stamped data points. There are many different types of time-series forecasting models, each with its own strengths and weaknesses. In this blog post, we will discuss the most common time-series forecasting machine learning models such as the following, and provide examples of how they can be used to predict future events. Autoregressive (AR) model Moving average (MA) model Autoregressive moving average (ARMA) model Autoregressive integrated moving average (ARIMA) model Seasonal autoregressive integrated moving average (SARIMA) model Vector autoregressive (VAR) model Vector error correction …
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 …
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 …
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 …
Difference between Data Science & Decision Science
Data science and decision science are two data-driven fields that have grown in prominence over the past few years. Data scientists use data to come up with conclusions or predictions about things like customer behavior, while decision scientists combine data with other information sources to make decisions. The difference between data science and decision science is important for business owners who want to make informed decisions. In this post, you will learn about the difference between data science and decision science. Those venturing out to learn data science must understand whether they want to learn data science or decision science or both. The following are some of the key questions …
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 …
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 …
