# Tag Archives: machine learning

## Steps for Evaluating & Validating Time-Series Models

Time-series machine learning models are becoming increasingly popular due to the large volume of data that is now available. These models can be used to make predictions about future events, and they are often more accurate than traditional methods. However, it is important to properly evaluate (check accuracy by performing error analysis) and validate these models before you put them into production. In this blog post, we will discuss the different ways that you can evaluate and validate time series machine learning models. We will also provide some tips on how to improve your results. As data scientists, it is important to learn the techniques related to evaluating time-series models. …

## Linear vs Logistic Regression: Differences, Examples

Linear regression and logistic regression are both methods for modeling relationships between variables. They are both used to build statistical models but perform different tasks. Linear regression is used to model linear relationships, while logistic regression is used to model binary outcomes (i.e. whether or not an event happened). In this blog post, we will discuss the differences between linear and logistic regression, as well as when to use each one. We will also provide examples so that you can understand how they work. What is linear regression? Linear regression is used to solve the regression problems. It is used to model linear relationships. This means that it can be …

## Machine Learning in Finance: Concepts & Examples

Machine learning has found its way into finance and is being used in various ways to improve the industry. Finance has always been a data-driven industry, and in recent years, machine learning has become an increasingly important tool for making sense of that data. In this blog post, we will explore some of these use cases and explain how machine learning is helping to make finance more efficient. We will also provide examples to help illustrate how machine learning is being used in finance. By the end of this post, you will have a better understanding of the many ways machine learning is benefiting finance and why it is such …

## Machine Learning with Alteryx: Examples

Alteryx is a self-service data analytics software platform that enables users to easily prep, blend, and analyze data all in one place. It is a powerful tool that can be used in a variety of machine learning scenarios. It can be used to clean and prepare data, and develop, evaluate and deploy machine learning (ML) models. It offers a variety of features and tools that can be used to preprocess data, choose algorithms, train models, and evaluate results. In this blog post, we will discuss some of the ways that Alteryx can be used in machine learning. We will also provide examples of how to use Alteryx in machine learning scenarios. …

## Hate Speech Detection Using Machine Learning

Hate speech is a big problem on the internet. It can be found on social media, in comment sections, and even in online forums. Detecting hate speech is important because it can have harmful effects on society. In this blog post, we will discuss the latest techniques for detecting hate speech using machine learning algorithms. We will also provide examples of how these algorithms work. What is hate speech? Hate speech can be defined as any speech that targets a group of people based on their race, religion, ethnicity, national origin, sexual orientation, or gender identity. Hate speech is often used to spread hate and bigotry. It can also be …

## Machine Learning with Graphs: Free online course(Stanford)

Are you interested in learning the concepts of machine learning with Graphs? Stanford University is offering a free online course in machine learning titled Machine Learning with Graphs (CS224W). The lecture videos will be available on Canvas for all the enrolled Stanford students. The lecture slides and assignments will be posted online as the course progresses. This class will be offered next in Fall 2022. After completing this course, you will be able to apply machine learning methods to a variety of real-world problems. The course titled Machine learning with Graphs, will teach you how to apply machine learning methods to graphs and networks. Complex data can be represented as …

## Business Analytics Team Structure: Roles/ Responsibilities

Business analytics is a business function that has been around for years, but it’s only recently gained traction as one of the most important business functions. Organizations are now realizing how business analytics can help them increase revenue and improve business operations. But before you bring on a business analytics team, you need to determine if your company needs full-time or part-time team members or both. It might seem logical to hire full-time staff members just because they’re in demand, but this isn’t always necessary. If your business operates without any external data sets and doesn’t have complex reporting and advanced analytics needs then it may be more cost-effective to …

## Linear Regression Interview Questions for Data Scientists

This page lists down 40 regression (linear/univariate, multiple/multilinear/multivariate) interview questions (in form of objective questions) which may prove to be helpful for Data Scientists / Machine Learning enthusiasts. Those appearing for interviews for machine learning/data scientist freshers/intern/beginners positions would also find these questions very helpful and handy enough to quickly brush up / check your knowledge and prepare accordingly. Practice Tests on Regression Analysis These interview questions are split into four different practice tests with questions and answers which can be found on following page: Linear, Multiple regression interview questions and answers – Set 1 Linear, Multiple regression interview questions and answers – Set 2 Linear, Multiple regression interview questions …

## Ordinary Least Squares Method: Concepts & Examples

Ordinary least squares (OLS) is a linear regression technique used to find the best-fitting line for a set of data points. It is a popular method because it is easy to use and produces decent results. In this blog post, we will discuss the basics of OLS and provide some examples to help you understand how it works. As data scientists, it is very important to learn the concepts of OLS before using it in the regression model. What is the ordinary least squares (OLS) method? The ordinary least squares (OLS) method can be defined as a linear regression technique that is used to estimate the unknown parameters in a …

## R-squared & Adjusted R-squared: Differences, Examples

There are two measures of the strength of linear regression models: adjusted r-squared and r-squared. While they are both important, they measure different aspects of model fit. In this blog post, we will discuss the differences between adjusted r-squared and r-squared, as well as provide some examples to help illustrate their meanings. As a data scientist, it is of utmost importance to understand the differences between adjusted r-squared and r-squared in order to select the most appropriate linear regression model out of different regression models. What is R-squared? R-squared is a measure of what proportion of the variance in the value of the dependent or response variable is explained by …

## R-squared, R2 in Linear Regression: Concepts, Examples

In linear regression, R-squared (R2) is a measure of how close the data points are to the fitted line. It is also known as the coefficient of determination. In this post, you will learn about the concept of R-Squared in relation to assessing the performance of multilinear regression machine learning model with the help of some real-world examples explained in a simple manner. Before doing a deep dive, you may want to access some of the following blog posts in relation to concepts of linear regression: Linear regression explained with real-world examples Linear regression hypothesis testing: concepts, examples Linear regression t-test: formula, examples Interpreting f-statistics in linear regression: formula, examples What …

## Interpreting f-statistics in linear regression: Formula, Examples

In this blog post, we will take a look at the concepts and formula of f-statistics in linear regression models and understand with the help of examples. F-test and F-statistics are very important concepts to understand if you want to be able to properly interpret the summary results of training linear regression machine learning models. We will start by discussing the importance of f-statistics in building linear regression models and understand how they are calculated based on the formula of f-statistics. We will, then, understand the concept with some real-world examples. As data scientists, it is very important to understand both the f-statistics and t-statistics and how they help in …

## Different Success / Evaluation Metrics for AI / ML Products

In this post, you will learn about some of the common success metrics that can be used for measuring the success of AI / ML (machine learning) / DS (data science) initiatives / projects / products. If you are one of the AI / ML stakeholders including product managers, you would want to get hold of these metrics in order to apply right metrics in right business use cases. Business leaders do want to know and maximise the return on investments (ROI) from AI / ML investments. Here is the list of success metrics for AI / DS / ML initiatives: Business value metrics / key performance indicators (KPIs): Business …

## Warehouse Management & Machine Learning Use Cases

Warehouses are a vital part of the supply chain. Not only do they store products, but warehouses also play a role in shipping and receiving goods. As warehouse operations become more complex, it’s important to use technology to help manage them. Warehouses need to be able to efficiently manage the flow of goods in and out while still making room for new deliveries. Increasingly warehouses are turning to machine learning algorithms as a way to improve warehouse efficiency, reduce costs, and increase warehouse productivity. In this blog post, we will explore different machine learning use cases which can be deployed by warehouse managers to create a positive business impact. Machine …

## Type I & Type II Errors in Hypothesis Testing: Examples

This article describes Type I and Type II errors made due to incorrect evaluation of the outcome of hypothesis testing, based on a couple of examples such as the person comitting a crime, the house on fire, and Covid-19. You may want to note that it is key to understand type I and type II errors as these concepts will show up when we are evaluating a hypothesis such as those related to machine learning algorithms (linear regression, logistic regression, etc). For example, in the case of linear regression models, the significance value is compared with the p-value and, the null hypothesis that the parameter/coefficient is equal to zero is …

## Cybersecurity Machine Learning Use Cases: Examples

Cybersecurity professionals are increasingly finding cybersecurity machine learning use cases in their work. The reason for this is that cybersecurity has become more complicated and the scale of cybersecurity threats is growing exponentially. Machine learning can help to combat these cybersecurity threats by providing security teams with real-time alerts, but there are many cybersecurity machine learning use cases beyond just cybersecurity. Artificial intelligence (AI) technologies, in particular, machine learning models such as logistic regression, SVM and random forest, etc., and deep neural networks models such as CNN, LSTM, etc., have been widely used to fight against cyberattacks. In this blog post, we will look into how machine learning is being …