Tag Archives: machine learning

Hate Speech Detection Using Machine Learning

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 …

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Posted in Deep Learning, Machine Learning. Tagged with , .

Machine Learning with Graphs: Free online course(Stanford)

what is machine learning

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 …

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Business Analytics Team Structure: Roles/ Responsibilities

business analytics value chain

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 …

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Posted in Data analytics, Data Science, Machine Learning, Product Management. Tagged with , , .

Linear Regression Interview Questions for Data Scientists

linear regression questions

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 …

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Ordinary Least Squares Method: Concepts & Examples

ordinary least squares method

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 …

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Posted in Data Science, Machine Learning. Tagged with , .

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

r-squared vs adjusted r-squared

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 …

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

R-squared formula linear regression model

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 …

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Posted in AI, Data Science, Machine Learning. Tagged with , , .

Interpreting f-statistics in linear regression: Formula, Examples

linear regression R-squared concepts

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 …

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Posted in Data Science, Machine Learning, statistics. Tagged with , , .

Reinforcement Learning Real-world examples

Reinforcement-learning-real-world-example

 In this blog post, we’ll learn about some real-world / real-life examples of Reinforcement learning, one of the different approaches to machine learning where other approaches are supervised and unsupervised learning. Reinforcement learning is a type of machine learning that enables a computer system to learn how to make choices by being rewarded for its successes. This can be an extremely powerful tool for optimization and decision-making. It’s one of the most popular machine learning methods used today. Before looking into the real-world examples of Reinforcement learning, let’s quickly understand what is reinforcement learning. Introduction to Reinforcement Learning (RL) Reinforcement learning is an approach to machine learning in which the agents …

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Different Success / Evaluation Metrics for AI / ML Products

Success metrics for AI and 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 …

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Posted in AI, Data Science, Machine Learning. Tagged with , , .

Warehouse Management & Machine Learning Use Cases

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 …

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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 …

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Posted in Data Science, statistics. Tagged with , , .

Cybersecurity Machine Learning Use Cases: Examples

cybersecurity machine learning use cases

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 …

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Posted in AI, Deep Learning, Machine Learning. Tagged with , .

Procurement: Key Advanced Analytics Use Cases

procurement analytics use cases

The procurement analytics applications are poised to grow exponentially in the next few years. With so much data available and the need for digital transformation across procurement organization, it’s important to know how procurement analytics can help you make better business decisions. This blog will cover procurement analytics and key use cases of advanced analytics that will be useful for business stakeholders such as category managers, sourcing managers, supplier relationship managers, business analysts / product managers, and data scientists implement different use cases using machine learning. Procurement analytics will allow you to use data very effectively in achieving data-driven decision making.  One can get started with procurement analytics with focus …

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Posted in Data Science, Machine Learning, Procurement. Tagged with , , .

What are Sequence Models: Types & Examples

sequence-to-sequence model

Sequence models are a very common sequence modeling technique in machine learning that is used for analyzing sequence data. This blog post will discuss types of sequence models, their examples, and how they can be used to help with the understanding and analysis of sequences. What is sequence data? Sequence data are the data points which are ordered in the meaningful manner such that earlier data points or observations provide the information about later data points or observations. The time series data is an example of sequence data which can be defined as a sequence of observations where each observation is dependent on the previous one. Sequence data can be …

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Elbow Method vs Silhouette Score – Which is Better?

In K-means clustering, elbow method and silhouette analysis or score techniques are used to find the number of clusters in a dataset. The elbow method is used to find the “elbow” point, where adding additional data samples does not change cluster membership much. Silhouette score determines whether there are large gaps between each sample and all other samples within the same cluster or across different clusters. In this post, you will learn about these two different methods to use for finding optimal number of clusters in K-means clustering. Selecting optimal number of clusters is key to applying clustering algorithm to the dataset. As a data scientist, knowing these two techniques to find …

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Posted in Data Science, Machine Learning, Python. Tagged with , , , .