Category Archives: AI

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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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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E-commerce Machine Learning Use Cases: Examples

ecommerce machine learning use cases

In e-commerce, machine learning can be used to improve a number of decisions thereby resulting in creating a positive business impact. Not only does it help e-commerce organizations increase conversion rates and find the best deals for their customers, but it also helps them understand the customer better. This blog post will look at various different use cases where AI/machine learning and deep learning have been used in eCommerce. What are some key machine learning use cases in eCommerce? Here are some key areas in eCommerce where AI/machine learning can be leveraged: Product recommendation: One of the key use cases where machine learning has been used is to provide product …

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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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Free AI / Machine Learning Courses at

free machine learning courses at alison

Are you interested in learning about AI / machine learning / data sicence and looking for free online courses? As per MANUELA M. VELOSO, Herbert A. Simon University Professor at CMU,Machine Learning (ML) is a fascinating field of Artificial Intelligence (AI) research and practice where  we investigate how  computer agents can improve their perception, cognition, and action  with experience. Machine Learning is about machines improving from  data, knowledge, experience, and interaction. Machine Learning  utilizes a variety of techniques to intelligently handle large and complex amounts of  information build upon foundations  in many disciplines, including  statistics, knowledge representation, planning and control, databases, causal inference, computer systems, machine vision, and natural  language …

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Difference between Supervised & Unsupervised Learning

Supervised vs Unsupervised Machine Learning Problems

Supervised and unsupervised learning are two different common types of machine learning tasks that are used to solve many different types of business problems. Supervised learning uses training data with labels to create supervised models, which can be used to predict outcomes for future datasets. Unsupervised learning is a type of machine learning task where the training data is not labeled or categorized in any way. For beginner data scientists, it is very important to get a good understanding of the difference between supervised and unsupervised learning. In this post, we will discuss how supervised and unsupervised algorithms work and what is difference between them. You may want to check …

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12 Weeks Free course on AI: Knowledge Representation & Reasoning (IIT Madras)

IIT madras free course ai knowledge representation

Are you interested in learning about exploring a variety of representation formalisms and the associated algorithms for reasoning in Artificial intelligence? IIT Madras is going to offer a free online course on AI: knowledge representation and reasoning. This course will help you understand the basics of knowledge representation and reasoning. You’ll learn how to solve problems using logic, how to build intelligent systems that can interpret natural language, reason using formal methods and more. The course is taught by Professor Deepak Khemani, who has over 20 years of experience teaching at IIT Madras. Prof. Khemani is a Professor at Department of Computer Science and Engineering. He’s also written several books …

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NIT Warangal offers one-week online training on AI, Machine Learning

NIT Warangal one week course on AI and machine learning

Are you interested in learning about AI and Machine Learning, or refresing your concepts? NIT Warangal offers one-week online paid training (minimal fees) on AI, Machine Learning. This program is a great opportunity for students to learn about AI & machine learning basics and advanced concepts. It is organized by the Department of Electronics and Communication Engineering & Department of R&D in association with Center of Continuing Education. It will be taught by experience professors who have years of experience in their respective fields. The course will take place between 30th November to 4th December 2021, and it is open to all Faculty/ Research Scholars/Industry professionals/ and other eligible students …

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AI Product Manager Interview Questions

interview questions for machine learning

AI has become such an integral part of our lives that it is important to hire professionals who can help create AI / machine learning products that will be used by many people. These AI product manager interview questions will give you insight into your candidate’s experience, skills, and industry knowledge so that you can get prepared in a betterinterma manner before appearing for your next interview as an AI product manager. Check out a detailed interview questions and answers with greater focus on machine learning topics. Here are some interview questions that you can get when you are appearing for AI / Machine learning (ML) product manager (PM) job: …

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Machine Learning Examples from Daily Life

machine learning models development and deployment challenges

Machine learning is a powerful machine intelligence technique that can be used in a variety of settings to generate data insights. In this blog post, we will explore real-world or real-life machine learning / deep learning / AI examples from daily life. We’ll see how machine-learning techniques have been successfully applied to solve real-life problems. The idea is to make you aware of how machine learning and data science applications are everywhere. What are some real-world examples of machine learning from daily life? Here are some real-world examples of machine learning that we use in our daily life: Best driving directions (Google Maps): A bunch of machine learning / deep …

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Data Science / AI Team Structure – Roles & Responsibilities

Data Science Team Roles & Responsibilities

Setting up a successful artificial intelligence (AI) / data science or advanced analytics practice or center of excellence (CoE) is key to success of AI in your organization. In order to setup a successful data science COE, setting up a well-organized data science team with clearly defined roles & responsibilities is the key. Are you planning to set up the AI or data science team in your organization, and hence, looking for some ideas around data science team structure and related roles and responsibilities? In this post, you will learn about some of the following aspects related to the building data science/machine learning team. Focus areas Roles & responsibilities Data Science Team – Focus …

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Sentiment Analysis & Machine Learning Techniques

sentiment analysis machine learning

Artificial intelligence (AI) / Machine learning (ML) techniques are getting more and more popular. Many people use machine learning to analyze the sentiment of tweets, for example, to make predictions related to different business areas. In this blog post, you will learn about different machine learning / deep learning and NLP techniques which can be used for sentiment analysis. What is sentiment analysis? Sentiment analysis is about predicting the sentiment of a piece of text and then using this information to understand users’ (such as customers) opinions. . The principal objective of sentiment analysis is to classify the polarity of textual data, whether it is positive, negative, or neutral. Whether …

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Binomial Distribution Explained with Examples

binomial experiment coin tossing 100 experiments 50 trials

The binomial distribution is a probability distribution that applies to binomial experiments. It’s the number of successes in a specific number of tries. The binomial distribution may be imagined as the probability distribution of a number of heads that appear on a coin flip in a specific experiment comprising of a fixed number of coin flips. In this blog post, we will learn binomial distribution with the help of examples. If you are an aspiring data scientist looking forward to learning/understand the binomial distribution in a better manner, this post might be very helpful. What is a Binomial Distribution? The binomial distribution is a discrete probability distribution that represents the probabilities of binomial random …

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Procure-to-pay Processes & Machine Learning

procure to pay machine learning use cases

The procure-to-pay (P2P) cycle or process consists of a set of steps that must be taken in order for an organization to procure and pay for goods and services. Procurement is the process by which organizations purchase goods, supplies, equipment, or services from outside sources. The procurement function may also serve as an intermediary between two internal departments or divisions that have overlapping needs. In this blog post, we will discuss how AI / machine learning can be leveraged to automate certain procure-to-pay processes such that procure-to-pay teams can focus on core business goals. What is the procure-to-pay cycle or process? The procure-to-pay (P2P) cycle or process is defined as …

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Demand Forecasting & Machine Learning Techniques

demand forecasting machine learning use cases

Machine learning is a technology that can be used for demand forecasting in order to make demand forecasts more accurate and reliable. In demand forecasting, machine learning techniques are used to forecast demand for a product or service. There are different types of machine learning/deep learning techniques used in demand forecastings such as neural networks, support vector machines, time series forecasting, and regression analysis. This blog post will introduce different machine learning & deep learning techniques for demand forecasting and give an overview of how they work. What is the demand forecasting process? The demand forecasting process is defined as the creation of demand forecasts, demand planning, and demand decision …

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Drug Discovery & Deep Learning: A Starter Guide

generative chemistry with variational autoencoder VAE

The drug discovery process is tedious, time-consuming, and expensive. A drug company has to identify the compounds that are most likely to be successful in drug development. The drug discovery process can take up to 15 years with an average cost of $1 billion for each drug candidate that passes clinical trials. With AI and deep learning models becoming more popular in recent years, scientists have been looking at ways to use these tools in the drug discovery process. This article will explore how deep learning generative models (GANs) could be used as a starting point for data scientists to get started drug discovery AI projects! What is the drug …

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