Tag Archives: ai

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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What is Explainable AI? Concepts & Examples

Explainable AI - SHAP Explainability

Explainable AI is an important part of artificial intelligence. It provides humans with the ability to explain how decisions are made by machines. This helps people trust and understand what’s happening, instead of feeling like their information is being taken advantage of or used without their permission. In this blog post, we’ll explain explainable AI and explain it with examples so you know how it works! What is Explainable AI and how does it work? Explainable AI is defined as AI systems that explain their reasoning behind the prediction. Explainable AI is part of the larger umbrella term for artificial intelligence known as “interpretability.” Explainable AI is a type of …

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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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What is Machine Learning? Concepts & Examples

what is machine learning

Machine learning is a machine’s ability to learn from data. It has been around for decades, but machine learning is now being applied in nearly every industry and job function. In this blog post, we’ll cover what machine learning entails, how it differs from traditional programming. What is machine learning? Simply speaking, machine learning is a technology where in machine learns to perform a prediction/estimation task based on past experience represented by historical data set.  There are three key aspects of machine learning which are following: Task: Task can be related to prediction problems  Experience: Experience represents historical dataset Performance: The goal is to perform better in the prediction task …

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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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Hypothesis Testing Explained with Real-life Examples

Hypothesis Testing Workflow

Hypothesis testing is a statistical technique that helps researchers test the validity of their theories. It’s often used in statistics and data science to analyze whether an event has occurred, or if it will occur based on past events.  This blog post will cover some of the key statistical concepts along with examples in relation to how to formulate a hypothesis for hypothesis testing. The knowledge of hypothesis formulation and hypothesis testing would prove key to building various different machine learning models. In later articles, hypothesis formulation for machine learning algorithms such as linear regression, logistic regression models, etc., will be explained. What is a Hypothesis? Simply speaking, hypothesis testing …

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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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Clinical Trials & Predictive Analytics Use Cases

clinical trials predictive analytics machine learning use cases

Analytics plays a big role in modeling clinical trials and predictive analytics is one such technique that has been embraced by clinical researchers. Machine learning algorithms can be applied at various stages in the drug discovery process – from early compound selection to clinical trial simulation. Data scientists have been applying machine learning algorithms to clinical trial data in order to identify predictive patterns and correlations between clinical outcomes, patient demographics, drug response phenotypes, medical history, and genetic information. Predictive analytics has the potential to enhance clinical research by helping accelerate clinical trials through predictive modeling of clinical outcome probability for better treatment decisions with reduced clinical trial costs. In …

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

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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Graph Neural Networks Explained with Examples

Training a graph neural network model

Graph neural networks (GNNs) are a relatively new area in the field of deep learning. They arose from graph theory and machine learning, where the graph is a mathematical structure that models pairwise relations between objects. Graph Neural Networks are able to learn graph structures for different data sets, which means they can generalize well to new datasets – this makes them an ideal choice for many real-world problems like social network analysis or financial risk prediction. This post will cover some of the key concepts behind graph neural networks with the help of multiple examples. What are graph neural networks (GNNs)? Graphs are data structures which are used to …

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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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Supplier risk management & machine learning techniques

supplier risk management machine learning

Supplier risk management (SRM) is a serious issue for procurement professionals. Suppliers can be unreliable, have poor quality products, or fail to meet specifications. In this blog post we will discuss AI / machine learning algorithms / techniques that you can use to manage supplier risk and make your procurement process more efficient. What is supplier risk management? Supplier Risk Management (SRM) also known as Supplier Risk Optimization (SRO), refers to policies and technology that enables organizations to manage risks related with suppliers. This can be done by analyzing data about past purchases from the supplier, predicting future risks related with purchases from this particular company. It’s crucial for procurement …

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Online AI News from Top Global Universities – List

US universities ai news and events

In this post, you will get an access to a list of web pages representing latest news related to artificial intelligence from top universities across the globe. This page will be updated from time-to-time for including new pages from different universities across the globe. These URLs will be very useful for those machine learning / data science enthusiasts who want to keep tab on current news and events in the field of artificial intelligence. MIT Stanford Stanford university – Human-centered AI (HAI) Stanford university – Center for AI in medicine and imaging Stanford AI research and ideas Harvard university JHU Malone center for Engg. in healthcare Yale university Princeton university …

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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 which can be used for measuring the success of AI / ML (machine learning) / DS (data science) initiatives / products. If you are one of the AI / ML stakeholders, 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 value metrics such as operating …

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Predictive vs Prescriptive Analytics Difference

In this post, you will quickly learn about the difference  between  predictive analytics and prescriptive analytics. As data analytics stakeholders, one must get a good understanding of these concepts in order to decide when to apply predictive and when to make use of prescriptive analytics in analytics solutions / applications. Without further ado, let’s get straight to the diagram.  In the above diagram, you could observe / learn the following: Predictive analytics: In predictive analytics, the model is trained using historical / past data based on supervised, unsupervised, reinforcement learning algorithms. Once trained, the new data / observation is input to the trained model. The output of the model is prediction in form …

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