Why It is important to identify outliers? Often outliers are discarded because of their effect on the total distribution and statistical analysis of the dataset. This is certainly a good approach if the outliers are due to an error of some kind (measurement error, data corruption, etc.), however often the source of the outliers is unclear. There are many situations where occasional'extreme' events cause an outlier that is outside the usual distribution of the dataset but is a valid measurement and not due to an error. In these situations, the choice of how to deal with the outliers is not necessarily clear and the choice has a significant impact on the results of any statistical analysis done on the dataset.

Some carcinomas show that one or more metastatic sites appear with unknown origins. The identification of primary or metastatic tumor tissues is crucial for physicians to develop precise treatment plans for patients. With unknown primary origin sites, it is challenging to design specific plans for patients. Usually, those patients receive broad-spectrum chemotherapy, while still having poor prognosis though. Machine learning has been widely used and already achieved significant advantages in clinical practices.

Intelligence, in simpler words, can be explained as the mental ability of reasoning, problem-solving, and learning. Intelligence comes with perception, attention, and planning. Humans are the only resource of intelligence on this planet and this is what makes us stand out from all the natural god-gifted resources on this planet. The human brain has the capability of making decisions, remembering things of the past, and calculating for the future. Artificial intelligence as the name itself suggests it is a man-made intelligent machine.

Every possible organization that one can think of relies on data to achieve the set objectives. On that note, having access to data that isn't smart enough to get goals accomplished poses a hurdle. It is thus important to have data that is transformed in a manner that can cater to the needs and objectives of the organization. With most organizations relying on Artificial Intelligence (AI) and machine learning, the necessity of dealing with the right data is all the more important for the sole reason that the models employed aim at obtaining meaningful insights. No wonder data is vast and one shouldn't ideally fall short of it while aiming at the objectives.

Restaurants are an essential part of a country's economy and society. Whether it may be for social gatherings or a quick bite, most of us have experienced at least one visit. With the recent rise in pop up restaurants and food trucks, it's imperative for the business owner to figure out when and where to open new restaurants since it takes up a lot of time, effort, and capital to do so. This brings up the problem of finding the best optimal time and place to open a new restaurant. TFI which owns many giant restaurant chains has provided demographic, real estate, and commercial data in their restaurant revenue prediction on Kaggle.

Are you planning to start a career in Artificial Intelligence and Machine learning? Well, then this article is for you. Building a career in AI and ML is not easy nor hard either. But it requires a dedicated approach. Sometimes when you're from an IT background, you may feel like swapping the career options too, because of the diverse opportunities.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Why #MLOps is the key for productionized ML system? ML model code is only a small part ( 5–10%) of a successful ML system, and the objective should be to create value by placing ML models into production. F1 score) while stakeholders focus on business metrics (e.g. Improving labelling consistency is an iterative process, so consider repeating the process until disagreements are resolved as far as possible. For instance, partial automation with a human in the loop can be an ideal design for AI-based interpretation of medical scans, with human judgement coming in for cases where prediction confidence is low.

Consider you have a prediction system h1 (example a photo tagger) whose output is consumed in real world (example tagging your photos on phone). Now, you train a system h2 whose aggregate metrics suggest that it is better than h1. Let's consider an unlabeled dataset D of examples (a pool of all user photos). Prediction update refers to the process where h2 is used to score examples in dataset D and update the predictions provided by h1. The problem here is that even though h2 is better than h1 globally, we haven't determined if h2 is significantly worse for some users or some specific pattern of examples.

A patent from Apple suggests the company is considering how machine learning can make augmented reality (AR) more useful. Most current AR applications are somewhat gimmicky, with barely a handful that have achieved any form of mass adoption. Apple's decision to introduce LiDAR in its recent devices has given AR a boost but it's clear that more needs to be done to make applications more useful. A newly filed patent suggests that Apple is exploring how machine learning can be used to automatically (or "automagically," the company would probably say) detect objects in AR. The first proposed use of the technology would be for Apple's own Measure app. Measure's previously dubious accuracy improved greatly after Apple introduced LiDAR but most people probably just grabbed an actual tape measure unless they were truly stuck without one available.

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to "learn" intrinsic patterns in a complex plane of data is the strength of the approach.