Month: October 2021

Forest, bush, or vegetarian fire, can be described as any uncontrolled and non-prescribed combustion or burning of plants in a natural setting such as a forest, grassland, etc. In this article we are not determining if a forest fire will take place or not, we are predicting the confidence of the forest fire based on some attributes.
Well, the first question arises as that why we even need Machine learning to predict forest fire in that particular area? So, yes the question is valid that despite having the experienced forest department who have been dealing with these issues for a long time why is there a need for ML, having said that answer is quite simple that the experienced forest department can check on 3-4 parameters from their human mind but ML on other hand can handle the numerous parameters whether it can be latitude, longitude, satellite, version, and whatnot, so dealing with this multi-relationship of a parameter that is responsible for the fire in the forest we do need ML for sure!

What do you think when someone asks you about empathy? Do you struggle to find its meaning or does it come to you naturally? In the age of artificial intelligence, do our AI systems need empathy? If so, what are some use cases where empathy can be most helpful in AI Systems?

Humans are social creatures. We thrive on empathy. When we read a book out loud to our children, they can hear the emotions we imbue into the passages. With each tone and inflection, our children will remember the love we inject into their favorite books. They will remember their bedtime reading as a special moment. They will have fond memories of the books that we read to them.

In human discourse, people often emphasize the ‘context’ or ‘contextual explanation’ to develop a better understanding over the specific conversation. In many cases, context is vital in decision-making or reacting to the actual situation. Nowadays, data is like a goldmine for researchers, service providers and technology vendors. At this juncture, data-driven models are extensively used in intelligent buildings to monitor the whole building performance along with forecasting services.

Random forests are a supervised Machine learning algorithm that is widely used in regression and classification problems and produces, even without hyperparameter tuning a great result most of the time. It is perhaps the most used algorithm because of its simplicity. It builds a number of decision trees on different samples and then takes the majority vote if it’s a classification problem.

I am assuming you have already read about Decision Trees, if not then no need to worry we’ll read everything from start. In this article, we’ll figure out how the Random Forest algorithm works, how to use it, and the math intuition behind this simple algorithm.

Before learning this algorithm let’s first see what are Ensemble techniques.

As investment in the development of practical quantum computers and with key breakthroughs in this technology, are we poised to enter the age of working quantum computers? What could this mean for businesses? Do you need to be creating a quantum computing strategy for your enterprise?

Often seen as a technology that will become a practical tool for businesses in the distant future, with several recent developments and continued investment into the technology, your business could be using quantum computers sooner than you think.

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