Even if you are really good at estimating the time a task will take, that doesn’t mean you will be good at estimating th

The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. Let us understand this concept with a simple example.

One day I was doing a task that should have taken 10 minutes and ended up taking 2 hours. I started contemplating the reasons why I thought it would take 10 minutes and the root cause that pumped that number all the way up to 2 hours. My thought process was a bit interesting:

Is there a method to the madness? Is this really the best way we can get things done? Well, I didn’t think so and that’s when I embarked on my journey trying to find a rational mathematical explanation as to why all those smart people are unable to estimate the time it’d take them to do something.

This question is commonly asked by beginners or people inexperienced in the fields of AI and data science. In this article, we aim to overcome this question with ten out of the billion possible applications in the real-world. The aim of the ten use-cases provided in this article is to understand the most commonly used AI and Data Science Technologies in the current generation.

The main methodology behind detecting if the provided email is — spam or not is by detecting patterns of fake emails and words that are usually used while promoting or advertising products for customers with over the top discounts or other similar ways.

Normal distributions are all around us and the human brain is pretty used to them. We are experts at estimating things following a normal distribution by nature; it’s the basis of gaining experience by exposure.

Most developers, especially rather experienced ones, quickly conclude that it’s just pure uncertainty. And it follows that time estimates will always be wrong and that’s just a fact of life and the only thing we can do about it is, well, try to meet client demands and tell developers to “just crunch” when things go wrong. We are all familiar with the stress, the garbage code, and the absolute mayhem that this philosophy causes.

Now you might be thinking that I’m just proving the uncertainty argument with that scenario. Well, yes and no. Let’s zoom out a bit. Sure, uncertainty is the root cause of the delay of this particular task because I would have never guessed that bug existed. But should it be responsible for the delay of the whole project?

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change your perspective both about the future and how you view your environment and yourself in the moment,” Romanoff says. “You likely will be more positive, less critical of yourself and others, and assign less damaging attributions to external events and behaviors of others.”

Assume that we have a dataset of 30,000 emails, out of which some are classified as spam, and some are classified as not spam. The machine learning model will be trained on the dataset. Once the training process is complete, we can test it with a mail that was not included in our training dataset. The machine learning model can make predictions on the following input and classify it correctly if the input email is spam or not.

If you went to the nearest 7–11 almost 20 times this month and every time it took you 5 minutes, except for that time the elevator needed maintenance and you had to wait for 10 minutes and maybe that other time you decided to wait a couple of minutes until it stops raining. What would be your guess for the time it takes you to go there right now? 5 minutes?

In fact, Artificial Intelligence is the fastest-growing field in the present-day. According to fortune, the statistics say that the hirings for AI specialists have grown by 74% over the last four years. Artificial Intelligence is often regarded as the “Hottest” job of the present generation.

The demand for skilled AI specialists is growing faster, like never before. Requirements and open positions for experts in the sub-fields of AI like machine learning, deep learning, computer vision, statistics, and natural language processing are surging each day.

I mean, it doesn’t make sense to say 15 because it’s a rare incident or 7 unless it’s raining outside. And you’d be right, most likely. If 18 out of 20 times took 5 minutes then certainly there’s a big chance that it would just take 5 minutes (the median) this time, roughly 90% chance (without getting into more complex algebra, of course).

The various machine learning algorithms like Naive Bayes, support vector machines, K-nearest neighbors, and random forests, among many other algorithms, can be used for filtering spam messages and classifying if the received email is a “spam message” or not. Advanced spam detection can be performed using techniques like neural networks or optical character recognition (OCR), which is also used by companies like Gmail for spam filtering.