Now you might be thinking that I’m just proving the uncertainty argument with that scenario. Well, yes and no. Let’s zoo

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.,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).,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.,Even if you are really good at estimating the time a task will take, that doesn’t mean you will be good at estimating the time the project will take! Counter intuitively, you will be more wrong.,Autocomplete, or word completion, is a feature in which an application predicts the rest of a word a user is typing. In Android smartphones, this is called predictive text. In graphical user interfaces, users can typically press the tab key to accept a suggestion or the down arrow key to accept one of several.,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.,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?,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.,As we type in “what is the wea..” we already receive some predictions. These predictive searches also work on AI. These usually work on concepts such as natural language processing, machine learning, and deep learning. A sequence to sequence mechanism with attention can be used to achieve higher accuracy and lower losses for these predictions.,Zero-shot and one-shot learning methods even exist for natural language processing. The same methods can be used for better training of the model to improve the overall performance and avoid repeated training procedures which can be a really big hindrance in some real-life applications and scenarios. Hence, one-shot learning is a great alternative for deployment and working in other embedded systems with lower training capacities.,Now all the math nerds (or data scientists/statisticians) reading right now must have already recognized that tiny graph in the previous meme as a right-skewed normal distribution. Let me enlarge, and clarify:,The structure of our company always helped transparency. We remained a small company until our recent acquisition (30–50 employees). It is no secret that the more people get involved, the harder it is to keep everyone in the know, and to make sure nothing leaks.,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.,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.