How AI Works: PRO-TECH Behind Search Engine Machine Learning 2023

How AI Works: PRO-TECH Behind Search Engine Machine Learning 2023 AI Expert explains the New Machines learning models that make up your search bar. It includes a catchy and Official music rap video in HD featuring BeatzbyBotlord and lil rap trap lyrics with Dirty Data Mining Mike. New technology review with creative story telling giving a classic account behind the mythological search bar. Its a creative unboxing revealing a funny house vlog on technology pregnancy life analysis. Watch as the Dirty Data Doctor hacks at knowledge with an ice axe. Its a great challenge to game the game with world knowledge. Full song will be part of a new album on a hip elk rack hip hop record. SEO is the ability to influence a machine to put your content in front of other people. Hello world my name is Data Mining Mike, I’m a data scientist in machine learning researcher with the BloomingBiz Media Network. How does a Search Engine Work? For example if you were to go to a search bar and type in “Can dogs eat”… it’s going to auto populate with a bunch of suggestions. The first (AI) is called a Recurrent Neural Network. The Recurrent Neural Network (RNN) takes all the typed words and removes all common stop words and special characters, including hashtags. Which is the First reason why you should Stop Using Hashtags. Next, every letter is converted to lowercase, and assigned a value. The values are then combined into phrases and tokenized. Markov probability chains this is the first predictive Brains. These chains are used to predict which words and phrases go together and operate in typical spell check systems. Recurrent Neural Networks are cool because they hold memories, long and short-term memory or LSTM you went to the search bar and typed: “McDonalds near me” that would be great because I have a claim to their future earnings. Dadadatdatda I’m loving dividends. The 2nd AI, K-Means Cluster Analysis, locates the node content type. All these types are used: K means clustering, hierarchical clustering, and density-based analysis is an unsupervised learning technique used to identify groups or clusters within a data set. The goal is to partition the data points into distinct groups based on their similarities or distances from each other. Once a related cluster is found, it hones in with the 3rd model. K Nearest Neighbors. K Nearest Neighbors (KNN) is a supervised learning algorithm used for classification and regression tasks. In KNN data points are represented as feature vectors in a multidimensional space the algorithm assigns a class or predicts a value for a new data point based on its proximity to the number ‘K’ of nearest neighbors in the data set. The choice of K determines the number of neighbors relies on the assumption that the data points with similar features tend to belong in the same class or have similar output both Clustering and KNN techniques involve proximity or similarity, the key difference is lies in the objectives. KNN is used for prediction or classification while cluster analysis is used for grouping or discovering Fourth and final Machine Learning Model is the Ranking model. Basically, Google Rank Brain because everyone is copying Google. What Rank Brain does is assesses the importance of a content node based on the number of quality nodes that are linking back to it in the cluster. Certain nodes have more value than others and therefore if they link to the node their vote for the node is stronger than mapping process of nodes in the cluster creates a link graph also known as the Google link matrix; the link matrix is a mathematical representation, typically as a square matrix where each row and column represent a web page and entities in the matrix indicate the existence or strengths of the links between the pages or nodes. The link matrix is used by Google’s page rank algorithm to calculate the importance of the authority of web pages based on the analysis of the link structure. The algorithm assigns a numerical value called a page rank score to each page, which reflects A relative importance based on the incoming links from other authoritative pages or nodes in the cluster mapped out within the link to the example, if you were to go to a search bar and type in “Can dogs eat”… it’s going to auto populate with a bunch of suggestions. With K Nearest Neighbors, the links are assessed between each cluster to the cluster node. All four of these models’ fire in that order, per keystroke, serving the probability, in the form of a suggestion, ‘C’, representing either ‘chicken bones’ or ‘chocolate’. Now I am highly confident that you have been exposed to little known Search Engine Optimization info. Because of that, you should ‘Like’ this video and share it because it increases your karma. Which I will discuss about in a latter video on SEO tips.