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If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Markov chains are used to model probabilities using information that can be encoded in the current state. Each state has a certain probability of transitioning to each other state, so each time you are in a state and want to transition, a markov chain can predict outcomes based on pre-existing probability data. More technically, information is put into a matrix and a vector - also called a column matrix - and with many iterations, a collection of probability vectors makes up Markov chains. To determine the transition probabilities, you have to "train" your Markov Chain on some input corpus.
Artificial intelligence (AI) is all the rage now. It's impacting numerous industries globally and changing the way we do things. One of the critical industries AI is making strides in is the financial technology "fintech" industry. AI now plays a significant role in facilitating financial services, replacing what required manual work a few years ago. For example, banks now apply AI to assess credit risks with high accuracy.
Science has been the most important tool of humanity even before the dawn of history. Humans have used science, without even knowing that they are scientists, to understand and improve every aspect of their lives. For many thousands of years, the way to handle, make sense of and address the experiences of life was through the use of science (and myth). "The purpose of science and art is one: to render experiences intelligible, i.e., to assist man to adjust himself and the environment in order that he may live" (White, 1938). Although the term'science' had different meaning than the one we use today, throughout history the knowledge created by science enabled humanity to create technologies.
New research* has found that one in three (37%) employees consider their current job to be at risk from automation and digital transformation. Around a third of women (33%) and over two-fifths (43%) of men consider it likely or very likely that automation could replace their jobs. While over half (54%) of those aged 18-to-24-years-old, compared to around a quarter (27%) of over-45s, believe that their job might not exist one day. Just because an occupation could become fully automated, however, doesn't mean it necessarily will. A more widely accepted view is that many roles will adapt and evolve and that new roles will be created, as even more work tasks and business processes – particularly those that are more routine or repetitive – can be done efficiently by machines.
The quantile function is a mathematical function that takes a quantile (a percentage of a distribution, from 0 to 1) as input and outputs the value of a variable. It can answer questions like, "If I want to guarantee that 95% of my customers receive their orders within 24 hours, how much inventory do I need to keep on hand?" As such, the quantile function is commonly used in the context of forecasting questions. In practical cases, however, we rarely have a tidy formula for computing the quantile function. Instead, statisticians usually use regression analysis to approximate it for a single quantile level at a time.
It is no secret that everybody wants to predict recessions. Many economists and finance firms have attempted this with limited success, but by and large there are several well known leading indicators for recessions in the US economy. However, when presented to the general public these indicators are typically taken alone, and are not framed in a way that can give probability statements associated with an upcoming recession. In this project, I have taken several of those economic indicators and built a classification model to generate probabilistic statements. Here, the actual classification ('recession' or'no recession') is not as important as the probability of a recession, since this probability will be used to determine a basic portfolio scheme which I will describe later on.
This course will help you develop Machine Learning skills for solving real-life problems in the new digital world. Machine Learning combines computer science and statistics to analyze raw real-time data, identify trends, and make predictions. The participants will explore key techniques and tools to build Machine Learning solutions for businesses. You don't need to have any technical knowledge to learn this skill. You'll start with the History of Machine Learning; Difference Between Traditional Programming and Machine Learning; What does Machine Learning do; Definition of Machine Learning; Apply Apple Sorting Example Experiences; Role of Machine Learning; Machine Learning Key Terms; Basic Terminologies of Statistics; Descriptive Statistics-Types of Statistics; Types of Descriptive Statistics; What is Inferential Statistics; What is Analysis and its types; Probability and Real-life Examples; How Probability is a Process; Views of Probability; Base Theory of Probability.
Artificial Intelligence in sports makes its presence felt in every corner of the world, from post-game analysis to in-game action to fan experience. If you watched the movie Moneyball, you must be in your element about how data-driven performance optimization in sports works and changes the games we dearly love for good. Coaches have employed data science in sports to enhance their players' performance for the previous two decades. They've been using big data to make split-second on-the-field judgments, and they've been relying on sports analytics to help them discover the next big thing for their game's and team's sake or a particular player's growth. Referees have also embraced Video Assistant Technology (VAR) in football to aid them in making more accurate judgments on the biggest calls, such as penalties, free kicks, and red cards.