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) …
A Center of Excellence is "a team, a shared facility or an entity that provides leadership, best practices, research, support, and training for a focus area," and they are commonly used in healthcare to focus on specific problems or disciplines. I advocate that they can be used in organizations for artificial intelligence (AI) as well. What makes AI a strong candidate for a dedicated Center of Excellence is its rapidly expanding role as mission-critical technology in enterprises. Companies are finding that people in many different business units--not just data science or IT--want to be or are already involved with AI. In some cases, people are bringing in their own AI tools and solutions, but there is a need to orchestrate this buying to avoid waste.
As you hopefully have heard, we at scikit-learn are doing a user survey (which is still open by the way). One of the requests there was to provide some sort of flow chart on how to do machine learning. As this is clearly impossible, I went to work straight away. This is the result: [edit2] clarification: With ensemble classifiers and ensemble regressors I mean random forests, extremely randomized trees, gradient boosted trees, and the soon-to-be-come weight boosted trees (adaboost). More seriously: this is actually my work flow / train of thoughts whenever I try to solve a new problem.
Converting dates to numbers is important because while time is essential for a model's consideration, it cannot handle datetime objects. Instead, time can be represented as an integer. The majority of a data science project comprises of data cleaning and manipulation. Images created by author unless explicitly stated otherwise. Missing values often plague data, and given that there are not too many of them, they can be imputed (filled in).
Data Science is an ever-growing field, there are numerous tools & techniques to remember. It is not possible for anyone to remember all the functions, operations and formulas of each concept. That's why we have cheat sheets. But there are a plethora of cheat sheets available out there, choosing the right cheat sheet is a tough task. So, I decided to write this article. Enjoy and feel free to share!
It wasn't too long ago that talking to a computer and having it not only understand, but speak back, was confined to the realm of science fiction, like that of the shipboard computers of Star Trek. The technology of the 24th century's Starship Enterprise is reality in the 21st century thanks to natural language processing (NLP), a machine learning-driven discipline that gives computers the ability to understand, process, and respond to spoken words and written text. Make no mistake: NLP is a complicated field that one can spend years studying. This guide contains the basics about NLP, details how it can benefit businesses, and explains where to get started with its implementation. Natural language processing (NLP) is a cross-discipline approach to making computers hear, process, understand, and duplicate human language.
Wikipedia defines cheat sheets as a concise set of notes used for quick reference. Now the word that needs to be emphasized here is'quick reference'. In programming, cheat sheets are OK because no one can remember all the syntax of a programming language. Especially if the programming language constantly evolves (like Python) or if the programmer finds himself/herself transitioning in and out of different programming languages. A quick reference like a cheat sheet helps the programmer save time and focus on the larger problem.
Dimensionality reduction is the process of expressing high-dimensional data in a reduced number of dimensions such that each one contains the most amount of information. Dimensionality reduction may be used for visualization of high-dimensional data or to speed up machine learning models by removing low-information or correlated features. Principal Component Analysis, or PCA, is a popular method of reducing the dimensionality of data by drawing several orthogonal (perpendicular) vectors in the feature space to represent the reduced number of dimensions. The variable number represents the number of dimensions the reduced data will have. In the case of visualization, for example, it would be two dimensions.
Artificial intelligence as a discipline consists of hundreds of individual technologies, concepts, and applications. These terms have become increasingly important as STEM education expands and there is a boom in practical household and consumer-facing applications for the technology. Despite that, there is a lack of consistency in how many AI concepts are discussed, not just at the STEM education level, but in popular entertainment, science writing, and even at times in scientific journals. To address this, we need to standardize how we describe AI and its many subsets, and accurately define these terms both in general and specific to individual technologies and applications of those technologies. We discuss some of the most commonly misused and what they really mean.
Over the past few months, I have been collecting AI cheat sheets. From time to time I share them with friends and colleagues and recently I have been getting asked a lot, so I decided to organize and share the entire collection. To make things more interesting and give context, I added descriptions and/or excerpts for each major topic. Update: We have recently redesigned these cheat sheets into a Super High Definition PDF.
If you are a beginner and just started machine learning or even an intermediate level programmer, you might have been stuck on how do you solve this problem. Where do you start? and where do you go from here? In Machine Learning, there's no single solution that can fit all and multiple solutions to a problem can exist. With lots of varieties of algorithms, choosing the right algorithm for your problem can become a daunting task. Don't worry! in this article, we will be simplifying your approach in Machine Learning with a cheat sheet that you can use to select the right algorithm suited for your problem.