In statistics, Linear Regression is a linear approach for modeling the relationship between a scalar dependent variable Y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. In Linear Regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models.

This volume introduces working with text, with a focus on the most plentiful source of text out there: email. Working with email text from your own Gmail account, you will build up a label predictor, similar in effect to the technology Google uses to power the Social and Promotions tabs. With this technique, you will be able to build your own email classification and automated workflow hooks. Will Ballard serves as Chief Technology Officer at GLG and is responsible for the Engineering and IT organizations. Prior to joining GLG, Will was the Executive Vice President of Technology and Engineering at Demand Media.

Microsoft is the latest company to offer courses in artificial intelligence (A.I.). Microsoft's Professional Program for Artificial Intelligence includes 10 online courses that teach 10 skills. Each course takes roughly 8-16 hours to complete and kicks off at the beginning of each quarter. The courses, which can be taken in any order, range from "Introduction to AI" to "Essential Mathematics for Artificial Intelligence." The artificial-intelligence program joins a number of other Microsoft professional tracks, including software development, IT support, DevOps, and more.

Considered the Holy Grail of automation, data analysis, and robotics, Artificial Intelligence has taken the world by storm as a major field of research and development. Python has surfaced as a dominate language in AI/ML programming because of its simplicity and flexibility, in addition to its great support for open source libraries such as spaCy and TensorFlow. This video course is built for those with a basic understanding of artificial intelligence, introducing them to advanced artificial intelligence projects as they go ahead. The first project introduces natural language processing including part-of-speech tagging and named entity extraction. Wikipedia articles are used to demonstrate the extraction of keywords, and the Enron email archive is mined for mentions and relationships of people, places, and organizations.

Have an app idea that requires machine learning? Then this course is for you! Join me as we dive into Apple's latest iOS 11 API - CoreML - a native iOS framework built with Swift. "I am about a third through this course and I have learned so much. This course is worth way more than what it cost but I'm thankful prices are low or I might have passed it up in the first place not knowing what I would get. I have used a couple Udemy courses and countless youtube tutorials. This is the best course I've ever took." - Jeffrey Nelson "The course offers interesting concepts coupled with a teacher that explains things clearly. You get to make a bunch of interesting apps and expand your skills. "Clear tutorials, the lecturer explains everything well.

Big Data is the future, and it's right here, right now! There's no doubt about it that Big Data is a powerful discovery tool, but all too often when you analyse a lot of data, you end up with a lot of results - too many, in fact, to be able to hold them all in your head simultaneously. So I'll amend my earlier statement: Data Visualisation is the future, and it's right here, right now! Apparently, visuals are processed 60,000 times faster in the brain than text, and are more easily committed to long-term memory. Visuals also make it easier to tell stories with data. Hey - I think I've heard that before somewhere...(see website footer for a clue!). Most of all though - visuals can help to simplify complex information.

Learn how to create Artificial Intelligent Agents that have Flocking Behavior and apply them to your projects in games or movies. You have seen Flocking behavior in nature, in games, in movies and in architectural simulations but you might have missed it. Both pseudocode and Unity C# lectures complement each other giving you a full perspective. You will have access to the course forum where you can discuss each topic with like-minded, A.I. passionate, students like yourself. With the help of this course you will be able to understand a piece of nature and replicate it, essentially reverse engineer a piece of nature.

Know the key concepts of Machine Learning. Learn how to create disruptive business models based on Machine Learning. Every few years, there is a technological trend that leads to the creation of thousands of startups and/or new businesses. At present, we can say without any doubt that one of these trends is Machine Learning (Artificial Intelligence). To put it in context, McKinsey (one of the leading Management Consulting companies worldwide) tells us that Tech giants including Baidu and Google are spending between $20B to $30B on AI, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions.

With Core ML, you can integrate trained machine learning models into your apps. In this course, you'll get an an introduction to the Core ML framework. You'll learn how to incorporate Apple's Core ML framework into your app. You'll also get a quick overview of machine learning fundamentals, and exposure to real-world examples of companies using machine learning technology in their iOS apps In this course you'll learn the advantages of using machine learning models, computer vision, and natural language processing in modern apps. In addition, this course walks through the development of sample apps that leverage different machine learning features.

ML has become a fundamental part of the 21st century; from Netflix recommendations to fraud detection, ML is ever- present in our daily lives. At its roots, ML effectively applies statistics and pattern recognition, we will use these ideas to help solve a range of modern-day problems. C is a very fast language to execute your code and is extensively used when your final "models" are being deployed. If you want to run a program, with a lot of array calculation then C should be your weapon of choice. This course will start off with a broad overview of ML and the varying methods associated with it.