This is my first data analysis related video. In this video, I have solved the Fake news detection problem using four machine learning classification algorithms. Hi everyone, This is my first data analysis related video. In this video, I have solved the Fake news detection problem using four machine learning classification algorithms. From this video, you will learn how you can apply Linear regression, Decision Tree classification, Gradient boost classification, and random forest classification model.

Data science is an interdisciplinary field that uses scientific methods, processes, and algorithms to extract knowledge and insights from data. The field of data science has several subdivisions such as data mining, data transformation, data visualization, machine learning, deep learning, etc. This is the stage where data is collected, analyzed to unravel patterns and relationships in the data, and questions are asked to be answered using the data. This is where mathematical skills come into play. In this stage, mathematical tools are used to build models (predictive models) for quantifying patterns or studying the relationships between features in the dataset.

Inductive inference in supervised classification context constitutes to methods and approaches to assign some objects or items into different predefined classes using a formal rule that is derived from training data and possibly some additional auxiliary information. The optimality of such an assignment varies under different conditions due to intrinsic attributes of the objects being considered for such a task. One of these cases is when all the objects' features are discrete variables with a priori known categories. As another example, one can consider a modification of this case with a priori unknown categories. These two cases are the main focus of this thesis and based on Bayesian inductive theories, de Finetti type exchangeability is a suitable assumption that facilitates the derivation of classifiers in the former scenario. On the contrary, this type of exchangeability is not applicable in the latter case, instead, it is possible to utilise the partition exchangeability due to John Kingman. These two types of exchangeabilities are discussed and furthermore here I investigate inductive supervised classifiers based on both types of exchangeabilities. I further demonstrate that the classifiers based on de Finetti type exchangeability can optimally handle test items independently of each other in the presence of infinite amounts of training data while on the other hand, classifiers based on partition exchangeability still continue to benefit from joint labelling of all the test items. Additionally, it is shown that the inductive learning process for the simultaneous classifier saturates when the amount of test data tends to infinity.

Learn Machine Learning Practically By Building Projects What you'll learn Description Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to self-learn and improve over time without being explicitly programmed. In short, machine learning algorithms are able to detect and learn from patterns in data and make their own predictions. In traditional programming, someone writes a series of instructions so that a computer can transform input data into a desired output. Instructions are mostly based on an IF-THEN structure: when certain conditions are met, the program executes a specific action. Machine learning, on the other hand, is an automated process that enables machines to solve problems and take actions based on past observations.

I've recently launched my online course about Supervised Machine Learning in Python. In this post, I'll explain to you 5 reasons it's worth joining it. In my experience as a Data Scientist and Physicist I've understood that, sometimes, practice is more important than theory. Data Science is a very practical discipline and a data scientist is like a craftsman working data just like wood. He needs to know how the wood responds to his tools, but his job is more practical than theoretical.

This tutorial (view the original article here) introduces our new R Package, Modeltime H2O. If you like what you see, I have an Advanced Time Series Course where you will learn the foundations of the growing Modeltime Ecosystem. This article is part of a series of software announcements on the Modeltime Forecasting Ecosystem. Register to stay in the know on new cutting-edge R software like modeltime. Modeltime H2O is part of a growing ecosystem of Modeltime forecasting packages.

In this course for beginners, you will get started with Data Science and SQL Server Machine Learning Services. You will learn the basics of Data Science, as well as, how you can start implementing Data Science projects in SQL Server with Python, via its Machine Learning built-in feature. Data Science, Big Data, Machine Learning and Artificial Intelligence, are the areas of technology that have been significantly evolved over the last few years. These technologies, are already heavily used by many organizations, in order to efficiently solve complex problems. Among other, they are used for predicting patterns based on large data sets and thus transform raw data into meaningful knowledge.

Neural networks are a family of machine learning algorithms that are generating a lot of excitement. They are a technique that is inspired by how the neurons in our brains function. They are based on a simple idea: given certain parameters, it is possible to combine them in order to predict a certain result. For example, if you know the number of pixels in an image, there are ways of knowing which number is written in the image. The data that enters passes through various " layers" in which a series of adjusted learning rules are applied by a weighted function.

Today, Artificial Intelligence (AI), Machine Learning, and Deep Learning technologies are used in diverse fields as part of the daily life of large organizations across the globe. The rapid speed of AI growth demonstrates that it is a groundbreaking technology designed to transform the way people use devices and conduct business: achievements in unmanned aerial vehicles, the ability to beat people in chess and sporting games, automated customer service, and analytical systems - of course. Talking about the business, development, or marketing field, for instance, it is worth noting that Artificial Intelligence does not apply in a pure form to real self-aware intelligence machines in this sense. Instead, it can be considered a generic term for the number of software powered by automation that is being used by developers of websites and smartphone apps. They include the recognition of images and speech, cognitive computing, automated processing, and machine learning - for that matter. Speaking of AI in app creation, for many years, starting with Apple's Siri, AI has already been influential in app-creation and marketing growth.

From transforming marketing strategies to delivering thrilling and engaging digital fan experiences, find out how sports organisations can adjust to a digital-first future. Explore what’s possible now and how the AI ladder can help you develop the enterprise capabilities required to bring technology-driven initiatives to life. The world is undergoing tremendous change that impacts all industries, and the sports ecosystem is no different. The current health crisis caused training sessions, matches and sports competitions to be postponed or even cancelled entirely, disrupting all the stakeholders in the industry and leaving fans everywhere disconnected from the teams and the sportspeople they love. Now, organisations are left to navigate the implications of these extreme measures, while planning for a future that may look very different from the past. When technology meets sport Sport, in all its forms, is driven by – and produces, in turn – copious amounts of data. Purposefully exploiting that abundance of data can enable sports…