With a GDP of AED 1.5 trillion in 2020, the UAE's economy is the fifth-largest in the Middle East. The UAE economy, which was once reliant on oil exports, is now increasingly dependent on earnings from petroleum and natural gas. Economic diversification has occurred in recent years, particularly in Dubai. According to studies, the worldwide number of internet-connected devices is predicted to reach 1 trillion by 2030, with the UAE alone expected to achieve this amount by 2050. As a transit country between the East and the West with a pro-business environment, the UAE has become a technology powerhouse for the Internet of Things in all fields, enabling digital transformation in airports, freight, and logistics.

Python has become the most popular data science and machine learning programming language. But in order to obtain effective data and results, it's important that you have a basic understanding of how it works with machine learning. In this introductory tutorial, you'll learn the basics of Python for machine learning, including different model types and the steps to take to ensure you obtain quality data, using a sample machine learning problem. In addition, you'll get to know some of the most popular libraries and tools for machine learning. Machine learning (ML) is a form of artificial intelligence (AI) that teaches computers to make predictions and recommendations and solve problems based on data. Its problem-solving capabilities make it a useful tool in industries such as financial services, healthcare, marketing and sales, and education among others. There are three main types of machine learning: supervised, unsupervised, and reinforcement. In supervised learning, the computer is given a set of training data that includes both the input data (what we want to predict) and the output data (the prediction).

In this article we will be learning in depth about the nuances of how one can start with Docker and is it really important?r

In 2022, Artificial Intelligence is the hottest and most in-demand field; most engineers want to make their careers in AI, Data Science & Data Analytics. Going through the best and most reliable resources is the best way to learn, so here is the list of the best AI books on the market today. Artificial Intelligence is the field of study that simulates the processes of human intelligence on computer systems. These processes include the acquisition of information, using them, and approximating conclusions. The research topics in AI include problem-solving, reasoning, planning, natural language, programming, and machine learning. Automation, robotics, and sophisticated computer software and programs characterize a career in Artificial Intelligence.

Group work is a prevalent activity in educational settings, where students are often divided into topic-specific groups based on their preferences. The grouping should reflect the students' aspirations as much as possible. Usually, the resulting groups should also be balanced in terms of protected attributes like gender or race since studies indicate that students might learn better in a diverse group. Moreover, balancing the group cardinalities is also an essential requirement for fair workload distribution across the groups. In this paper, we introduce the multi-fair capacitated (MFC) grouping problem that fairly partitions students into non-overlapping groups while ensuring balanced group cardinalities (with a lower bound and an upper bound), and maximizing the diversity of members in terms of protected attributes. We propose two approaches: a heuristic method and a knapsack-based method to obtain the MFC grouping. The experiments on a real dataset and a semi-synthetic dataset show that our proposed methods can satisfy students' preferences well and deliver balanced and diverse groups regarding cardinality and the protected attribute, respectively.

The general aim of the course is to equip participants with practical and technical knowledge to deploy machine learning methods on genomic data sets. With this aim in mind, we will go through certain statistical concepts and move on to unsupervised and supervised machine learning methods to analyze high-dimensional data sets. There will be theoretical lectures followed by practical sessions where students directly apply what they have learned. These sessions will be provided online in succession. Participants will have a week to work on each module in their own time. Interactions will be provided over the online teaching platform.

Intel, in association with Analytics India Magazine, is organising a webinar on "achieving real-time AI inference on your CPU" on 7th July, from 5:00 – 6:30 PM (IST). We all know that the amount of data generated in today's world is exponential. AI Inference involves the process of using a trained neural network model to predict an outcome. For a typical AI workflow, the workloads associated with all the steps involved follow a diverse mechanism and a single GPU or CPU cannot work for the entire pipeline smoothly. To this end, Intel is organising this webinar for the attendees to understand how to optimise a deep learning neural network model and achieve fast AI inference with a CPU.

Have you been using your loss function for evaluating your machine learning system's performance? That's a mistake, but don't worry, you're not alone. It's a widespread misunderstanding that may have something to do with software defaults, college course format, and decision-maker absenteeism in AI. In this article, I'll explain why you need two separate model scoring functions for evaluation and optimization… and possibly a third one for statistical testing. Throughout data science, you'll see scoring functions (like the MSE, for example) being used for three main purposes: These three are subtly -- but importantly -- different from one another, so let's take a deeper look at what makes a function "good" for each purpose.

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It is possible to design and deploy advanced machine learning algorithms that are essentially math-free and stats-free. People working on that are typically professional mathematicians. These algorithms are not necessarily simpler. See for instance a math-free regression technique with prediction intervals, here. Or supervised classification and alternative to t-SNE, here. Interestingly, this latter math-free machine