This is Moein Ud Din, I am instructor of mathematics, a programmer of Python, Artificial intelligence, machine learning, deep learning, image processing, NLP and an animation geek. It's my passion to pass my knowledge what I know to the desired entities. Kinda quintessential person, always hounding to make things ideal. I have plan in future to prepare more mathematics and IT courses, would love to make things change in my own certain way. Critics are most welcome as long as it helps me and my students.

Being a data scientist is one of the most lucrative and future proof careers with Glassdoor naming it the best job in America for the third consecutive year in a row with great future growth prospects and a median base salary of $110,000. I have recently made the transition from being a PhD student in Computer Science to a Senior Data Scientist at a large tech company. In this course I give you all the questions and answers that I used to prepare for my data science interviews as well as the questions and answers that I now expect when I am giving interviews to potential data science candidates. The course provides a complete list of 150 questions and answers that you can expects in a typical data science interview including questions on machine learning, neural networks and deep learning, statistics, practical experience, big data technologies, SQL, computer science, culture fit, questions for the interviewer and brainteasers.

The answer is multifaceted, but I'll try to make it simple: Training can be demanding. For companies, it takes time to organize courses, curricula and other content; track progress; and demonstrate ROI. Plus, employees don't tend to enjoy standardized training that fails to cater to their needs and takes time from their already busy schedules. These arguably onerous tasks take a fundamental business process and turn it into something many people are reluctant to participate in -- a disadvantage for any company. I strongly believe that great learning keeps organizations from stagnation and irrelevance.

In this course, we aim to specialize in artificial intelligence by working on 14 Machine Learning Projects and Deep Learning Projects at various levels (easy - medium - hard). Before starting the course, you should have basic Python knowledge. Our aim in this course is to turn real-life problems that seem difficult to do into projects and then solve them using latest versions of artificial intelligence algorithms (machine learning algortihms and deep learning algorithms) and Python(3.8). This course was prepared in August 2021. We will carry out some of our projects using machine learning and some using deep learning algorithms.

Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has been conducted on HAR and numerous approaches based on deep learning and machine learning have been exploited by the research community to classify human activities. The main goal of this review is to summarize recent works based on a wide range of deep neural networks architecture, namely convolutional neural networks (CNNs) for human activity recognition. The reviewed systems are clustered into four categories depending on the use of input devices like multimodal sensing devices, smartphones, radar, and vision devices. This review describes the performances, strengths, weaknesses, and the used hyperparameters of CNN architectures for each reviewed system with an overview of available public data sources. In addition, a discussion with the current challenges to CNN-based HAR systems is presented. Finally, this review is concluded with some potential future directions that would be of great assistance for the researchers who would like to contribute to this field.

Teaching artificial intelligence (AI) is challenging. It is a fast moving field and therefore difficult to keep people updated with the state-of-the-art. Educational offerings for students are ever increasing, beyond university degree programs where AI education traditionally lay. In this paper, we present an experience report of teaching an AI course to business executives in the United Arab Emirates (UAE). Rather than focusing only on theoretical and technical aspects, we developed a course that teaches AI with a view to enabling students to understand how to incorporate it into existing business processes. We present an overview of our course, curriculum and teaching methods, and we discuss our reflections on teaching adult learners, and to students in the UAE.

This paper describes principles and practices of knowledge engineering that enable the development of holistic language-endowed intelligent agents that can function across domains and applications, as well as expand their ontological and lexical knowledge through lifelong learning. For illustration, we focus on dialog act modeling, a task that has been widely pursued in linguistics, cognitive modeling, and statistical natural language processing. We describe an integrative approach grounded in the OntoAgent knowledge-centric cognitive architecture and highlight the limitations of past approaches that isolate dialog from other agent functionalities.

Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently. Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs. GAN stands for generative adversarial network, where 2 neural networks compete with each other. Unsupervised learning means we're not trying to map input data to targets, we're just trying to learn the structure of that input data. Once we've learned that structure, we can do some pretty cool things.

In this project, we will perform one of the most essential applications of machine learning – Customer Segmentation. We will implement customer segmentation in Apache Spark and Scala, whenever you need to find your best customer. Customer Segmentation is one of the most important applications of unsupervised learning. In this machine learning project, we will make use of K-means clustering which is the essential algorithm for clustering unlabeled datasets. Welcome to this project on Customer Segmentation using Apache Spark Machine Learning using Apache Zeppelin platform which allows you to execute your spark code in Apache Zeppelin notebook.

The purpose of the lecture course in Artificial Intelligence (AI) is to review and analyze the role and place of AI in the framework of the scientific-technical revolution and to explain, how it performs the creative functions that are traditionally considered the prerogative of a person. During the lecture course, the history of Artificial Intelligence development, different tests of the logical character, fields of business and economy, and other sciences, where the AI is used, main scientific centers of AI, and other interesting topics will be discussed. One of the particular definitions of intelligence, common to humans and "machines", can be formulated as follows: "Intelligence is the ability of a system to create, in the course of self-learning, programs (primarily heuristic) for solving problems of a certain class of complexity and to solve these problems."