Then this course is for you!! This course has been practically and carefully designed by industry experts to offer the best way of learning Data Science and Machine Learning the practical way with hands-on projects throughout the course. This course will help you learn complex Data Science concepts and machine learning algorithms the practical way for easier understanding. We will walk you through step-by-step on each topic explaining each line of code for your understanding. There is going to be a lot of fun, excited, and robust projects to better understand each concept under each topic.

Students should have strong coding skills and some familiarity with equity markets. No finance or machine learning experience is assumed. Note that this course serves students focusing on computer science, as well as students in other majors such as industrial systems engineering, management, or math who have different experiences. All types of students are welcome! The ML topics might be "review" for CS students, while finance parts will be review for finance students.

After completing this course, participants may receive a certificate. This AI course covers a data science workflow that starts with exploring a dataset and training a model and ends with deploying an application.

We will develop 15 AI Apps with Flutter using TensorFlow Machine Learning and Deep Learning Concepts. In this course you will also learn how to train a model/machine for your apps. And how to import and use these trained models after training in your flutter app (android iOS app). This is a complete step by step course. At the end of this course you will be able to make your own Ai, Deep Learning and Machine Learning Apps for the Android Smart Phones and iOS [iPhones] using Flutter SDK with TensorFlow Lite.

Learn key NLP concepts and intuition training to get you quickly up to speed with all things NLP. I will give you the information in an optimal way, I will explain in the first video for example what is the concept, and why is it important, what is the problem that led to thinking about this concept and how can I use it (Understand the concept). In the next video, you will go to practice in a real-world project or in a simple problem using python (Practice). The first thing you will see in the video is the input and the output of the practical section so you can understand everything and you can get a clear picture! You will have all the resources at the end of this course, the full code, and some other useful links and articles.

At a moment when vaccines promise to end the coronavirus pandemic, emerging new variants threaten to accelerate it. The astonishingly fast development of safe and effective vaccines is being stymied by the glacial pace of actual vaccinations while 3,000 Americans die each day. Minimizing death and suffering from COVID-19 requires vaccinating the most vulnerable Americans first and fast, but the vaccine rollout has been slow and inequitable. Prioritization algorithms have led to the most privileged being prioritized over the most exposed, and strict adherence to priority pyramids has been disastrously slow. Yet without prioritization, vaccines go to those with greatest resources rather than to those at greatest risk.

The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to ultimately meet her desired goal. This concept emerged several years ago and is being adopted by a rapidly-growing number of educational institutions around the globe. In recent years, the boost of artificial intelligence (AI) and machine learning (ML), together with the advances in big data analysis, has unfolded novel perspectives to enhance personalized education in numerous dimensions. By taking advantage of AI/ML methods, the educational platform precisely acquires the student's characteristics. This is done, in part, by observing the past experiences as well as analyzing the available big data through exploring the learners' features and similarities. It can, for example, recommend the most appropriate content among numerous accessible ones, advise a well-designed long-term curriculum, connect appropriate learners by suggestion, accurate performance evaluation, and the like. Still, several aspects of AI-based personalized education remain unexplored. These include, among others, compensating for the adverse effects of the absence of peers, creating and maintaining motivations for learning, increasing diversity, removing the biases induced by the data and algorithms, and the like. In this paper, while providing a brief review of state-of-the-art research, we investigate the challenges of AI/ML-based personalized education and discuss potential solutions.

Contributions: The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created an AI curriculum for pre-tertiary education and evaluated its efficacy. While AI is conventionally taught in tertiary level education, our co-creation process successfully developed the curriculum that has been used in secondary school teaching in Hong Kong and received positive feedback. Background: AI4Future is a cross-sector project that engages five major partners - CUHK Faculty of Engineering and Faculty of Education, Hong Kong secondary schools, the government and the AI industry. A team of 14 professors with expertise in engineering and education collaborated with 17 principals and teachers from 6 secondary schools to co-create the curriculum. This team formation bridges the gap between researchers in engineering and education, together with practitioners in education context. Research Questions: What are the main features of the curriculum content developed through the co-creation process? Would the curriculum significantly improve the students perceived competence in, as well as attitude and motivation towards AI? What are the teachers perceptions of the co-creation process that aims to accommodate and foster teacher autonomy? Methodology: This study adopted a mix of quantitative and qualitative methods and involved 335 student participants. Findings: 1) two main features of learning resources, 2) the students perceived greater competence, and developed more positive attitude to learn AI, and 3) the co-creation process generated a variety of resources which enhanced the teachers knowledge in AI, as well as fostered teachers autonomy in bringing the subject matter into their classrooms.

Hey, My name is Nilay Mehta! I am an experienced .Net developer, having the Microsoft certificate of Programming with C#.Net. I have a Master of Computer Applications and Bachelor of Computer Application degrees. I've worked with a range of development tools from PHP, C#, ASP.NET, and ASP.Net core. I am a passionate software engineer who loves learning new technologies, and from the past 3 years, I'm enjoying sharing that knowledge through blogs and courses.

Billions of users create a large amount of data every day, which in a sense comes from various types of sources. This data is in most cases unorganized and unclassified and is presented in various formats such as text, video, audio, or images. Processing and analyzing this data is a major challenge that we face every day. The problem of unstructured and unorganized text dates back to ancient times, but Text Classification as a discipline first appeared in the early 60s, where 30 years later the interest in various spheres for it increased [1], and began to be applied in various types of domains and applications such as for movie review [2], document classification [3], ecommerce [4], social media [5], online courses [6, 7], etc. As interest has grown more in the upcoming years, the uses start solving the problems with higher accurate results in more flexible ways. Knowledge Engineering (KE) was one of the applications of text classification in the late 80s, where the process took place by manually defining rules based on expert knowledge in terms of categorization of the document for a particular category [1]. After this time, there was a great wave of use of various modern and advanced methods for text classification, which all improved this discipline and made it more interesting for scientists and researchers, more specifically the use of machine learning techniques. These techniques bring a lot of advantages, as they are now in very large numbers, where they provide solutions to almost every problem we may encounter. The need for education and learning dates back to ancient times, where people are constantly improving and trying to gain as much knowledge as possible.