Welcome to Machine Learning with Imbalanced Datasets. In this course, you will learn multiple techniques which you can use with imbalanced datasets to improve the performance of your machine learning models. If you are working with imbalanced datasets right now and want to improve the performance of your models, or you simply want to learn more about how to tackle data imbalance, this course will show you how. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about working with imbalanced datasets. Throughout this comprehensive course, we cover almost every available methodology to work with imbalanced datasets, discussing their logic, their implementation in Python, their advantages and shortcomings, and the considerations to have when using the technique.

Some early adopters are already experimenting with the generative AI program ChatGPT at the office. In seconds, consultants are conjuring decks and memos, marketers are cranking out fresh copy and software engineers are debugging code. Almost 30% of the nearly 4,500 professionals surveyed this month by Fishbowl, a social platform owned by employer review site Glassdoor, said that they've already used OpenAI's ChatGPT or another artificial intelligence program in their work. Respondents include employees at Amazon, Bank of America, JPMorgan, Google, Twitter and Meta. The chatbot uses generative AI to spit out human-like responses to prompts in seconds, but because it's been trained on information publicly available from the internet, books and Wikipedia, the answers aren't always accurate.

ChatGPT is a powerful language model developed by OpenAI that has the ability to generate human-like text, making it capable of engaging in natural language conversations. This technology has the potential to revolutionize the way we interact with computers, and it has already begun to be integrated into various industries. However, the implementation of ChatGPT in the field of higher education in the UK poses a number of challenges that must be carefully considered. If ChatGPT is used to grade assignments or exams, there is the possibility that it could be biased against certain groups of students. For example, ChatGPT may be more likely to give higher grades to students who write in a style that it is more familiar with, potentially leading to unfair grading practices.

When the company OpenAI launched its new artificial intelligence program, ChatGPT, in late 2022, educators began to worry. ChatGPT could generate text that seemed like a human wrote it. How could teachers detect whether students were using language generated by an AI chatbot to cheat on a writing assignment? As a linguist who studies the effects of technology on how people read, write and think, I believe there are other, equally pressing concerns besides cheating. These include whether AI, more generally, threatens student writing skills, the value of writing as a process, and the importance of seeing writing as a vehicle for thinking.

Artificial intelligence (AI), which enables machines to learn to perform a task by training on diverse datasets, is one of the most revolutionary developments in scientific history. Although AI and especially deep learning is relatively new, it has already had transformative impact on medicine, biology, transportation, entertainment, and beyond. As AI changes our daily lives at an increasingly fast pace, we are challenged with preparing our society for an AI-driven future. To this end, a critical step is to ensure an AI-ready workforce through education. Advocates of beginning instruction of AI basics at the K-12 level typically note benefits to the workforce, economy, and national security. In this complementary perspective, we discuss why learning AI is beneficial for motivating students and promoting creative thinking, and how to develop a module-based approach that optimizes learning outcomes. We hope to excite and engage more members of the education community to join the effort to advance K-12 AI education in the USA and worldwide.

Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Contextbased challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.

Machine Learning graphs (or models) can be challenging or impossible to train when either devices have limited memory, or models are large. To split the model across devices, learning-based approaches are still popular. While these result in model placements that train fast on data (i.e., low step times), learning-based model-parallelism is time-consuming, taking many hours or days to create a placement plan of operators on devices. We present the Baechi system, the first to adopt an algorithmic approach to the placement problem for running machine learning training graphs on small clusters of memory-constrained devices. We integrate our implementation of Baechi into two popular open-source learning frameworks: TensorFlow and PyTorch. Our experimental results using GPUs show that: (i) Baechi generates placement plans 654 X - 206K X faster than state-of-the-art learning-based approaches, and (ii) Baechi-placed model's step (training) time is comparable to expert placements in PyTorch, and only up to 6.2% worse than expert placements in TensorFlow. We prove mathematically that our two algorithms are within a constant factor of the optimal. Our work shows that compared to learning-based approaches, algorithmic approaches can face different challenges for adaptation to Machine learning systems, but also they offer proven bounds, and significant performance benefits.

Human mobility clustering is an important problem for understanding human mobility behaviors (e.g., work and school commutes). Existing methods typically contain two steps: choosing or learning a mobility representation and applying a clustering algorithm to the representation. However, these methods rely on strict visiting orders in trajectories and cannot take advantage of multiple types of mobility representations. This paper proposes a novel mobility clustering method for mobility behavior detection. First, the proposed method contains a permutation-equivalent operation to handle sub-trajectories that might have different visiting orders but similar impacts on mobility behaviors. Second, the proposed method utilizes a variational autoencoder architecture to simultaneously perform clustering in both latent and original spaces. Also, in order to handle the bias of a single latent space, our clustering assignment prediction considers multiple learned latent spaces at different epochs. This way, the proposed method produces accurate results and can provide reliability estimates of each trajectory's cluster assignment. The experiment shows that the proposed method outperformed state-of-the-art methods in mobility behavior detection from trajectories with better accuracy and more interpretability.

Artificial intelligence is expected to have a huge impact on Post Graduate Diploma in Management (PGDM) students, from handling job migration to Robo-educators. Recently, artificial intelligence has moved beyond science fiction and will disrupt how we live our lives. Business is being impacted left, right, and focused. Add to that the extended 40 per cent expansion in labor efficiency from computer-based intelligence use, and the 61 per cent of business experts who say AI and machine learning are their association's most critical information drive. Artificial intelligence is crawling into the daily existence of the present PGDM understudy as well, and occasionally in astonishing ways.

The graph represents a network of 2,605 Twitter users whose tweets in the requested range contained "deeplearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 14 December 2022 at 17:27 UTC. The requested start date was Wednesday, 14 December 2022 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 3-day, 8-hour, 12-minute period from Saturday, 10 December 2022 at 16:46 UTC to Wednesday, 14 December 2022 at 00:59 UTC.