As the world enters a fully digital age, cyber threats are on the rise with massive data breaches, hacks into personal and financial data, and any other digital source that people can exploit. To combat these attacks, security experts are increasingly tapping into AI to stay a step ahead using every tool in their toolbox including unsupervised learning methods. Machine learning in the cybersecurity space is considered to still be in its infancy stage, but there has been a lot of traction since 2020 to have more AI involved in the process of combating cyber threats. Understanding how machine learning can be used in cyber security, recognizing the need for unsupervised learning methods in cyber security, and knowing how to implement AI in combating cyber attacks are the key to fighting cybercrime in the years ahead. The scary thing about cybercrime is that it can take up to six months to even detect a breach, and it takes an average of roughly 50 days from the time a breach is found to the time it is reported.

Are you a PhD student or researcher with an interest in science communication? We are recruiting AIhub ambassadors to help us write about the latest news, research, conferences, and more, in the field of artificial intelligence. Ideally you would produce a series of blog posts on aspects of the field that interest you. You could write about some significant research, give a tutorial, or cover a session at a conference. You could draw attention to exciting new developments in the field, interview a researcher, produce a tutorial video, review a paper or book, or summarise recent social media commentary.

More and more businesses are adopting machine learning, from predictive analysis to improving the overall workflow of the organization. Machine Learning has now become a critical element of business functionality in the past few years. Businesses are curious about how implementing technology can benefit them, whilst Machine Learning professionals are eager to learn how far Machine Learning can take us. In order for this to be successful, the operations behind it need to become proficient in understanding the concept of Machine Learning, being able to analyse the data, tweaking algorithms, solving problems, and more. That seems like a lot of work, that's umbrellaed under one topic.

On the Internet, there are a lot of sources that provide enormous amounts of daily news. Further, the demand for information by users has been growing continuously, so it is important to classify the news in a way that lets users access the information they are interested in quickly and efficiently. Using this model, users would be able to identify news topics that go untracked, and/or make recommendations based on their prior interests. Thus, we aim to build models that take news headlines and short descriptions as inputs and produce news categories as outputs. The problem we will tackle is the classification of BBC News articles and their categories.

How to implement Generative AI models. Recently, we have seen a shift in AI that wasn't very obvious. Generative Artificial Intelligence (GAI) - the part of AI that can generate all kinds of data - started to yield acceptable results, getting better and better. As GAI models get better, questions arise e.g. Or, how to utilize data generation for your own projects?

Whether games between family and friends are as easy to orchestrate in VR will be the big question for the murder mystery game. The game previously rose to popularity in part because both the mobile app and PC version of the game were easy to download and play on those widely owned platforms. The VR version will of course require a VR headset. While Meta's sales of the Oculus Quest 2 nearly doubled in 2021 with 8.7 million headsets sold, smartphone sales alone topped 1.3 billion last year. The disparity highlights the much smaller potential audience for VR as the technology aims to secure a larger share of the gaming market.

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The Recurrent Neural Network (RNN) has been used to obtain state-of-the-art results in sequence modeling. This includes time series analysis, forecasting and natural language processing (NLP). Learn about why RNNs beat old-school machine learning algorithms like Hidden Markov Models. All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow.

Machine Learning is making the computer learn from studying data and statistics. This course will help you develop Machine Learning skills for solving real-life problems in the new digital world. Machine Learning combines computer science and statistics to analyze raw real-time data, identify trends, and make predictions. The participants will explore key techniques and tools to build Machine Learning solutions for businesses. You don't need to have any technical knowledge to learn this skill.

This forms the basis for everything else. This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python. One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don't know enough about the Numpy stack in order to turn those concepts into code. Even if I write the code in full, if you don't know Numpy, then it's still very hard to read. This course is designed to remove that obstacle - to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science.