Recently, I decided to hold a series of workshops about Java development at the CoderDojo Linz. As lots of kids and teens there enjoy playing Minecraft, it was a straightforward decision to choose Minecraft plugin development as the topic for these workshops. As I never played Minecraft before, I wanted to get to know the game first. My plan to play for 2 or 3 hours to learn the basics escalated rather quickly and I ended up not being productive for a week. Well, probably I could have anticipated that. Anyway, now I'm back and in the following section, I summarized some basics about Minecraft plugin development with Bukkit. Please note that this guide assumes, that you are already familiar with Java. If you are new to Java, I'd recommend getting to know Java first. Here you can find some great resources to learn it.

For all the hype around machine learning, its practical uses and what programmers and IT pros need to know is often left out of the conversation. The Machine Learning Mastery eBook Bundle provides a full and up-to-date training library on machine learning and its professional use cases. The 10-book set opens with three books for people new to the field with a volume on machine learning algorithms, mastery of those algorithms and a practical manual that shows you how to apply those algorithms to real-world data problems. A follow-up volume looks at the statistical basis for machine learning to help you build your own statistically sound tools. From there, three books look at Python's role in machine learning, with the first looking at Python libraries more generally and the second a more general advanced course using Python as the basis.

As the field of machine learning continues to grow, there is an increasing demand for professionals with the skills and knowledge to develop and implement machine learning solutions. Whether you're a data scientist, engineer, or business professional looking to expand your skillset, pursuing an online machine learning course can help you stand out in the job market and take your career to the next level. To help you get started, we've compiled a list of 15 top-ranked online machine-learning certificates that you can pursue in 2023. These certificates are offered by reputable institutions and are designed to provide you with the theoretical knowledge and practical skills needed to succeed in the field of machine learning. Check out the 15 best machine learning courses below that will help you cement a spot in this ever-growing industry!

Africa has a high student-to-teacher ratio which limits students' access to teachers for learning support such as educational question answering. In this work, we extended Kwame, our previous AI teaching assistant for coding education, adapted it for science education, and deployed it as a web app. Kwame for Science provides passages from well-curated knowledge sources and related past national exam questions as answers to questions from students based on the Integrated Science subject of the West African Senior Secondary Certificate Examination (WASSCE). Furthermore, students can view past national exam questions along with their answers and filter by year, question type (objectives, theory, and practicals), and topics that were automatically categorized by a topic detection model which we developed (91% unweighted average recall). We deployed Kwame for Science in the real world over 8 months and had 750 users across 32 countries (15 in Africa) and 1.5K questions asked. Our evaluation showed an 87.2% top 3 accuracy (n=109 questions) implying that Kwame for Science has a high chance of giving at least one useful answer among the 3 displayed. We categorized the reasons the model incorrectly answered questions to provide insights for future improvements. We also share challenges and lessons with the development, deployment, and human-computer interaction component of such a tool to enable other researchers to deploy similar tools. With a first-of-its-kind tool within the African context, Kwame for Science has the potential to enable the delivery of scalable, cost-effective, and quality remote education to millions of people across Africa.

How can one meaningfully make a measurement, if the meter does not conform to any standard and its scale expands or shrinks depending on what is measured? In the present work it is argued that current evaluation practices for machine-learning classifiers are affected by this kind of problem, leading to negative consequences when classifiers are put to real use; consequences that could have been avoided. It is proposed that evaluation be grounded on Decision Theory, and the implications of such foundation are explored. The main result is that every evaluation metric must be a linear combination of confusion-matrix elements, with coefficients - "utilities" - that depend on the specific classification problem. For binary classification, the space of such possible metrics is effectively two-dimensional. It is shown that popular metrics such as precision, balanced accuracy, Matthews Correlation Coefficient, Fowlkes-Mallows index, F1-measure, and Area Under the Curve are never optimal: they always give rise to an in-principle avoidable fraction of incorrect evaluations. This fraction is even larger than would be caused by the use of a decision-theoretic metric with moderately wrong coefficients.

Artificial intelligence (AI) is rapidly becoming a more prominent component of several global industries, including education. But in some industries, it has reached a point where workers are now concerned about whether or not their jobs are safe. When it comes to EdTech what makes a user interface engaging for a student? I've personally seen students open up an EdTech product, including Google Classroom, and immediately groan out loud. Lloyd Alexander once said, "We learn more by looking for the answer to a question and not finding it than we do from the answer itself." I love this quote because I've witnessed the truth of it firsthand in the classroom.

Environmental disturbances, such as sensor data noises, various lighting conditions, challenging weathers and external adversarial perturbations, are inevitable in real self-driving applications. Existing researches and testings have shown that they can severely influence the vehicles perception ability and performance, one of the main issue is the false positive detection, i.e., the ghost object which is not real existed or occurs in the wrong position (such as a non-existent vehicle). Traditional navigation methods tend to avoid every detected objects for safety, however, avoiding a ghost object may lead the vehicle into a even more dangerous situation, such as a sudden break on the highway. Considering the various disturbance types, it is difficult to address this issue at the perceptual aspect. A potential solution is to detect the ghost through relation learning among the whole scenario and develop an integrated end-to-end navigation system. Our underlying logic is that the behavior of all vehicles in the scene is influenced by their neighbors, and normal vehicles behave in a logical way, while ghost vehicles do not. By learning the spatio-temporal relation among surrounding vehicles, an information reliability representation is learned for each detected vehicle and then a robot navigation network is developed. In contrast to existing works, we encourage the network to learn how to represent the reliability and how to aggregate all the information with uncertainties by itself, thus increasing the efficiency and generalizability. To the best of the authors knowledge, this paper provides the first work on using graph relation learning to achieve end-to-end robust navigation in the presence of ghost vehicles. Simulation results in the CARLA platform demonstrate the feasibility and effectiveness of the proposed method in various scenarios.

Understanding the skill sets and knowledge required for any career is of utmost importance, but it is increasingly challenging in today's dynamic world with rapid changes in terms of the tools and techniques used. Thus, it is especially important to be able to accurately identify the required skill sets for any job for better career insights and development. In this paper, we propose and develop the Skill Recommendation (SkillRec) system for recommending the relevant job skills required for a given job based on the job title. SkillRec collects and identify the skill set required for a job based on the job descriptions published by companies hiring for these roles. In addition to the data collection and pre-processing capabilities, SkillRec also utilises word/sentence embedding techniques for job title representation, alongside a feed-forward neural network for job skill recommendation based on the job title representation. Based on our preliminary experiments on a dataset of 6,000 job titles and descriptions, SkillRec shows a promising performance in terms of accuracy and F1-score.

Data Science is bringing innovations to the world. The recent incident of Covid and ChatGPT is making the demand for Data scientists even more crucial in organizations. Employees with data science and analytical skills are highly valued and paid for in organizations. If you are interested in learning and understanding the field of data science, then this course is for you. In this course, you will get to understand the core areas of Data Science and the various career opportunities in Data Science.

This course is for everyone who has desire to learn JAVA. The game complexity increases with every section and you will be able to rise your knowledge throughout the course. You will develop amazing games and you will see how JAVA works moving things on screen and objects interaction. You will also create and import pictures used in the games and get familiar with creating randomly movable enemies, animating the game characters and playing music while playing the game. Learn how to create video games using Java, which is one of the leading free to use software development tool.