The graph represents a network of 2,721 Twitter users whose tweets in the requested range contained "iot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 30 November 2022 at 12:31 UTC. The requested start date was Wednesday, 30 November 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 4-day, 2-hour, 0-minute period from Friday, 25 November 2022 at 23:00 UTC to Wednesday, 30 November 2022 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 5,915 Twitter users whose tweets in the requested range contained "(Artificial Intelligence) OR #AI", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 01 December 2022 at 05:40 UTC. The requested start date was Thursday, 01 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 2-day, 1-hour, 12-minute period from Monday, 28 November 2022 at 23:10 UTC to Thursday, 01 December 2022 at 00:23 UTC.

Complete Blender Creator: Learn 3D Modelling for Beginners - Use Blender to Create Beautiful 3D models for Video Games, 3D Printing & More. This course is in the process of being completely remastered in Blender 3.2. Currently both the new and original content are in this course, once the remaster is complete students will be able to access the original 2.8 content in a separate archive course. Blender is a fantastic platform which enables you to make AAA-quality models which can be exported to any game engine, 3D printer, or other software. Here are some of the reasons why you want to learn Blender with this online tutorial... Create assets for video games.

ML-based motion planning is a promising approach to produce agents that exhibit complex behaviors, and automatically adapt to novel environments. In the context of autonomous driving, it is common to treat all available training data equally. However, this approach produces agents that do not perform robustly in safety-critical settings, an issue that cannot be addressed by simply adding more data to the training set - we show that an agent trained using only a 10% subset of the data performs just as well as an agent trained on the entire dataset. We present a method to predict the inherent difficulty of a driving situation given data collected from a fleet of autonomous vehicles deployed on public roads. We then demonstrate that this difficulty score can be used in a zero-shot transfer to generate curricula for an imitation-learning based planning agent. Compared to training on the entire unbiased training dataset, we show that prioritizing difficult driving scenarios both reduces collisions by 15% and increases route adherence by 14% in closed-loop evaluation, all while using only 10% of the training data.

The introductory programming sequence has been the focus of much research in computing education. The recent advent of several viable and freely-available AI-driven code generation tools present several immediate opportunities and challenges in this domain. In this position paper we argue that the community needs to act quickly in deciding what possible opportunities can and should be leveraged and how, while also working on how to overcome or otherwise mitigate the possible challenges. Assuming that the effectiveness and proliferation of these tools will continue to progress rapidly, without quick, deliberate, and concerted efforts, educators will lose advantage in helping shape what opportunities come to be, and what challenges will endure. With this paper we aim to seed this discussion within the computing education community.

The data features that you use to train your machine learning models have a huge influence on the performance you can achieve. Irrelevant or partially relevant features can negatively impact model performance. In this post you will discover automatic feature selection techniques that you can use to prepare your machine learning data in python with scikit-learn. Feature Selection For Machine Learning in Python Photo by Baptiste Lafontaine, some rights reserved. Feature selection is a process where you automatically select those features in your data that contribute most to the prediction variable or output in which you are interested.

Do you want to upgrade your skills with Best Data Analytics Certification Online to stand out in the industry? Here is a list of Best Data Analytics Courses Online, Training, Tutorials, and Classes to assist you to become a top Data Analyst. Now Big data, Data Science, Machine Learning, Deep Learning, Artificial Intelligence (AI), Analytics, Python, R, r-stats are the most trending and highly demanding subjects in every sector for almost every industry. Learn business analytics to get hands-on knowledge of big data analytics, data visualization, data management, and data mining as an analytics professional. The majority of business professionals are upgrading their skills with Best Data Analytics Training to stand out in their industry.

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

Machine learning (ML) is a field of inquiry devoted to understanding and building methods that'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. When beginning your educational path, it's important to first understand how to learn ML. We've broken the learning process into four areas of knowledge, with each area providing a foundational piece of the ML puzzle. To help you on your path, we've identified books, videos, and online courses that will uplevel your abilities, and prepare you to use ML for your projects.

Kickstart your learning of Python with this beginner-friendly self-paced course taught by an expert. Python is one of the most popular languages in the programming and data science world and demand for individuals who have the ability to apply Python has never been higher. This introduction to Python course will take you from zero to programming in Python in a matter of hours--no prior programming experience necessary! You will learn about Python basics and the different data types. You will familiarize yourself with Python Data structures like List and Tuples, as well as logic concepts like conditions and branching.