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- All Subjects: Machine Learning
- Creators: Arts, Media and Engineering Sch T
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The pandemic that hit in 2020 has boosted the growth of online learning that involves the booming of Massive Open Online Course (MOOC). To support this situation, it will be helpful to have tools that can help students in choosing between the different courses and can help instructors to understand what the students need. One of those tools is an online course ratings predictor. Using the predictor, online course instructors can learn the qualities that majority course takers deem as important, and thus they can adjust their lesson plans to fit those qualities. Meanwhile, students will be able to use it to help them in choosing the course to take by comparing the ratings. This research aims to find the best way to predict the rating of online courses using machine learning (ML). To create the ML model, different combinations of the length of the course, the number of materials it contains, the price of the course, the number of students taking the course, the course’s difficulty level, the usage of jargons or technical terms in the course description, the course’s instructors’ rating, the number of reviews the instructors got, and the number of classes the instructors have created on the same platform are used as the inputs. Meanwhile, the output of the model would be the average rating of a course. Data from 350 courses are used for this model, where 280 of them are used for training, 35 for testing, and the last 35 for validation. After trying out different machine learning models, wide neural networks model constantly gives the best training results while the medium tree model gives the best testing results. However, further research needs to be conducted as none of the results are not accurate, with 0.51 R-squared test result for the tree model.
Meanwhile, machine learning is growing more advanced by the day. Online retailers like Amazon run complex algorithms to recommend future purchases and monitor price changes. Mobile phones use neural networks to interpret speech. GPS apps track anonymous motion data in smartphones to give real-time traffic estimates. Artificial intelligence is becoming increasingly ubiquitous because of its versatility in analyzing and solving human problems; it follows, then, that a machine could learn how to teach humans skills and techniques. HelperBot is a platform fighting game project that employs this cutting-edge learning technology to close the skill gap between novice and veteran gamers as quickly and seamlessly as possible.
In order to train the model, data was collected from the NBA statistics website. The model was trained on games dating from the 2010 NBA season through the 2017 NBA season. Three separate models were built, predicting the winner, predicting the total points, and finally predicting the margin of victory for a team. These models learned on 80 percent of the data and validated on the other 20 percent. These models were trained for 40 epochs with a batch size of 15.
The model for predicting the winner achieved an accuracy of 65.61 percent, just slightly below the accuracy of other experts in the field of predicting the NBA. The model for predicting total points performed decently as well, it could beat Las Vegas’ prediction 50.04 percent of the time. The model for predicting margin of victory also did well, it beat Las Vegas 50.58 percent of the time.
From our research, we found that for as little as $5 a day, an independent artist can make effective introductions to audiences most likely to enjoy what they have to offer without compromising artistic expression, while also learning from and engaging with their growing audience.
This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.
This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.
This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.
This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.