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Build A Board Game Predictor Using Machine Learning
Machine Learning is slowly spreading its tentacles into all aspects of technology and even further. Better algorithms are helping devices become smarter and users to become more informed. Now, its time to add a little fun to Machine Learning and in this course, we have tried to do exactly that!
Board games have been a great way to pass the time, from simple ones like The Game of Life to more complicated ones like Dungeons & Dragons. But before you become vested in a Game, what if you could find out how popular it really is? What if it was rated to help you learn how fun it really is? Well, now using Machine Learning you can!
This is short and concise Machine Learning course, we will focus on predicting the reviews of over 80,000 different board games. This project based course is a fun way for you to master two important Machine Learning algorithms that can be applied on a much grander scale to other data sets - Linear Regression Model and a Random Forest Regressor.
However, for this project course we will focus on board games. The information that we will work on was scrubbed from a database of 80,000 board games and includes information such as minimum players, maximum players, minimum playtime, maximum playtime, etc. We will use the models to ensure that using all of this information, it gives an accurate prediction regarding the reviews.
In this course, we will walk you through all of the steps needed to generate this output including how to train the models, how to load and preprocess the dataset appropriately, and so much more. At the end of this course, you will not only have an accurate prediction of the board game reviews, but also hands-on experience to learn how you can actually train two significant Machine Learning algorithms to learn and sort data, as well as make accurate predictions using a data set.
Heres what you will learn in this course:
Master two important Machine Learning algorithms in this EPIC project-based course! Enroll now and get started!