LearnX uses natural language processing within machine learning to identify subjects and topics.
Lecture text can be pasted into the algorithm for analysis. A script that can download upto 10 lectures from MIT OCW website is also available.
The texts are transformed into a bag of words using Tf-idf vectorizer and fed to the classifier to predict the subject of each lecture.
Non-Negative Matrix Factorization (NMF) is used to extract topics from each lecture.Next
To train the algorithm we use wikipedia glossary pages. Following pages will be used by default. Add or remove pages as necessary.
Click LEARN when ready.
|4||probability and statistics|
|5||elementary quantum mechanics|
|7||gene expression terms|