This blog discusses the detailed explanation of the end-to-end creation and deployment of a spelling error correction system which can be implemented using any text file as a dataset and achieved a BLEU score of 0.8 on the test dataset.
Spelling error correction is the task of automatically correcting spelling errors in text; e.g. [I followed his advice -> I followed his advice]. It can be used not only to help language learners improve their writing errors, but also alert native speakers to accidental mistakes…
This blog explores detailed explanations for the implementation of the music recommendation system which is implemented on a dataset of KKBox’s Music Recommendation Challenge with a Kaggle score of 65(highest=74)
The music Recommendation system is all about recommending the songs which the user likes based on their previous activities like searches, previously heard songs, etc. There are many methods for building a recommendation system like Collaborative Filtering, Content-Based Filtering, Hybrid methods (Combining both Content-Based and Collaborative Filtering methods).
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