For the next step, I would recommend focusing on Named Entity Recognition (NER) as the next important NLP concept. NER is a core task in NLP that involves identifying and classifying entities (like names of people, organizations, locations, dates, etc.) in a text. This concept is vital for many real-world applications, such as information extraction, document categorization, and search engines. One of the most efficient and widely used Python libraries for NER is spaCy !
For the next step, I would recommend focusing on Named Entity Recognition (NER) as the next important NLP concept. NER is a core task in NLP that involves identifying and classifying entities (like names of people, organizations, locations, dates, etc.) in a text. This concept is vital for many real-world applications, such as information extraction, document categorization, and search engines. One of the most efficient and widely used Python libraries for NER is spaCy !
good to hear from you ! 😊
For the next step, I would recommend focusing on Named Entity Recognition (NER) as the next important NLP concept. NER is a core task in NLP that involves identifying and classifying entities (like names of people, organizations, locations, dates, etc.) in a text. This concept is vital for many real-world applications, such as information extraction, document categorization, and search engines. One of the most efficient and widely used Python libraries for NER is spaCy !
Thanks a lot for recommendation. Will definitely consider this for the next videos
Long time no seen naruto ... 🥷
For the next step, I would recommend focusing on Named Entity Recognition (NER) as the next important NLP concept. NER is a core task in NLP that involves identifying and classifying entities (like names of people, organizations, locations, dates, etc.) in a text. This concept is vital for many real-world applications, such as information extraction, document categorization, and search engines. One of the most efficient and widely used Python libraries for NER is spaCy !