Natural Language Processing Tutorial | How To Ace Text Classification & Similarity Tasks | Part -2

This video explains the concepts of building Machine Learning models to classify a textual dataset. It covers supervised ML algorithms and clustering for binary classification. Next, it explains the concept of Text Similarity with examples of how similarity between texts can be found using Word Embedding methods like Word2Vec from the Gensim library. The video evaluates the results of various word embedding algorithms (fine-tuning pre-trained models and building models from scratch) and demonstrates how applying transfer learning on pre-trained models like Transformer yields better results. Stay tuned to the Part-3 video on building a system that performs Question Answering and Next Word Prediction using fine-tuned pre-trained models and deploying the same using FAST API.

#nlp #nlponlinetraining #ml #machinelearning #naturallanguageprocessing

[saaraanand]

Interesting video! Waiting for Part 3💯

[baruta]

very well explained...Waiting for part 3 ✨✨💫💫

[nishitverma]

Really interesting…!! Eagerly looking forward to part 3

[lalithyamanasapatri]

Awesome tutorial ! Clearly Explained 💯💯

[LaveshNK]

NLTK is a tough library to use. well made tutorial!