Code Logic

Hey guys, this is sarvesh again, I hope you enjoyed the recent episodes and as promised, this is another episode for collocation's. These techniques are generally and most commonly used for finding if the words are collocations or not. The generally principle behind this is hypothesis testing. Our null hypothesis being, assumption that the words are not collocations. To find out mo

Hey guys, sorry for the long break. I was working from home and its difficult to record audio at home, anyways I have started the podcast back, I think we will be discussing about word embeddings and other embeddings later because we have a lot to learn before that. About this episode, this episode focuses about collocations, what comprises of collocations and two methods for collo

Hey guys welcome to another episode for word embeddings! In this episode we talk about another popularly used word embedding technique that is known as word2vec. We use word2vec to grab the contextual meaning in our vector representation. I've found this useful reading for word2vec. Do read it for an in depth explanation. p.s. Sorry for always posting episode after a significant de

In this podcast episode we discuss about why word embeddings are required, what are they and we also discuss about one hot encodings. In next episode we will talk about specific techniques for word embeddings individually. Stay tuned. Sponsored by www.stacklearn.org --- Send in a voice message: https://podcasters.spotify.com/pod/show/sarvesh-bhatnagar/message

In this podcast episode we will talk about TF-IDF model in Natural Language Processing. TF-IDF model stands for term frequency inverse document frequency. We use TF-IDF model to give more weight to important words as compared with common words like the, a, in, there, where, etc. To learn python programming visit www.stacklearn.org. See you in the next podcast episode! --- Send in a

In this podcast episode we talk about bag of words model in natural language processing. Bag of Words model is simply a feature extraction method used in NLP. We mainly discuss about why bag of words model is required and what it is. In summary BOW is simply a set of tuples with words along with their frequency pairs. To learn more about BOW : visit this Gensim Introduction : visit

In this episode we review preprocessing steps such as making text lowercase, removing unwanted characters and other related cleaning tasks which we have discussed in the previous videos... we also talk about Gensim package in python and how it simplifies preprocessing in Natural language processing --- Send in a voice message: https://podcasters.spotify.com/pod/show/sarvesh-bhatnag

In this podcast episode we will be talking about Lemmatization in natural language processing. It is a text normalization step which we need to perform to normalize words. Lemmatization improves on shortcomings of Stemming in Natural Language Processing and In this podcast episode we talk about that shortcoming and also how we can use lemmatization using nltk library. Learn Python:

In this podcast episode we will be talking about stemming in natural language processing. It is a text normalization step which we need to perform to normalize words such that run, runs and running counts the same... stemming involves chopping off affixes such as ing, ly, etc. --- Send in a voice message: https://podcasters.spotify.com/pod/show/sarvesh-bhatnagar/message

In this episode we discuss about tokenization in Natural Language Processing. As discussed in previous episode, tokenisation is an important step in data cleaning and it entails dividing a large piece of text into smaller chunks. In this episode we discuss some of the basic tokenizers available from nltk.tokenize in nltk. If you liked this episode, do follow and do connect with me