About this course
Build upon your foundational knowledge of natural language processing (NLP) by exploring more complex topics such as word2vec, doc2vec, and recurrent neural networks.
Leveraging the power of messy text data
1m 2sWhat you should know
1m 31sWhat tools you need
1m 46sUsing the exercise files
1m 29sWhat is NLP?
2m 27sNLTK setup
4m 55sReading text data into Python
5m 37sCleaning text data
8m 11sVectorize text using TF-IDF
6m 45sBuilding a model on top of vectorized text
7m 14sWhat is word2vec?
4m 14sWhat makes word2vec powerful?
3m 36sHow to implement word2vec
8m 7sHow to prep word vectors for modeling
7m 27sWhat is doc2vec?
1m 55sWhat makes doc2vec powerful?
1m 32sHow to implement doc2vec
4m 24sHow to prep document vectors for modeling
2m 54sWhat is a neural network?
2m 39sWhat is a recurrent neural network?
1m 49sWhat makes RNNs so powerful for NLP problems?
3m 27sPreparing data for an RNN
5m 53sHow to implement a basic RNN
11m 39sPrep the data for modeling
2m 52sBuild a model on TF-IDF vectors
6m 34sBuild a model on word2vec embeddings
6m 41sBuild a model on doc2vec embeddings
3m 59sBuild an RNN model
5m 11sCompare all methods using key performance metrics
4m 16sKey takeaways for advanced NLP modeling techniques
3m 6sHow to continue advancing your skills
1m 22s