Congratulations!
1. Congratulations!
Congratulations on making it to the end of the course!2. Review
In this course, we learned about various feature engineering techniques for natural language processing in python. We started off by computing basic features such as character length and word length of documents. We then moved on to readability scores and learned various metrics that could help us deduce the amount of education required to comprehend a piece of text fully. Next, we were introduced to the spacy library and learned to perform tokenization and lemmatization. Building on these techniques, we proceeded to explore text cleaning. We also learned how to perform part of speech tagging and named entity recognition using spacy models and had a sneak peek at their applications. The third chapter was dedicated to n-gram modeling. We also explored an application of it in sentiment analysis of movie reviews. The final chapter saw us covering tf-idf vectors and cosine similarity. Using these concepts, we built a movie and a TED Talk recommender. The final lesson gave you a sneak peek into word embeddings and their use cases.3. Further resources
This, by no means, is the end of the road. Once you're done with this course, it is highly recommended that you take the following courses, also offered by DataCamp to muscle up your skills further.4. Thank you!
We hope you have enjoyed taking this course as much as we did developing it. Thank you and all the best with your data science journey!Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.