Advanced NLP with Python for Machine Learning
Offered By: LinkedIn Learning
Course Description
Overview
Build upon your foundational knowledge of natural language processing (NLP) by exploring more complex topics such as word2vec, doc2vec, and recurrent neural networks.
Syllabus
Introduction
- Leveraging the power of messy text data
- What you should know
- What tools you need
- Using the exercise files
- What is NLP?
- NLTK setup
- Reading text data into Python
- Cleaning text data
- Vectorize text using TF-IDF
- Building a model on top of vectorized text
- What is word2vec?
- What makes word2vec powerful?
- How to implement word2vec
- How to prep word vectors for modeling
- What is doc2vec?
- What makes doc2vec powerful?
- How to implement doc2vec
- How to prep document vectors for modeling
- What is a neural network?
- What is a recurrent neural network?
- What makes RNNs so powerful for NLP problems?
- Preparing data for an RNN
- How to implement a basic RNN
- Prep the data for modeling
- Build a model on TF-IDF vectors
- Build a model on word2vec embeddings
- Build a model on doc2vec embeddings
- Build an RNN model
- Compare all methods using key performance metrics
- Key takeaways for advanced NLP modeling techniques
- How to continue advancing your skills
Taught by
Derek Jedamski
Related Courses
Interactive Word Embeddings using Word2Vec and PlotlyCoursera Project Network via Coursera Машинное обучение на больших данных
Higher School of Economics via Coursera Generating discrete sequences: language and music
Ural Federal University via edX Explore Deep Learning for Natural Language Processing
Salesforce via Trailhead 2024 Natural Language Processing in Python for Beginners
Udemy