Advanced NLP with Python for Machine Learning - DeepNeuron

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Course Content

Total learning: 28 lessons Time: 10 weeks

1. Introduction 0/3
- Lecture1.1
  
  What you should know
- Lecture1.2
  
  What tools you need
- Lecture1.3
  
  Using the exercise files
2. Review NLP Basics 0/6
- Lecture2.1
  
  What is NLP?
- Lecture2.2
  
  NLTK setup
- Lecture2.3
  
  Reading text data into Python
- Lecture2.4
  
  Cleaning text data
- Lecture2.5
  
  Vectorize text using TF-IDF
- Lecture2.6
  
  Building a model on top of vectorized text
3. word2vec 0/4
- Lecture3.1
  
  What is word2vec?
- Lecture3.2
  
  What makes word2vec powerful?
- Lecture3.3
  
  How to implement word2vec
- Lecture3.4
  
  How to prep word vectors for modeling
4. doc2vec 0/4
- Lecture4.1
  
  What is doc2vec?
- Lecture4.2
  
  What makes doc2vec powerful?
- Lecture4.3
  
  How to implement doc2vec
- Lecture4.4
  
  How to prep document vectors for modeling
5. Recurrent Neural Networks 0/4
- Lecture5.1
  
  What is a recurrent neural network?
- Lecture5.2
  
  What makes RNNs so powerful for NLP problems?
- Lecture5.3
  
  Preparing data for an RNN
- Lecture5.4
  
  How to implement a basic RNN
6. Compare Advance NLP Techniques on an ML Problem 0/7
- Lecture6.1
  
  Prep the data for modeling
- Lecture6.2
  
  Build a model on TF-IDF vectors
- Lecture6.3
  
  Build a model on word2vec embeddings
- Lecture6.4
  
  Build a model on doc2vec embeddings
- Lecture6.5
  
  Build an RNN model
- Lecture6.6
  
  Compare all methods using key performance metrics
- Lecture6.7
  
  Key takeaways for advanced NLP modeling techniques

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