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Machine Learning: Natural Language Processing in Python (V2)

Machine Learning: Natural Language Processing in Python (V2)

Hello friends! Welcome to Machine Learning: Natural Language Processing in Python (Version 2). This is a massive 4-in-1 course covering:

What you'll learn

  • How to convert text into vectors using CountVectorizer, TF-IDF, word2vec, and GloVe
  • How to implement a document retrieval system / search engine / similarity search / vector similarity
  • Probability models, language models and Markov models (prerequisite for Transformers, BERT, and GPT-3)
  • How to implement a cipher decryption algorithm using genetic algorithms and language modeling
  • How to implement spam detection
  • How to implement sentiment analysis
  • How to implement an article spinner
  • How to implement text summarization
  • How to implement latent semantic indexing
  • How to implement topic modeling with LDA, NMF, and SVD
  • Machine learning (Naive Bayes, Logistic Regression, PCA, SVD, Latent Dirichlet Allocation)
  • Deep learning (ANNs, CNNs, RNNs, LSTM, GRU) (more important prerequisites for BERT and GPT-3)
  • Hugging Face Transformers (VIP only)
  • How to use Python, Scikit-Learn, Tensorflow, +More for NLP
  • Text preprocessing, tokenization, stopwords, lemmatization, and stemming
  • Parts-of-speech (POS) tagging and named entity recognition (NER)

Welcome to Machine Learning: Natural Language Processing in Python (Version 2).

This is a massive 4-in-1 course covering:

1) Vector models and text preprocessing methods

2) Probability models and Markov models

3) Machine learning methods

4) Deep learning and neural network methods

In part 1, which covers vector models and text preprocessing methods, you will learn about why vectors are so essential in data science and artificial intelligence. You will learn about various techniques for converting text into vectors, such as the CountVectorizer and TF-IDF, and you'll learn the basics of neural embedding methods like word2vec, and GloVe.

You'll then apply what you learned for various tasks, such as:

  • Text classification
  • Document retrieval / search engine
  • Text summarization

Along the way, you'll also learn important text preprocessing steps, such as tokenization, stemming, and lemmatization.

You'll be introduced briefly to classic NLP tasks such as parts-of-speech tagging.

In part 2, which covers probability models and Markov models, you'll learn about one of the most important models in all of data science and machine learning in the past 100 years. It has been applied in many areas in addition to NLP, such as finance, bioinformatics, and reinforcement learning.

Online Course CoupoNED based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.