Machine Learning: Natural Language Processing in Python (V2) - NLP: Use Markov Models, NLTK, Artificial Intelligence, Deep Learning, Machine Learning, and Data Science in Python
I found a course on Udemy called "Machine Learning: Natural Language Processing in Python (Version 2)"1. This course covers vector models and text preprocessing methods, probability models and Markov models, machine learning methods, and deep learning and neural network methods. It is a massive 4-in-1 course that can help you learn more about natural language processing in Python.
There is also a book called “Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for NLP” by Taweh Beysolow II2. This book can help you learn how to utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim. You can also learn how to manipulate and preprocess raw text data in formats such as .txt and .pdf.
Finally, there is a tutorial on Towards AI called “Natural Language Processing (NLP) with Python — Tutorial for Beginners” by Srishti Chakraborty3. This tutorial covers the basics of natural language processing with Python. It includes exploring features of NLTK, opening the text file for processing, reading the file which we want to analyze, analyzing it, tokenizing the text into words and sentences, stemming the words, lemmatizing the words, removing stop words from the text, and more.
Tensorflow 2.0: Deep Learning and Artificial Intelligence
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)
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