Machine Learning: Natural Language Processing in Python (V2) Course
NLP: Use Markov Models, NLTK, Artificial Intelligence, Deep Learning, Machine Learning, and Data Science in Python
Welcome to Machine Learning: Natural Language Processing in Python (Version 2). This is a massive 4-in-1 course covering: Vector models and text preprocessing methods, Probability models and Markov models, Machine learning methods, Deep learning and neural network methods.
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
- 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 tagging and named entity recognition.
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