Machine Learning: Natural Language Processing in Python (V2)
NLP: Use Markov Models, NLTK, Artificial Intelligence, Deep Learning, Machine Learning, and Data Science in Python
4.75 (6395 reviews)
23,811
students
22.5 hours
content
Apr 2025
last update
$99.99
regular price
What you will 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)
Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion
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udemy ID
9/12/2021
course created date
12/20/2021
course indexed date
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