Machine Learning: Natural Language Processing in Python (V2)

Machine Learning: Natural Language Processing in Python (V2) - NLP: Use Markov Models, NLTK, Artificial Intelligence, Deep Learning, Machine Learning, and Data Science in Python

Preview this Course

GET COUPON CODE

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)
• Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion