NLP – Natural Language Processing with Python

NLP: Use Markov Models, NLTK, Artificial Intelligence, Deep Learning, Machine Learning, and Data Science in Python

Created by: Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Created by: Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer

What Will I 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

Requirements

• Install Python, it's free!
• Decent Python programming skills
• Optional: If you want to understand the math parts, linear algebra and probability are helpful

Target audience

• Anyone who wants to learn natural language processing (NLP)
• Anyone interested in artificial intelligence, machine learning, deep learning, or data science
• Anyone who wants to go beyond typical beginner-only courses on Udemy