Machine Learning: Natural Language Processing in Python (V2)
NLP: Use Markov Models, NLTK, Artificial Intelligence, Deep Learning, Machine Learning, and Data Science in Python
4.76 (6477 reviews)
24 241
students
22.5 hours
content
May 2025
last update
$94.99
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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Our Verdict
This 22.5-hour NLP deep dive, taught by a knowledgeable and engaging instructor, challenges learners with gradual yet rewarding content. Despite some redundancy and occasional access issues for certain notebooks, it provides theoretical and practical understanding to tackle real-life applications with confidence in machine learning and data science projects.
What We Liked
- Covers fundamental techniques of NLP with easy-to-follow applications and customizable advanced explanations
- Gradual challenge that pays off with substantial knowledge gain in NLP, machine learning, and deep learning
- Comprehensive course with extensive details for practical and theoretical students seeking to apply ML to text data
- Excellent engagement from the instructor, prompt Q&A response, and clear, efficient coding approach
Potential Drawbacks
- Notebooks occasionally require third-party email submission or manual creation—inconvenient for some learners
- Some slides repeat verbal explanations, causing redundancy and prolonging course length
- Instructor's tone in Q&A can be aggressive and off-putting to some students
- Access to certain advanced resources may depend on enrollment in other courses offered by the instructor
4294434
udemy ID
12/09/2021
course created date
20/12/2021
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