2025 Natural Language Processing (NLP) Mastery in Python
Text Cleaning, Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, LSTM for Sentiment, Emotion, Spam, CV Parsing
4.58 (1033 reviews)
20 860
students
38.5 hours
content
May 2025
last update
$74.99
regular price
What you will learn
Learn complete text processing with Python
Learn how to extract text from PDF files
Use Regular Expressions for search in text
Use SpaCy and NLTK to extract complete text features from raw text
Use Latent Dirichlet Allocation for Topic Modelling
Use Scikit-Learn and Deep Learning for Text Classification
Learn Multi-Class and Multi-Label Text Classification
Use Spacy and NLTK for Sentiment Analysis
Understand and Build word2vec and GloVe based ML models
Use Gensim to obtain pretrained word vectors and compute similarities and analogies
Learn Text Summarization and Text Generation using LSTM and GRU
Understand the basic concepts and techniques of natural language processing and their applications.
Learn how to use Python and its popular libraries such as NLTK and spaCy to perform common NLP tasks.
Be able to tokenize and stem text data using Python.
Understand and apply common NLP techniques such as sentiment analysis, text classification, and named entity recognition.
Learn how to apply NLP techniques to real-world problems and projects.
Understand the concept of topic modeling and implement it using Python.
Learn the basics of text summarization and its implementation using Python.
Understand the concept of text generation and implement it using Python
Understand the concept of text-to-speech and speech-to-text conversion and implement them using Python.
Learn how to use deep learning techniques for NLP such as RNN, LSTM, and word embedding.
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Our Verdict
The 2025 Natural Language Processing (NLP) Mastery in Python course offers an extensive exploration into NLP, text processing, and deep learning for language analysis. However, it's essential to allocate extra time for independent research and troubleshoot deprecated code modules. With engaging projects and comprehensive content, aspiring NLP practitioners can establish a solid foundation upon which they can further refine their skills.
What We Liked
- Comprehensive coverage of Natural Language Processing (NLP) concepts in Python with libraries like NLTK, SpaCy, Scikit-Learn, and deep learning frameworks
- In-depth exploration of text processing, feature extraction, sentiment analysis, emotion detection, spam filtering, and CV parsing
- Hands-on experience with text summarization, text generation using LSTM & GRU, topic modeling, and word embedding techniques
- Well-organized course structure, clear video lectures, useful reading materials, quizzes, and challenging real-world projects
Potential Drawbacks
- Some explanations lack depth, requiring external research (e.g., using Google or ChatGPT) for better understanding
- Training models with small datasets may lead to poor accuracy and raises concerns about practical applications in professional settings
- Outdated course materials with deprecated code snippets make it difficult to follow along in certain sections, causing frustration for some learners
- Jupyter Notebooks as the sole source of documentation can be limiting. Additional PDFs or PPTs with command references and intuitions would enhance learning
Related Topics
3257648
udemy ID
21/06/2020
course created date
17/07/2020
course indexed date
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