2023 Natural Language Processing in Python for Beginners
Text Cleaning, Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, LSTM for Sentiment, Emotion, Spam & CV Parsing
What you'll 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.
