Mastering NLP Techniques from Text Transcription to Insight Extraction

The Natural Language Processing (NLP) Techniques for Text Transcription and Insight Extraction course is designed for individuals who are interested in learning about the fundamental principles and techniques used in NLP for transcribing and extracting insights from various types of text data, including books, articles, and websites. This course is particularly suitable for individuals who are interested in data science, machine learning, and deep learning, as well as those who want to build practical NLP applications.

After taking the Natural Language Processing (NLP) Techniques for Text Transcription and Insight Extraction course, students will gain a solid understanding of the fundamental principles and techniques used in NLP for transcribing and extracting insights from various types of text data, including books, articles, and websites. Specifically, Students will learn the following:

• The basics of NLP and deep learning, including popular Python NLP libraries.
• Text processing fundamentals, including techniques for tokenization, stemming, and lemmatization.
• Machine learning methods in sentiment analysis, including supervised and unsupervised learning, as well as techniques for feature extraction and model selection.
• Voice user interface techniques for speech-to-text transcription.
• Practical experience with implementing NLP techniques for text transcription and insight extraction.

• Basic Python 
• Understanding of Machine Learning Algorithms
• Basic Understanding NLP

Introduction to NLP and Deep Learning

• What is NLP and why is it important?
• Introduction to deep learning and neural networks
• Popular Python NLP libraries: NLTK, spaCy, and Gensim
• Overview of text preprocessing techniques

Text Processing Fundamentals

• Tokenization and segmentation
• Stop word removal
• Stemming and lemmatization
• Part-of-speech tagging

 Sentiment Analysis

• Introduction to sentiment analysis
• Feature extraction techniques
• Supervised learning methods: Naive Bayes, logistic regression, and support vector machines
• Unsupervised learning methods: Lexicon-based approaches and clustering
• Model selection and evaluation

Voice User Interface Techniques for Speech-to-Text Transcription

• Introduction to voice user interfaces (VUIs)
• Speech recognition techniques: Hidden Markov Models and Deep Neural Networks
• Text normalization and correction
• Implementation of a simple VUI system

 Advanced Topics in NLP

• Named Entity Recognition (NER)
• Topic modeling
• Text summarization
• Applications of NLP in industry and research