What you will learn
- Have a deep understanding of NLP principles and applications
- Preprocess and tokenize textual data for NLP tasks
- Represent text as numerical vectors using various techniques
- Develop language models and generate text using advanced techniques
- Understand and implement sequence-to-sequence models for NLP tasks
- Explore advanced NLP topics such as coreference resolution and question answering
- Stay informed about emerging trends and challenges in NLP research
Beneficial for
- Data Scientists
- Machine Learning Engineers
- Natural Language Processing Engineers
- Software Developers interested in NLP
Course Pre-requisite
- Basic knowledge of machine learning concepts
- Familiarity with programming (preferably Python)
- Enthusiasm for working with natural language data is key.
Course Outline
Understanding the foundations of Natural Language Processing
Overview of NLP applications and real-world use cases
Challenges and opportunities in processing human language
Cleaning and pre processing textual data for NLP
Tokenization techniques for breaking text into meaningful units
Handling special cases like stemming and lemmatization
Understanding the grammatical structure of sentences
Implementing Part-of-Speech tagging algorithms
Practical applications of Part-of-Speech tagging in NLP
Identifying and classifying named entities in text
Implementing NER algorithms and models
NER applications in information extraction and analysis
Representing textual data as numerical vectors
Bag-of-Words and TF-IDF vectorization techniques
Word embeddings (e.g., Word2Vec, GloVe) for semantic representation
Understanding the basics of text classification
Implementing supervised learning algorithms for text classification
Evaluating and fine-tuning text classification models
Analysing sentiments in textual data
Implementing sentiment analysis algorithms
Applications of sentiment analysis in business and social media
Building language models for predicting and generating text
N-gram models, Markov models, and neural language models
Text generation techniques and applications
Understanding sequence-to-sequence models in NLP
Implementing models for machine translation and summarization
Applications of sequence-to-sequence models in NLP
Coreference resolution and discourse analysis
Question answering systems and information retrieval
Emerging trends and challenges in NLP research
