The Certificate in NLP (Natural Language Processing) with Python exam evaluates a candidate's proficiency in using Python to implement NLP techniques. This certification covers fundamental NLP concepts, practical implementation of NLP algorithms, and the use of Python libraries such as NLTK, spaCy, and Transformers for processing and analyzing text data.
Skills Required
Python Programming: Proficiency in Python programming language.
Text Processing: Understanding of text preprocessing techniques.
NLP Algorithms: Knowledge of core NLP algorithms and concepts.
Machine Learning: Basic understanding of machine learning principles.
Data Analysis: Skills in analyzing and interpreting text data.
Libraries and Tools: Familiarity with NLP libraries and tools in Python.
Who should take the exam?
Data Scientists: Individuals working with text data and looking to enhance their NLP skills.
Machine Learning Engineers: Professionals implementing NLP algorithms in projects.
Software Developers: Developers interested in incorporating NLP into applications.
Linguists: Linguists looking to apply computational techniques to language data.
Students and Academics: Individuals studying NLP or related fields.
Researchers: Researchers focusing on text analysis and NLP applications.
Course Outline
The NLP With Python exam covers the following topics :-
Module 1: Introduction to Natural Language Processing
Overview of NLP and its applications
History and evolution of NLP
Key concepts and terminology in NLP
Module 2: Python for NLP
Introduction to Python programming
Python libraries for NLP: NLTK, spaCy, gensim, Transformers
Setting up the Python environment for NLP projects
Module 3: Text Preprocessing
Tokenization and sentence segmentation
Stopword removal and stemming
Lemmatization and part-of-speech tagging
Text normalization and cleaning
Module 4: NLP Algorithms and Techniques
Bag-of-words and TF-IDF
Word embeddings: Word2Vec, GloVe, FastText
Named Entity Recognition (NER)
Sentiment analysis
Module 5: Advanced NLP Techniques
Topic modeling: LDA and LSA
Text classification and clustering
Sequence models: RNN, LSTM, GRU
Transformer models: BERT, GPT
Module 6: Implementing NLP with Python
Building NLP pipelines with spaCy
Text classification with scikit-learn
Using pre-trained models with Transformers
Customizing NLP models for specific tasks
Module 7: NLP in Practice
Sentiment analysis in social media
Chatbot development
Machine translation
Text summarization
Module 8: Evaluation and Optimization
Evaluation metrics for NLP models
Hyperparameter tuning and model optimization
Cross-validation and model validation techniques
Module 9: Real-World NLP Applications
Case studies of NLP applications in various industries
Ethical considerations in NLP
Future trends in NLP and AI
Module 10: Capstone Project
Designing and implementing an end-to-end NLP project