Introduction to Natural Language Processing in Python [2024]

Description

Natural Language Processing (NLP) is a rapidly evolving field at the intersection of linguistics, computer science, and artificial intelligence. This course provides a comprehensive introduction to NLP using the Python programming language, covering fundamental concepts, techniques, and tools for analyzing and processing human language data.

Throughout the course, students will learn how to leverage Python libraries such as NLTK (Natural Language Toolkit), spaCy, and scikit-learn to perform various NLP tasks, including tokenization, stemming, lemmatization, part-of-speech tagging, named entity recognition, sentiment analysis, text classification, and language modeling.

The course begins with an overview of basic NLP concepts and techniques, including text preprocessing, feature extraction, and vectorization. Students will learn how to clean and preprocess text data, convert text into numerical representations suitable for machine learning models, and visualize textual data using techniques such as word clouds and frequency distributions.

Next, the course covers more advanced topics in NLP, including syntactic and semantic analysis, grammar parsing, and word embeddings. Students will explore techniques for analyzing the structure and meaning of sentences and documents, including dependency parsing, constituency parsing, and semantic role labeling.

The course also introduces students to practical applications of NLP in various domains, such as information retrieval, question answering, machine translation, and chatbot development. Students will learn how to build and evaluate NLP models using real-world datasets and evaluate their performance using appropriate metrics and techniques.

By the end of the course, students will have a solid understanding of the fundamental principles and techniques of NLP and the ability to apply them to solve real-world problems using Python. Whether you are a beginner or an experienced Python programmer, this course will provide you with the knowledge and skills you need to start working with natural language data and build intelligent NLP applications.

Course Outline:

1. Introduction

   • Course strucure

   • How to make out of this course

   • Overview of natural language processing

2. Text pre-processing

   • Tokenization techniques (word-level, sentence-level) and its implementation

   • Regular expression and its implementation

   • Treebank tokenizer and its implementation

   • TweetTokenizer and its implementation

   • Stemming and its implementation

   • WordNet Lemmatizer and its implementation

   • spacy Lemmatizer and its implementation

   • The introduction and implementation of stop word removal

   • The introduction and implementation of Case folding

   • Introduction and implementation of N-grams

3. Text Representation

   • Introduction to Word2vec and implementation

   • skip-gram implementation

   • Bag of word implementation

4. How to perform basic feature extraction methods

   • What are types of data

   • Text cleaning and tokenization practice.

   • How to perform text tokenization using keras and TextBlob

   • Singularizing and pluralizing words and language translation

   • What does feature extraction mean in natural language processing

   • Implementation of  feature extraction in natural language processing.

   • Introduction to Zipf's Law and implementation

   • Introduction to TF-IDF and implementation

   • feature engineering

   • Introduction to WordCloud and its implementation

5. spaCy overview and implementation

   • Introduction to spaCy

   • Tokenization Implementation

   • lemmatization Implementation

6. Text Classifier Implementation

   • Introduction to Machine learning

   • Introduction to Hierarchical Clustering and implementation

   • introduction to K-means Clustering and implementation

   • Introduction to Text Classification and implementation

   • introduction to tree methods and implementation

   • introduction to Removing Correlated Features and implementation

   • introduction to Dimensionality Reduction and implementation

Mode of Instruction:

• The course will be delivered through a combination of lectures, demonstrations, hands-on exercises, and project work.

• Students will have access to online resources, including lecture slides, code examples, and additional reading materials.

• Instructor-led sessions will be supplemented with self-paced learning modules and group discussions.

certification:

• Upon successful completion of the course, students will receive a certificate of completion, indicating their proficiency in natural language processing with Python.

Join us on a journey into the fascinating world of natural language processing and discover the endless possibilities for building intelligent applications that can understand and interact with human language data. Enroll now and take the first step towards mastering the art of NLP with Python!

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