Natural Language
Processing
with NLTK
Overview
Course Objective
By the end of this course:
• You will understand what natural language processing is
• You will understand all of the required processes for text processing in NLTK
• You will be familiar with the most common text corpus (datasets) in the field
• You understand advanced concept in the field like bag of words and TF-IDF
• You will learn how to use machine learning algorithms in context of NLP
Who Should Attend
This course is ideal for individuals who are interested in working with text data and wish to explore the field of Natural Language Processing (NLP). It is suitable for:
Data scientists and analysts who want to gain hands-on experience with NLP techniques.
Machine learning enthusiasts who want to apply NLP methods to real-world data.
Developers who are looking to expand their skill set to include text data processing and analysis.
Beginners in the field of data science who wish to dive into NLP with Python.
Students and professionals who want to learn how to build and implement NLP solutions for various text-based tasks such as sentiment analysis, text classification, and more.
Prerequisites
A basic understanding of Python programming, as the course will involve coding in Python.
Familiarity with fundamental data structures in Python, such as lists, dictionaries, and loops.
A basic understanding of statistics or machine learning concepts will be helpful but not mandatory.
No prior experience with NLP is required, as this course is designed for beginners. However, some experience with data analysis or Python libraries (such as Pandas or NumPy) may be advantageous.
Training Calendar
Intake
Duration
Program Fees
Module
Module 1 - Get started on NLP
• What is NLP?
• NLP Frameworks
• Basic Text Analysis With Python
Module 2 - Regular expressions
• Introduction to Regular Expressions
• Key Functions of Regular Expression
Module 3 - Text Analysis with NLTK’
• What is NLTK?
• Install NLTK
• Tokenize Words and Sentences
• Stop Words
• Stemming
• Lemmatizing
• Part-of-Speech (POS) Tagging
• Chucking
Module 4 - NLTK Corpus
• What is Corpus?
• Popular NLTK Corpus
• Your Own Corpus
• Frequency Distribution
Module 5 - Text Classification
• Getting the Data for Text Classi_cation
• Importing the Dataset
• Preprocessing the Data
• Transforming Data Into BOW Model
• Transform BOW Model Into TF-IDF Model
• Creating Training and Test Set
• Training Our Classifier
• Practical Model Performance
• Saving Model
• Importing and Using Model
Module 6 - Word Embedding
• What is Word Embedding
• Word2Vec & Glove
• CBOW and Skip-gram Models
• Word2Vec Training with Gensim
• Pre-trained Word2Vec Model
Module 7 - Twitter Sentiments Analysis
• Tweepy Module
• Twitter API
• Setting up Twitter Application
• Initializing Tokens
• Client Authentication
• Fetching Real Time Tweets
Module 8 - Practical project
FAQs
General Questions:
Q: What is the Natural Language Processing with NLTK course about?
This 3-day course introduces participants to the fundamentals of Natural Language Processing (NLP) using the NLTK library. It covers text data cleaning and preprocessing, various NLP techniques such as tokenization, stemming, lemmatization, part-of-speech tagging, and text classification. The course also explores advanced concepts like word embeddings, Twitter sentiment analysis, and includes a practical project to apply the learned skills.
Q: Who should attend this course?
Data scientists and analysts interested in working with text data
Machine learning enthusiasts eager to apply NLP techniques to real-world datasets
Developers looking to expand their skills in text data processing and analysis
Beginners in data science who want to learn NLP with Python
Students and professionals seeking to build NLP solutions for tasks like sentiment analysis and text classification
Q: What are the prerequisites for this course?
Basic understanding of Python programming
Familiarity with fundamental data structures in Python (e.g., lists, dictionaries)
Basic understanding of statistics or machine learning concepts (helpful, but not mandatory)
No prior experience with NLP is required, although familiarity with data analysis or Python libraries (e.g., Pandas, NumPy) is beneficial
Q: How long is the course?
The course spans 3 days.
Q: What key topics are covered in this course?
Module 1: Getting Started on NLP
Module 2: Regular Expressions
Module 3: Text Analysis with NLTK
Module 4: NLTK Corpus
Module 5: Text Classification
Module 6: Word Embedding
Module 7: Twitter Sentiment Analysis
Module 8: Practical Project
Q: Will I receive a certification after completing the course?
Yes, participants will receive a certificate of completion, recognizing their skills in Natural Language Processing with NLTK.
Program Content & Skills:
Q: What foundational Natural Language Processing concepts will I learn in this course?
You’ll explore the core principles of Natural Language Processing (NLP), focusing on text data cleaning and preprocessing. Key topics include tokenization, stemming, lemmatization, part-of-speech tagging, and advanced techniques like bag of words and TF-IDF. You’ll also gain a deep understanding of text classification, word embeddings, and how to work with popular text corpora using NLTK.
Q: How does the course help me apply NLP to real-world text data problems?
You’ll work with real-world text datasets to apply various NLP techniques, including sentiment analysis, text classification, and word embedding models. This hands-on approach will help you understand how to solve practical problems in areas such as social media analysis, customer feedback processing, and information retrieval.
Q: What skills will I develop in managing and optimizing NLP models?
You’ll learn how to process and clean text data, apply machine learning algorithms for text classification, and optimize models for better performance. Additionally, you’ll gain expertise in techniques like transforming text data into bag of words and TF-IDF models, training and evaluating NLP classifiers, and using pre-trained word embeddings for various applications.
Q: Will I learn how to handle different types of text data in NLP?
Yes. The course covers working with various types of text data, such as social media posts, articles, and tweets. You’ll learn data preprocessing techniques like tokenization, removing stop words, and normalizing text (stemming and lemmatization), along with handling noisy data, missing values, and text data formatting challenges.
Q: How does this course prepare me for applying NLP in a professional context?
You’ll gain the technical knowledge and practical skills needed to integrate NLP into real-world applications, such as automated customer support, sentiment analysis, content recommendation systems, and social media monitoring. By completing a hands-on project, you’ll be equipped to tackle NLP challenges in industries like marketing, healthcare, finance, and more.
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