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Natural Language Processing Training

$149.00

This course will provide an introduction to the field of Natural Language Processing (NLP). The goal is to familiarize students with the fundamentals of NLP and its various applications.

Natural Language Processing

Course Overview:

This course will provide an introduction to the field of Natural Language Processing (NLP). The goal is to familiarize students with the fundamentals of NLP and its various applications.

Topics covered include language structure, language models, text classification, sentiment analysis, machine translation, information extraction and natural language understanding.

Students will gain practical experience in building and working with NLP systems through hands-on projects. By the end of this course, students should have a solid foundation in the fundamentals of Natural Language Processing and be able to apply them in a range of contexts.

Prerequisites:

This course assumes that students are comfortable coding in Python or any other programming language as well as have basic knowledge about Linear Algebra and Probability Theory.

 


Learning Objectives:

– Understand the fundamentals of natural language processing, including language structure, models, and algorithms

– Learn how to apply NLP techniques in a range of applications such as text classification, sentiment analysis, machine translation, information extraction and natural language understanding

– Gain practical experience in building and working with NLP systems through hands-on projects

– Develop skills for independently researching new topics in the field of Natural Language Processing.

 


Curriculum:

Module 1 – Introduction to Natural Language Processing: Overview of language structure, models and algorithms used in NLP.

Module 2 – Text Classification: Identify the topic or intent of text using supervised methods.

Module 3 – Sentiment Analysis: Analyzing emotion and opinion in text using unsupervised learning techniques.

Module 4 – Machine Translation: Automatically translating between languages with neural networks and other deep learning approaches.

Module 5 – Information Extraction & Natural Language Understanding: Extracting meaning from text for an improved user experience.

Module 6 – Practical Projects: Hands-on projects in the fields of sentiment analysis, machine translation, information extraction, natural language understanding and more.

Module 7 – Advanced Topics & Research: Exploring the latest research in NLP and its applications.


 

Practical Assessment:

At the end of this course, students will be assessed through practical projects. Projects may include sentiment analysis, machine translation, information extraction and natural language understanding tasks in various domains such as finance, healthcare and e-commerce. Students will also be tested on their knowledge gained during the course and their ability to independently research new topics in the field of NLP.

By the end of this course, students should have a solid understanding of natural language processing and be able to apply it to their chosen domain. They should also be able to independently research new topics in the field and create innovative solutions for real-world problems.

 


Glossary:

Natural Language Processing (NLP): The study of computational techniques for understanding and manipulating human language.

Language Structure: The underlying rules that determine the structure of a language.

Language Model: A probabilistic model used to generate text by predicting the next word in a sequence based on previous words.

Text Classification: Using machine learning algorithms to classify text into different categories.

Sentiment Analysis: The process of automatically determining if an expression is positive, negative or neutral.

Machine Translation: The translation of one natural language into another using computer algorithms.

Information Extraction: Extracting structured information from unstructured sources such as text documents, web pages and emails.

Natural Language Understanding: The process of automatically understanding the meaning of text or speech.

Supervised Methods: Using labeled training data for a machine learning algorithm to learn from.

Unsupervised Learning: Extracting structure from unlabeled datasets.

Neural Networks & Deep Learning: Techniques used to build artificial neural networks that can learn complex tasks from large amounts of data.

Artificial intelligence: A field of computer science focused on building machines capable of intelligent behavior.

Computational Linguistics: The study of language from a computational perspective, using methods such as natural language processing and machine learning.

Analytics: A process for extracting insights from data by applying statistical techniques or algorithms.

Syntax: The structure of a language that defines how words are combined to form valid sentences.

Semantic: The meaning of a sentence or phrase in a given context.

Part-of-speech tagging: Assigning part-of-speech labels (e.g., nouns, verbs, adjectives) to words in sentences.

Summarization: Automatically creating concise summaries of longer texts.

NLTK: The Natural Language Toolkit, a suite of open-source Python libraries for natural language processing.

Natural Language Generation: Automatically generating human-like text from structured data.

Learning Models: Algorithms used to learn from data and make predictions about unseen data points.

NLPs Tasks: Specific tasks in natural language processing such as sentiment analysis, machine translation or information extraction.

Natural language processing algorithms: Algorithms that are used to analyze and process natural language data. Examples include part-of-speech tagging, sentence segmentation, parsing and semantic analysis.

Deep learning models: Artificial neural networks that are trained on large datasets to learn complex tasks such as image recognition or speech recognition.

NLP Applications: Software applications that use NLP algorithms for specific tasks such as information extraction or machine translation.

Speech Recognition: The process of automatically recognizing spoken words using computer algorithms.

Named Entity Recognition: A task in natural language processing to identify and classify entities mentioned in text into pre-defined categories such as people, locations or organizations.

Statistical Methods: Techniques used to infer knowledge from data by making assumptions about probability distributions and relationships between variables.

NLP Models: A set of algorithms designed to process natural language data. Examples include part-of-speech tagging, parsing, semantic analysis and machine translation.

Text Processing: The process of extracting meaningful information from raw text using techniques such as text normalization or filtering.

Deep Learning Neural Networks: Artificial neural networks that are trained on large datasets to learn complex tasks such as image recognition or speech recognition.

Word Processing: The manipulation of words to create new words or phrases with specific meaning.

Data Processing: The manipulation of data to produce meaningful information. This includes tasks such as sorting, cleaning and merging datasets.