Natural Language Processing Fundamentals

Natural Language Processing Fundamentals, available at $39.99, has an average rating of 4.55, with 48 lectures, 8 quizzes, based on 37 reviews, and has 164 subscribers.

You will learn about Obtain, verify, and clean data before transforming it into a correct format for use Perform data analysis and machine learning tasks using Python Understand the basics of computational linguistics Build models for general natural language processing tasks Evaluate the performance of a model with the right metrics Visualize, quantify, and perform exploratory analysis from any text data This course is ideal for individuals who are Natural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product. It is particularly useful for Natural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product.

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Summary

Title: Natural Language Processing Fundamentals

Price: $39.99

Average Rating: 4.55

Number of Lectures: 48

Number of Quizzes: 8

Number of Published Lectures: 48

Number of Published Quizzes: 8

Number of Curriculum Items: 56

Number of Published Curriculum Objects: 56

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Obtain, verify, and clean data before transforming it into a correct format for use

Perform data analysis and machine learning tasks using Python

Understand the basics of computational linguistics

Build models for general natural language processing tasks

Evaluate the performance of a model with the right metrics

Visualize, quantify, and perform exploratory analysis from any text data

Who Should Attend

Natural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product.

Target Audiences

Natural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product.

If NLP hasn’t been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems.

You’ll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you’ll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you’ll understand how to apply NLP techniques to answer questions as can be used in chatbots.

By the end of this course, you’ll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The course will easily equip you with the knowledge you need to build applications that interpret human language.

About the Author

Dwight Gunning is a data scientist at FINRA, a financial services regulator in the US. He has extensive experience in Python-based machine learning and hands-on experience with the most popular NLP tools such as NLTK, gensim, and spacy.

Sohom Ghosh is a passionate data detective with expertise in Natural Language Processing. He has publications in several international conferences and journals.

Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.

Course Curriculum

Chapter 1: Introduction to NLP

Lecture 1: Course Overview

Lecture 2: Lesson Overview

Lecture 3: Introduction to NLP

Lecture 4: Various Steps in NLP – Part I

Lecture 5: Various Steps in NLP – Part II

Lecture 6: Lesson Summary

Chapter 2: Basic Feature Extraction Methods

Lecture 1: Lesson Overview

Lecture 2: Types of Data

Lecture 3: Cleaning Text Data – Part I

Lecture 4: Cleaning Text Data – Part II

Lecture 5: Feature Extraction from Texts

Lecture 6: Feature Engineering

Lecture 7: Lesson Summary

Chapter 3: Developing a Text Classifier

Lecture 1: Lesson Overview

Lecture 2: Machine Learning – Part I

Lecture 3: Machine Learning – Part II

Lecture 4: Developing a Text Classifier

Lecture 5: Building Pipelines for NLP Projects

Lecture 6: Saving and Loading Models

Lecture 7: Lesson Summary

Chapter 4: Collecting Text Data from the Web

Lecture 1: Lesson Overview

Lecture 2: Collecting Data by Scraping Web Pages

Lecture 3: Requesting Content from Web Pages

Lecture 4: Dealing with Semi-Structured Data

Lecture 5: Lesson Summary

Chapter 5: Topic Modeling

Lecture 1: Lesson Overview

Lecture 2: Topic Discovery

Lecture 3: Topic Modeling Algorithms

Lecture 4: Topic Fingerprinting

Lecture 5: Lesson Summary

Chapter 6: Text Summarization and Text Generation

Lecture 1: Lesson Overview

Lecture 2: Automated Text Summarization

Lecture 3: High-Level View of Text Summarization

Lecture 4: TextRank

Lecture 5: Summarizing Text Using Different Methods

Lecture 6: Lesson Summary

Chapter 7: Vector Representation

Lecture 1: Lesson Overview

Lecture 2: Vector Definition

Lecture 3: Encoding

Lecture 4: Word Embeddings and Vectors

Lecture 5: Lesson Summary

Chapter 8: Sentiment Analysis

Lecture 1: Lesson Overview

Lecture 2: Sentiment Analysis

Lecture 3: Sentiment Analysis Tools

Lecture 4: TextBlob

Lecture 5: Understanding Data for Sentiment Analysis

Lecture 6: Training Sentiment Models

Lecture 7: Lesson Summary

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 0 votes

2 stars: 3 votes

3 stars: 2 votes

4 stars: 15 votes

5 stars: 17 votes

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