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.
Enroll now: Natural Language Processing Fundamentals
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
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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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