Natural Language Processing with Python: 3-in-1
What you'll learn
- Discover how to create frequency distributions on your text with NLTK
- Build your own movie review sentiment application in Python
- Import, access external corpus & explore frequency distribution of the text in corpus file
- Perform tokenization, stemming, lemmatization, spelling corrections, stop words removals, and more
- Build solutions such as text similarity, summarization, sentiment analysis and anaphora resolution to get up to speed with new trends in NLP
- Use dictionaries to create your own named entities using this easy-to-follow guide
Course content
- Preview03:35
- 06:31Installing and Setting Up NLTK
- 09:05Implementing Simple NLP Tasks and Exploring NLTK Libraries
- 08:38Part-Of-Speech Tagging
- 09:32Stemming and Lemmatization
- 07:30Named Entity Recognition
- 04:56Frequency Distribution with NLTK
- 06:13Frequency Distribution on Your Text with NLTK
- 04:06Concordance Function in NLTK
- 03:33Similar Function in NLTK
- 04:15Dispersion Plot Function in NLTK
- 04:44Count Function in NLTK
- 03:54Introduction to Recurrent Neural Network and Long Short Term Memory
- 04:04Programming Your Own Sentiment Classifier Using NLTK
- 06:46Perform Sentiment Classification on a Movie Rating Dataset
- 05:54Starting with Latent Semantic Analysis
- 06:32Programming Example of Principal Component Analysis
- 07:25Programming Example of Singular Value Decomposition
Requirements
- Good knowledge of Python is a must
Description
Natural Language Processing is a part of Artificial Intelligence that deals with the interactions between human (natural) languages and computers.
This comprehensive 3-in-1 training course includes unique videos that will teach you various aspects of performing Natural Language Processing with NLTK—the leading Python platform for the task. Go through various topics in Natural Language Processing, ranging from an introduction to the relevant Python libraries to applying specific linguistics concepts while exploring text datasets with the help of real-word examples.
About the Author
Tyler Edwards is a senior engineer and software developer with over a decade of experience creating analysis tools in the space, defense, and nuclear industries. Tyler is experienced using a variety of programming languages (Python, C++, and more), and his research areas include machine learning, artificial intelligence, engineering analysis, and business analytics. Tyler holds a Master of Science degree in Mechanical Engineering from Ohio University. Looking forward, Tyler hopes to mentor students in applied mathematics, and demonstrate how data collection, analysis, and post-processing can be used to solve difficult problems and improve decision making.
Krishna Bhavsar has spent around 10 years working on natural language processing, social media analytics, and text mining. He has worked on many different NLP libraries such as Stanford Core NLP, IBM's System Text and Big Insights, GATE, and NLTK to solve industry problems related to textual analysis. He has also published a paper on sentiment analysis augmentation techniques in 2010 NAACL. Apart from academics, he has a passion for motorcycles and football. In his free time, he likes to travel and explore.
Naresh Kumar has more than a decade of professional experience in designing, implementing, and running very-large-scale Internet applications in Fortune Top 500 companies. He is a full-stack architect with hands-on experience in domains such as e-commerce, web hosting, healthcare, big data and analytics, data streaming, advertising, and databases. He believes in open source and contributes to it actively. Naresh keeps himself up-to-date with emerging technologies, from Linux systems internals to frontend technologies. He studied in BITS-Pilani, Rajasthan with dual degree in computer science and economics.
Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, in its research and innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. Pratap is an artificial intelligence enthusiast. When not working, he likes to read about Next-gen technologies and innovative methodologies. He is also the author of the book Statistics for Machine Learning by Packt.
Who this course is for:
- Python developers who wish to master Natural Language Processing and want to make their applications smarter by implementing NLP
Instructor
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- 57,914 Reviews
- 349,049 Students
- 1,420 Courses
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