Course: Text Mining and Analytics
duration: 11 hours |
Language: English (US) |
access duration: 90 days |

Details
Sometimes, business wants to find similar-sounding words, specific
word occurrences, and sentiment from the raw text. In this course,
you’ll learn different tools for text mining and analyzing the
text. Learn how to extract synonyms and hypernyms with WordNet, a
widely used tool from the Natural Language Toolkit (NLTK) and learn
how to leverage machine learning to make predictions with language
data. You’ll explore the ML pipelines and common models used for
Natural Language Processing (NLP). In addition, you will discover
many important tools available for these NLP such as polyglot,
Genism, TextBlob, and CoreNLP. Finally, you’ll learn how to
implement NLP tools to solve a business problem end-to-end.
Result
After completing this course, you will be able to use a heuristic approach of natural language processing (NLP) and to illustrate the use of WordNet, NLTK chunking, regex, and SentiWordNet. You'll be able to illustrate the use of machine learning to solve NLP problems and demonstrate the use of NLP feature engineering. Feel confident with the Python tool ecosystem for NLP and able to perform state-of-art pattern extraction on any kind of text data. And you will be able to solve NLP problems for enterprises end-to-end by leveraging a variety of concepts and tools.
Prerequisites
No formal prerequisites. However, some prior knowledge about the topic is recommended.
Target audience
Business Analyst, Data analist
Content
Text Mining and Analytics
Text Mining and Analytics: Pattern Matching & Information Extraction
Sometimes, business wants to find similar-sounding words,
- specific word occurrences, and sentiment from the raw text. Having
- learned to extract foundational linguistic features from the text,
- the next objective is to learn the heuristic approach to extract
- non-foundational features which are subjective. In this course,
- learn how to extract synonyms and hypernyms with WordNet, a widely
- used tool from the Natural Language Toolkit (NLTK). Next, explore
- the regex module in Python to perform NLTK chunking and to extract
- specific required patterns. Finally, you will solve a real-world
- use case by finding sentiments of movies using WordNet. After
- comleting this course, you will be able to use a heuristic approach
- of natural language processing (NLP) and to illustrate the use of
- WordNet, NLTK chunking, regex, and SentiWordNet.
Text Mining and Analytics: Machine Learning for Natural Language Processing
Machine learning (ML) is one of the most important toolsets
- available in the enterprise world. It gives predictive powers to
- data that can be leveraged to investigate future behaviors and
- patterns. It can help companies proactively improve their business
- and help optimize their revenue. Learn how to leverage machine
- learning to make predictions with language data. Explore the ML
- pipelines and common models used for Natural Language Processing
- (NLP). Examine a real-world use case of identifying sarcasm in text
- and discover the machine learning techniques suitable for NLP
- problems. Learn different vectorization and feature engineering
- methods for text data, exploratory data analysis for text, model
- building, and evaluation for predicting from text data and how to
- tune those models to achieve better results. After completing this
- course, you'll be able to illustrate the use of machine learning to
- solve NLP problems and demonstrate the use of NLP feature
- engineering.
Text Mining and Analytics: Natural Language Processing Libraries
There are many tools available in the Natural Language
- Processing (NLP) tool landscape. With single tools, you can do a
- lot of things faster. However, using multiple state-of-art tools
- together, you can solve many problems and extract multiple patterns
- from your data. In this course, you will discover many important
- tools available for NLP such as polyglot, Genism, TextBlob, and
- CoreNLP. Explore their benefits and how they stand against each
- other for performing any NLP task. Learn to implement core
- linguistic features like POS tags, NER, and morphological analysis
- using the tools discussed earlier in the course. Discover defining
- features of each tool such as multiple language support, language
- detection, topic models, sentiment extractions, part of speech
- (POS) driven patterns, and transliterations. Upon completion of
- this course, you will feel confident with the Python tool ecosystem
- for NLP and will be able to perform state-of-art pattern extraction
- on any kind of text data.
Text Mining and Analytics: Hotel Reviews Sentiment Analysis
Using natural language processing (NLP) tools, an organization
can analyze their review data and predict the sentiments of their
customers.
In this course, we'll learn how to implement NLP tools to solve a
business problem end-to-end. To begin, learn about loading,
exploring, and preprocessing business data. Next, explore various
linguistic features and feature engineering methods for data and
practice building machine learning (ML) models for sentiment
prediction. Finally, examine the automation options available for
building and deploying models.
After completing this course, you will be able to solve NLP
problems for enterprises end-to-end by leveraging a variety of
concepts and tools.
Course options
We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.
Optional practice exam (trial exam)
To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam.
Optional practice lab
To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediately. The lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience.
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