This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.
The Apache OpenNLP library is a machine learning based toolkit for processing natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution.
In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises.
By the end of this training, participants will be able to:
Install and configure OpenNLP
Download existing models as well as create their own
Train the models on various sets of sample data
Integrate OpenNLP with existing Java applications
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
ChatBots are computer programs that automatically simulate human responses via chat interfaces. ChatBots help organizations maximize their operations efficiency by providing easier and faster options for their user interactions.
In this instructor-led, live training, participants will learn how to build chatbots in Python.
By the end of this training, participants will be able to:
Understand the fundamentals of building chatbots
Build, test, deploy, and troubleshoot various chatbots using Python
Audience
Developers
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Note
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in 中国 (online or onsite) is aimed at data scientists and developers who wish to use Spark NLP, built on top of Apache Spark, to develop, implement, and scale natural language text processing models and pipelines.
By the end of this training, participants will be able to:
Set up the necessary development environment to start building NLP pipelines with Spark NLP.
Understand the features, architecture, and benefits of using Spark NLP.
Use the pre-trained models available in Spark NLP to implement text processing.
Learn how to build, train, and scale Spark NLP models for production-grade projects.
Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).
This instructor-led, live training in 中国 (online or onsite) is aimed at data scientists and developers who wish to use TextBlob to implement and simplify NLP tasks, such as sentiment analysis, spelling corrections, text classification modeling, etc.
By the end of this training, participants will be able to:
Set up the necessary development environment to start implementing NLP tasks with TextBlob.
Understand the features, architecture, and advantages of TextBlob.
Learn how to build text classification systems using TextBlob.
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