Online or onsite, instructor-led live Natural Language Processing (NLP) training courses demonstrate through interactive discussion and hands-on practice how to extract insights and meaning from this data. Utilizing different programming languages such as Python and R and Natural Language Processing (NLP) libraries, our trainings combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to help participants understand the meaning behind text data. NLP trainings walk participants step-by-step through the process of evaluating and applying the right algorithms to analyze data and report on its significance.
NLP training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Natural Language Processing (NLP) training can be carried out locally on customer premises in Columbia or in NobleProg corporate training centers in Columbia.
NobleProg -- Your Local Training Provider
SC, Columbia - Main Street
1320 Main Street Suite 300, Columbia, united states, 29201
The venue is located just across the street from the Joye Law Firm and next door to the Capital Bank.
This instructor-led, live training in Columbia (online or onsite) is aimed at intermediate-level data and marketing professionals who wish to apply LLMs to analyze and interpret public sentiment from various text sources such as social media posts, product reviews, and customer feedback.By the end of this training, participants will be able to:
Understand the principles of sentiment analysis and its application using LLMs.
Preprocess and prepare datasets for sentiment analysis.
Train and fine-tune LLMs to accurately reflect sentiment in text.
Analyze sentiment in real-time from social media and other text sources.
Integrate sentiment analysis findings into business strategies and decision-making processes.
This instructor-led, live training in Columbia (online or onsite) is aimed at intermediate-level data scientists, AI developers, and AI enthusiasts who wish to use LLMs to perform various NLP tasks and create novel and diverse content for different purposes.By the end of this training, participants will be able to:
Establish a development environment with LLMs and essential tools.
Expertly perform NLU and NLI tasks with LLMs.
Extract, infer, and utilize knowledge graphs effectively.
Generate and manage dialogues using LLMs for conversational applications.
Evaluate content quality and diversity generated by LLMs and generative AI.
Apply ethical principles, ensuring fairness and responsible use of LLMs.
This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, NLP researchers, and AI enthusiasts who wish to understand the inner workings of GPT models, explore the capabilities of GPT-3 and GPT-4, and learn how to leverage these models for their NLP tasks.
By the end of this training, participants will be able to:
Understand the key concepts and principles behind Generative Pre-trained Transformers.
Comprehend the architecture and training process of GPT models.
Utilize GPT-3 for tasks such as text generation, completion, and translation.
Explore the latest advancements in GPT-4 and its potential applications.
Apply GPT models to their own NLP projects and tasks.
This instructor-led, live training in Columbia (online or onsite) is aimed at data scientists, machine learning practitioners, and NLP researchers and enthusiasts who wish to effectively utilize Hugging Face for NLP tasks.
By the end of this training, participants will be able to:
Utilize a Hugging Face Transformer model, and fine-tune it on a specific dataset.
Gain the ability to independently address common NLP challenges.
Create and share your model demos effectively.
Streamline the optimization of your models for production.
Employ Hugging Face Transformers for solving a wide range of machine learning problems.
It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.
This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.
By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.
Format of the Course
Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking.
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
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.
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.
In this instructor-led, live training in Columbia, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates captions.
By the end of this training, participants will be able to:
Design and code DL for NLP using Python libraries.
Create Python code that reads a substantially huge collection of pictures and generates keywords.
Create Python Code that generates captions from the detected keywords.
Natural language generation (NLG) refers to the production of natural language text or speech by a computer.
In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.
By the end of this training, participants will be able to:
Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
Understand the NLG pipeline and apply the right techniques at each stage
Understand the architecture of a Natural Language Generation (NLG) system
Implement the most suitable algorithms and models for analysis and ordering
Pull data from publicly available data sources as well as curated databases to use as material for generated text
Replace manual and laborious writing processes with computer-generated, automated content creation
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
This classroom based training session will explore NLP techniques in conjunction with the application of AI and Robotics in business. Delegates will undertake computer based examples and case study solving exercises using Python
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
By the end of the training the delegates are expected to be sufficiently equipped with the essential python concepts and should be able to sufficiently use NLTK to implement most of the NLP and ML based operations. The training is aimed at giving not just an executional knowledge but also the logical and operational knowledge of the technology therein.
This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to use spaCy to process very large volumes of text to find patterns and gain insights.
By the end of this training, participants will be able to:
Install and configure spaCy.
Understand spaCy's approach to Natural Language Processing (NLP).
Extract patterns and obtain business insights from large-scale data sources.
Integrate the spaCy library with existing web and legacy applications.
Deploy spaCy to live production environments to predict human behavior.
Use spaCy to pre-process text for Deep Learning
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
To learn more about spaCy, please visit: https://spacy.io/
In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations.
In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.
By the end of this training, participants will be able to:
Use a command-line tool that summarizes text.
Design and create Text Summarization code using Python libraries.
TensorFlow™ is an open source software library for numerical computation using data flow graphs.
SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.
Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).
Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.
Audience
This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.
After completing this course, delegates will:
understand TensorFlow’s structure and deployment mechanisms
be able to carry out installation / production environment / architecture tasks and configuration
be able to assess code quality, perform debugging, monitoring
be able to implement advanced production like training models, embedding terms, building graphs and logging
Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.
Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov.
Audience
This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.
This course has been designed for people interested in extracting meaning from written English text, though the knowledge can be applied to other human languages as well.
The course will cover how to make use of text written by humans, such as blog posts, tweets, etc...
For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.
This instructor-led, live training in Columbia (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 Columbia (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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