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 Dartmouth or in NobleProg corporate training centers in Dartmouth.
NobleProg -- Your Local Training Provider
Dartmouth - Metropolitan Place
99 Wyse Road, Dartmouth, Canada, B3A 4S5
Metropolitan Place is an ocean-front building, close to Alderney Landing and home to leading businesses. Motorway exits and the local ferry terminal are located close by, and restaurants cafes are easily accessible for lunch.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level AI professionals and ethicists, data scientists and engineers, and policy makers and stakeholders who wish to understand and navigate the ethical landscape of LLMs.
By the end of this training, participants will be able to:
Identify ethical issues and challenges associated with LLMs.
Apply ethical frameworks and principles to LLM deployment.
Assess the societal impact of LLMs and mitigate potential risks.
Develop strategies for responsible AI development and usage.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level NLP practitioners and data scientists, content creators and translators, and global businesses who wish to use LLMs for language translation and creating multilingual content.
By the end of this training, participants will be able to:
Understand the principles of cross-lingual learning and translation with LLMs.
Implement LLMs for translating content between various languages.
Create and manage multilingual datasets for training LLMs.
Develop strategies for maintaining consistency and quality in translation.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level data scientists and developers who wish to apply NLP techniques using Python in Google Colab.
By the end of this training, participants will be able to:
Understand the core concepts of natural language processing.
Preprocess and clean text data for NLP tasks.
Perform sentiment analysis using NLTK and SpaCy libraries.
Work with text data using Google Colab for scalable and collaborative development.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at advanced-level AI researchers and policy makers who wish to explore the ethical implications of AI development and learn how to apply ethical guidelines when building AI solutions with LangChain.
By the end of this training, participants will be able to:
Identify key ethical issues in AI development with LangChain.
Understand the impact of AI on society and decision-making processes.
Develop strategies for building fair and transparent AI systems.
Implement ethical AI guidelines into LangChain-based projects.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level web developers and UX designers who wish to leverage LangChain to create intuitive and user-friendly web applications.
By the end of this training, participants will be able to:
Understand the fundamental concepts of LangChain and its role in enhancing web user experience.
Implement LangChain in web apps to create dynamic and responsive interfaces.
Integrate APIs into web apps to improve interactivity and user engagement.
Optimize user experience using LangChain’s advanced customization features.
Analyze user behavior data to fine-tune web app performance and experience.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level business analysts and automation engineers who wish to understand how to use LangChain and APIs for automating repetitive tasks and workflows.
By the end of this training, participants will be able to:
Understand the basics of API integration with LangChain.
Automate repetitive workflows using LangChain and Python.
Utilize LangChain to connect various APIs for efficient business processes.
Create and automate custom workflows using APIs and LangChain’s automation capabilities.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level content creators who wish to utilize Google Gemini AI to enhance their content quality and efficiency.
By the end of this training, participants will be able to:
Understand the role of AI in content creation.
Set up and use Google Gemini AI to generate and optimize content.
Apply text-to-text transformations to produce creative and original content.
Implement SEO strategies using AI-driven insights.
Analyze content performance and adapt strategies using Gemini AI.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level urban planners, city administrators, and smart city solution developers who wish to implement Small Language Models (SLMs) in smart city projects to improve urban living.
By the end of this training, participants will be able to:
Understand the application of SLMs in smart cities.
Integrate SLMs with urban data sets for enhanced decision-making.
Develop strategies for deploying SLMs in urban management systems.
Assess the impact of SLMs on urban planning and smart city solutions.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level customer service professionals who wish to implement Google Gemini AI in their customer service operations.
By the end of this training, participants will be able to:
Understand the impact of AI on customer service.
Set up Google Gemini AI to automate and personalize customer interactions.
Utilize text-to-text and image-to-text transformations to improve service efficiency.
Develop AI-driven strategies for real-time customer feedback analysis.
Explore advanced features to create a seamless customer service experience.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level data professionals who wish to use LangChain to enhance their data analysis and visualization capabilities.
By the end of this training, participants will be able to:
Automate data retrieval and cleaning using LangChain.
Conduct advanced data analysis using Python and LangChain.
Create visualizations with Matplotlib and other Python libraries integrated with LangChain.
Leverage LangChain for generating natural language insights from data analysis.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level educational technologists, instructional designers, and AI developers in education who wish to integrate Small Language Models (SLMs) into educational platforms to enhance teaching and learning processes.
By the end of this training, participants will be able to:
Understand the role of SLMs in educational technology.
Design AI-driven learning experiences using SLMs.
Implement SLMs in various educational settings.
Evaluate the effectiveness of SLMs in learning outcomes.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to integrate AI functionalities into their applications using Google Gemini AI.
By the end of this training, participants will be able to:
Understand the fundamentals of large language models.
Set up and use Google Gemini AI for various AI tasks.
Implement text-to-text and image-to-text transformations.
Build basic AI-driven applications.
Explore advanced features and customization options in Google Gemini AI.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at advanced-level data engineers and DevOps professionals who wish to leverage LangChain's capabilities by integrating it with various cloud services.
By the end of this training, participants will be able to:
Integrate LangChain with major cloud platforms such as AWS, Azure, and Google Cloud.
Utilize cloud-based APIs and services to enhance LangChain-powered applications.
Scale and deploy conversational agents to the cloud for real-time interaction.
Implement monitoring and security best practices in cloud environments.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level IT professionals who wish to deploy small language models directly onto devices with limited processing capabilities, opening up possibilities for innovative applications in various sectors.
By the end of this training, participants will be able to:
Understand the challenges and solutions for implementing AI on compact hardware.
Optimize and compress AI models for efficient on-device deployment.
Utilize modern AI frameworks and tools for on-device model implementation.
Design and develop real-time AI applications for mobile and IoT devices.
Evaluate and ensure the security and privacy of on-device AI systems.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level professionals who wish to deepen their understanding of conversational agents and apply LangChain to real-world use cases.
By the end of this training, participants will be able to:
Understand the fundamentals of LangChain and its application in building conversational agents.
Develop and deploy conversational agents using LangChain.
Integrate conversational agents with APIs and external services.
Apply Natural Language Processing (NLP) techniques to improve the performance of conversational agents.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at advanced-level professionals who wish to specialize in cutting-edge deep learning techniques for NLU.
By the end of this training, participants will be able to:
Understand the key differences between NLU and NLP models.
Apply advanced deep learning techniques to NLU tasks.
Explore deep architectures such as transformers and attention mechanisms.
Leverage future trends in NLU for building sophisticated AI systems.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at advanced-level professionals who wish to fine-tune and customize AI models on Ollama for enhanced performance and domain-specific applications.
By the end of this training, participants will be able to:
Set up an efficient environment for fine-tuning AI models on Ollama.
Prepare datasets for supervised fine-tuning and reinforcement learning.
Optimize AI models for performance, accuracy, and efficiency.
Deploy customized models in production environments.
Evaluate model improvements and ensure robustness.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to develop energy-efficient AI solutions with small language models that are both powerful and environmentally friendly.
By the end of this training, participants will be able to:
Understand the impact of AI on energy consumption and the environment.
Apply model compression and optimization techniques to reduce the size and energy usage of AI models.
Utilize energy-efficient hardware and software frameworks for AI deployment.
Implement best practices for sustainable AI development.
Advocate for and contribute to sustainable practices in the AI industry.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level professionals who wish to gain practical skills in using NLG for automating text summarization and content generation.
By the end of this training, participants will be able to:
Understand the core principles of NLG for text summarization and content generation.
Implement NLG models to summarize large documents and articles.
Leverage pre-trained NLG models like GPT for content creation.
Apply advanced techniques to fine-tune NLG models for specific content generation tasks.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at developers and IT professionals of all skill levels who wish to automate tasks and processes using AI without writing extensive code.
By the end of this training, participants will be able to:
Design and implement complex workflows using n8n's visual programming interface.
Integrate AI capabilities into workflows using LangChain.
Build custom chatbots and virtual assistants for various use cases.
Perform advanced data analysis and processing with AI agents.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at advanced-level professionals who wish to explore the cutting-edge techniques in NLU and semantic understanding.
By the end of this training, participants will be able to:
Understand the principles of semantic understanding and contextual AI.
Work with state-of-the-art models like transformers and pre-trained language models.
Implement NLU techniques for improved AI language comprehension.
Develop applications that leverage semantic analysis and contextual AI.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at advanced-level professionals who wish to implement secure and efficient AI-driven workflows using Ollama.
By the end of this training, participants will be able to:
Deploy and configure Ollama for private AI processing.
Integrate AI models into secure enterprise workflows.
Optimize AI performance while maintaining data privacy.
Automate business processes with on-premise AI capabilities.
Ensure compliance with enterprise security and governance policies.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at advanced-level professionals who wish to master AI-driven NLG techniques for chatbot and virtual assistant applications.
By the end of this training, participants will be able to:
Understand the core principles of NLG for conversational AI.
Leverage deep learning techniques to enhance chatbot dialogue generation.
Integrate state-of-the-art NLG models like GPT-3 into chatbot frameworks.
Apply contextual AI for improving conversational coherence and fluidity.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level data scientists, machine learning and AI researchers who wish to create engaging and efficient AI-powered conversational experiences with small language models.
By the end of this training, participants will be able to:
Understand the fundamentals of conversational AI and the role of SLMs.
Design and implement user-centric AI interactions.
Develop and train SLMs for interactive applications.
Evaluate and improve the effectiveness of human-AI communication using appropriate metrics.
Deploy scalable and ethical AI-driven conversational interfaces in real-world scenarios.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level professionals who wish to enhance their skills in generating high-quality, human-like text using advanced NLG methods.
By the end of this training, participants will be able to:
Understand advanced techniques for generating natural language text.
Implement and fine-tune transformer-based models for NLG.
Optimize NLG outputs for fluency, coherence, and relevance.
Evaluate the quality of generated text using both automated and human metrics.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level professionals who wish to deepen their expertise in modern NLU techniques.
By the end of this training, participants will be able to:
Implement advanced NLU techniques for AI systems.
Leverage deep learning models for semantic understanding.
Perform intent recognition and classification in complex applications.
Utilize state-of-the-art tools like Hugging Face Transformers for NLU tasks.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level professionals who wish to deploy, optimize, and integrate LLMs using Ollama.
By the end of this training, participants will be able to:
Set up and deploy LLMs using Ollama.
Optimize AI models for performance and efficiency.
Leverage GPU acceleration for improved inference speeds.
Integrate Ollama into workflows and applications.
Monitor and maintain AI model performance over time.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to customize pre-trained models for specific tasks and datasets.
By the end of this training, participants will be able to:
Understand the principles of fine-tuning and its applications.
Prepare datasets for fine-tuning pre-trained models.
Fine-tune large language models (LLMs) for NLP tasks.
Optimize model performance and address common challenges.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level to intermediate-level developers and software engineers who wish to learn the core concepts and architecture of LangChain and gain the practical skills for building AI-powered applications.
By the end of this training, participants will be able to:
Grasp the fundamental principles of LangChain.
Set up and configure the LangChain environment.
Understand the architecture and how LangChain interacts with large language models (LLMs).
This instructor-led, live training in Dartmouth (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 Dartmouth (online or onsite) is aimed at intermediate-level data scientists and machine learning engineers who wish to create and apply small language models tailored for specific domains such as legal, medical, and technical fields.
By the end of this training, participants will be able to:
Understand the importance and application of domain-specific language models.
Curate and preprocess specialized datasets for model training.
Train and fine-tune language models for domain-specific applications.
Evaluate and benchmark models using domain-relevant metrics.
Deploy domain-specific language models in real-world scenarios.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level professionals who wish to enhance their NLP projects through the effective fine-tuning of pre-trained language models.
By the end of this training, participants will be able to:
Understand the fundamentals of fine-tuning for NLP tasks.
Fine-tune pre-trained models such as GPT, BERT, and T5 for specific NLP applications.
Optimize hyperparameters for improved model performance.
Evaluate and deploy fine-tuned models in real-world scenarios.
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 Dartmouth (online or onsite) is aimed at beginner-level professionals who wish to learn the basics of NLG and its role in AI and content generation.
By the end of this training, participants will be able to:
Understand the fundamental concepts of Natural Language Generation.
Explore the applications of NLG in various industries.
Learn basic techniques for generating human-like text using AI.
Work with Python libraries and models to generate text.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level developers and software engineers who wish to build AI-powered applications using the LangChain framework.
By the end of this training, participants will be able to:
Understand the fundamentals of LangChain and its components.
Integrate LangChain with large language models (LLMs) like GPT-4.
Build modular AI applications using LangChain.
Troubleshoot common issues in LangChain applications.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level participants who wish to learn the fundamentals of NLU and how it fits into the larger NLP landscape.
By the end of this training, participants will be able to:
Understand the foundational concepts of Natural Language Understanding (NLU).
Differentiate between NLU and NLP.
Explore basic techniques for language interpretation and context understanding.
Apply NLU techniques to simple text processing tasks.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level professionals who wish to install, configure, and use Ollama for running AI models on their local machines.
By the end of this training, participants will be able to:
Understand the fundamentals of Ollama and its capabilities.
Set up Ollama for running local AI models.
Deploy and interact with LLMs using Ollama.
Optimize performance and resource usage for AI workloads.
Explore use cases for local AI deployment in various industries.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at beginner-level to intermediate-level data scientists and developers who wish to implement and leverage Small Language Models in various applications.
By the end of this training, participants will be able to:
Understand the architecture and functionality of Small Language Models.
Implement SLMs for tasks such as text generation and sentiment analysis.
Optimize and fine-tune SLMs for specific use cases.
Deploy SLMs in resource-constrained environments.
Evaluate and interpret the performance of SLMs in real-world scenarios.
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.
AI is a collection of technologies for building intelligent systems capable of understanding data and the activities surrounding the data to make "intelligent decisions". For Telecom providers, building applications and services that make use of AI could open the door for improved operations and servicing in areas such as maintenance and network optimization.
In this course we examine the various technologies that make up AI and the skill sets required to put them to use. Throughout the course, we examine AI's specific applications within the Telecom industry.
Audience
This course is intended for developers and data scientists who want to understand and implement artificial intelligence in their applications. Special focus is placed on data analytics, distributed AI, and natural language processing.
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 Dartmouth, 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.
In this instructor-led, live training in Dartmouth, 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.
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
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
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Testimonials (6)
prompts engineering part
Michal - GE HealthCare
Course - Generative AI with Large Language Models (LLMs)
Hunter is fabulous, very engaging, extremely knowledgeable and personable. Very well done.
Rick Johnson - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
The good humor, support and skills of the trainer.
Oumayma - Physiobotic
Course - Scaling Data Pipelines with Spark NLP
Machine Translated
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course - Natural Language Processing with TensorFlow
This is one of the best hands-on with exercises programming courses I have ever taken.
Laura Kahn
Course - Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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