Explore Our Courses

Ethical Deployment of LLMs

 7 Hours
This instructor-led, live training in the US (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.
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Cross-Lingual LLMs

 14 Hours
This instructor-led, live training in the US (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.
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LLMs for Financial Market Prediction

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at intermediate-level financial analysts, data scientists, and investment professionals who wish to leverage LLMs for financial market analysis and prediction.By the end of this training, participants will be able to:
  • Understand the application of LLMs in financial market analysis.
  • Use LLMs to process financial news, reports, and data for market insights.
  • Develop predictive models for stock prices, market trends, and economic indicators.
  • Integrate LLM insights into investment decision-making processes.
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LLMs for Environmental Modeling

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at intermediate-level environmental scientists and researchers, data analysts, and policy makers and environmental advocates who wish to use LLMs for environmental modeling and analysis.By the end of this training, participants will be able to:
  • Understand the application of LLMs in environmental science.
  • Utilize LLMs to analyze and model environmental data.
  • Interpret LLM outputs for environmental impact assessments.
  • Communicate findings effectively to inform policy and conservation efforts.
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LLMs in Multimodal Applications

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at intermediate-level data scientists, machine learning engineers, and software developers who wish to apply Large Language Models (LLMs) to multimodal data for advanced AI applications.By the end of this training, participants will be able to:
  • Understand the principles of multimodal learning with LLMs.
  • Implement LLMs to process and analyze text, image, and audio data.
  • Develop applications that leverage the strengths of multimodal data integration.
  • Evaluate the performance of multimodal LLM systems.
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LLMs for Cybersecurity

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at intermediate-level cybersecurity professionals and data scientists who wish to utilize LLMs for enhancing cybersecurity measures and threat intelligence.By the end of this training, participants will be able to:
  • Understand the role of LLMs in cybersecurity.
  • Implement LLMs for threat detection and analysis.
  • Utilize LLMs for security automation and response.
  • Integrate LLMs with existing security infrastructure.
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LLMs for Predictive Analytics

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at intermediate-level data scientists and business analysts who wish to utilize large language models (LLMs) to forecast trends and behaviors in various industries.By the end of this training, participants will be able to:
  • Understand the fundamentals of LLMs and their role in predictive analytics.
  • Implement LLMs to analyze and forecast data in various industries.
  • Evaluate the effectiveness of predictive models using LLMs.
  • Integrate LLMs with existing data processing pipelines.
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Large Language Models (LLMs) and Reinforcement Learning (RL)

 21 Hours
This instructor-led, live training in the US (online or onsite) is aimed at intermediate-level data scientists who wish to gain a comprehensive understanding and practical skills in both Large Language Models (LLMs) and Reinforcement Learning (RL).By the end of this training, participants will be able to:
  • Understand the components and functionality of transformer models.
  • Optimize and fine-tune LLMs for specific tasks and applications.
  • Understand the core principles and methodologies of reinforcement learning.
  • Learn how reinforcement learning techniques can enhance the performance of LLMs.
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LLMs for Content Generation

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at intermediate-level content creators, marketers, and educational technologists who wish to harness the power of LLMs for generating high-quality, diverse, and engaging content across various domains.By the end of this training, participants will be able to:
  • Understand the capabilities of LLMs and their application in content generation.
  • Set up and use LLMs for generating various types of content.
  • Apply best practices for prompting and fine-tuning LLMs to produce desired outputs.
  • Evaluate the quality of AI-generated content and refine it for specific audiences.
  • Explore advanced techniques for creative and multi-modal content generation with LLMs.
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LLMs for Personalized Education

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at educators, EdTech professionals, and researchers with varying levels of experience and expertise who wish to leverage LLMs for creating personalized educational experiences.By the end of this training, participants will be able to:
  • Understand the architecture and capabilities of LLMs.
  • Identify opportunities for personalization in educational content using LLMs.
  • Design adaptive learning platforms that utilize LLMs for content personalization.
  • Implement LLM-driven strategies for enhancing student engagement and learning outcomes.
  • Evaluate the effectiveness of LLMs in educational settings and make data-driven decisions for
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LLMs for Speech Recognition and Synthesis

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at beginner-level to intermediate-level software developers and data scientists who wish to implement LLMs in speech recognition and synthesis systems.By the end of this training, participants will be able to:
  • Understand the role of LLMs in speech technologies.
  • Implement LLMs for accurate speech recognition and natural-sounding speech synthesis.
  • Integrate LLMs with speech recognition engines and speech synthesizers.
  • Evaluate and improve the performance of speech systems using LLMs.
  • Stay informed about current trends and future directions in speech technologies.
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LLMs for Automated Customer Support

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at beginner-level to intermediate-level customer support and IT professionals who wish to implement LLMs to create responsive and intelligent customer support chatbots.By the end of this training, participants will be able to:
  • Understand the fundamentals and architecture of Large Language Models (LLMs).
  • Design and integrate LLMs into customer support systems.
  • Enhance the responsiveness and user experience of chatbots.
  • Address ethical considerations and ensure compliance with industry standards.
  • Deploy and maintain an LLM-based chatbot for real-world applications.
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LLMs for Sentiment Analysis

 21 Hours
This instructor-led, live training in the US (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.
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LLMs for Code Generation and Documentation

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at intermediate-level software developers and technical writers who wish to leverage LLMs to streamline their coding workflow and create detailed, comprehensive documentation.By the end of this training, participants will be able to:
  • Understand the role of LLMs in automating code generation and software documentation.
  • Utilize LLMs to create accurate and efficient code snippets and documentation.
  • Integrate LLMs into their software development lifecycle for enhanced productivity.
  • Maintain high-quality documentation standards using automated tools.
  • Address ethical considerations and best practices for using AI in software development.
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LLMs for Business Intelligence

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at intermediate-level business professionals and data analysts who wish to harness the power of LLMs for extracting business insights.By the end of this training, participants will be able to:
  • Understand the fundamentals and applications of LLMs in the context of business intelligence.
  • Apply LLMs to analyze large datasets and extract meaningful insights.
  • Integrate LLM-driven analytics into strategic business decision-making processes.
  • Evaluate the ethical considerations and best practices for using LLMs in business.
  • Anticipate future trends in AI and prepare for the evolving landscape of business intelligence.
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LlamaIndex: Developing LLM Powered Applications

 42 Hours
This instructor-led, live training in the US (online or onsite) is aimed at beginner-level to advanced-level developers and data scientists who wish to master LlamaIndex for developing innovative LLM-powered applications.By the end of this training, participants will be able to:
  • Set up and configure LlamaIndex for use with LLMs.
  • Index and query custom datasets using LlamaIndex to enhance LLM functionality.
  • Design and develop sophisticated applications that utilize LlamaIndex and LLMs.
  • Understand and apply best practices for working with LLMs and LlamaIndex.
  • Navigate the ethical considerations involved in deploying LLM-powered applications.
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LlamaIndex: Enhancing Contextual AI

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at intermediate-level AI researchers, machine learning professionals, and data scientists who wish to use LlamaIndex to enhance the capabilities of AI models, making them more accurate and reliable for various applications.By the end of this training, participants will be able to:
  • Understand the principles and components of LlamaIndex.
  • Ingest and structure data for use with LLMs.
  • Implement context augmentation to improve AI model performance.
  • Integrate LlamaIndex into existing AI systems and workflows.
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Google Gemini AI for Data Analysis

 21 Hours
This instructor-led, live training in the US (online or onsite) is aimed at beginner-level to intermediate-level data analysts and business professionals who wish to perform complex data analysis tasks more intuitively across various industries using Google Gemini AI.By the end of this training, participants will be able to:
  • Understand the fundamentals of Google Gemini AI.
  • Connect various data sources to Gemini AI.
  • Explore data using natural language queries.
  • Analyze data patterns and derive insights.
  • Create compelling data visualizations.
  • Communicate data-driven insights effectively.
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SLMs for Smart Cities: Urban Planning and Management with AI

 14 Hours
This instructor-led, live training in the US (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.
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Google Gemini AI for Content Creation

 14 Hours
This instructor-led, live training in the US (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.
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SLMs for Educational Tech: Tailoring AI for Learning and Development

 14 Hours
This instructor-led, live training in the US (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.
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Google Gemini AI for Transformative Customer Service

 14 Hours
This instructor-led, live training in the US (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.
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Small Language Models (SLMs) for On-Device AI

 21 Hours
This instructor-led, live training in the US (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.
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Introduction to Google Gemini AI

 14 Hours
This instructor-led, live training in the US (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.
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Small Language Models (SLMs): Developing Energy-Efficient AI

 21 Hours
This instructor-led, live training in the US (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.
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Generative AI with Large Language Models (LLMs)

 21 Hours
This instructor-led, live training in the US (online or onsite) is aimed at intermediate-level developers who wish to learn how to use generative AI with LLMs for various tasks and domains.By the end of this training, participants will be able to:
  • Explain what generative AI is and how it works.
  • Describe the transformer architecture that powers LLMs.
  • Use empirical scaling laws to optimize LLMs for different tasks and constraints.
  • Apply state-of-the-art tools and methods to train, fine-tune, and deploy LLMs.
  • Discuss the opportunities and risks of generative AI for society and business.
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AI Automation with n8n and LangChain

 14 Hours
This instructor-led, live training in the US (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.
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Small Language Models (SLMs) for Human-AI Interactions

 14 Hours
This instructor-led, live training in the US (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.
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Advanced LLMs for NLP Tasks

 21 Hours
This instructor-led, live training in the US (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.
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LangChain Fundamentals

 14 Hours
This instructor-led, live training in the US (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).
  • Develop simple applications using LangChain.
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Small Language Models (SLMs) for Domain-Specific Applications

 28 Hours
This instructor-led, live training in the US (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.
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Exploring Generative Pre-trained Transformers (GPT): From GPT-3 to GPT-4

 14 Hours
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.
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LangChain: Building AI-Powered Applications

 14 Hours
This instructor-led, live training in the US (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.
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Small Language Models (SLMs): Applications and Innovations

 14 Hours
This instructor-led, live training in the US (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.
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Introduction to Large Language Models (LLMs)

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.By the end of this training, participants will be able to:
  • Set up a development environment that includes a popular LLM.
  • Create a basic LLM and fine-tune it on a custom dataset.
  • Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
  • Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
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NLP: Natural Language Processing with R

 21 Hours
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
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Natural Language Processing (NLP) with Python

 28 Hours
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.
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Artificial Intelligence (AI) Overview

 7 Hours
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.
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AI Awareness for Telecom

 14 Hours
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
  • Network engineers
  • Network operations personnel
  • Telecom technical managers
Format of the course
  • Part lecture, part discussion, hands-on exercises
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Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

 21 Hours
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.
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Building Chatbots in Python

 21 Hours
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.
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Core ML for iOS App Development

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at web developers who wish to create machine learning models with Core ML for iOS 11 and macOS High Sierra development.By the end of this training, participants will be able to:
  • Build AI driven applications that use machine learning.
  • Implement machine learning models that classify images.
  • Use the Core ML API for supporting custom workflows and advanced use cases
  • Analyze natural language text with machine learning models.
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Deep Learning for NLP (Natural Language Processing)

 28 Hours
In this instructor-led, live training in the US, 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.
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Python for Natural Language Generation

 21 Hours
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
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Natural Language Processing (NLP) - AI/Robotics

 21 Hours
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
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OpenNLP for Text Based Machine Learning

 14 Hours
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
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Natural Language Processing (NLP) with Deep Dive in Python and NLTK

 35 Hours
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. 
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Natural Language Processing (NLP) with Python spaCy

 14 Hours
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/
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Text Summarization with Python

 14 Hours
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.
  • Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17
Audience
  • Developers
  • Data Scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Natural Language Processing (NLP) with TensorFlow

 35 Hours
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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NLP with Deeplearning4j

 14 Hours
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.AudienceThis course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.
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Testimonials(4)

Hunter is fabulous, very engaging, extremely knowledgeable and personable. Very well done.

Rick Johnson - Laramie County Community College

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Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject

Ali Kattan - TWPI

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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

I did like the exercises.

Office for National Statistics

Course - Natural Language Processing with Python

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