Natural Language Processing (NLP) Training Courses

Introduction to Natural Language Processing• What is NLP and why it matters• NLP vs NLU vs NLG• Common NLP applicationsText Preprocessing and Language Basics• Tokenization, stemming, and lemmatization• Stop words and normalization• Handling noisy and unstructu...

• AI and data science beginners
• Business and technical professionals
• Product managers and analysts
• Students and academic researchers
• Anyone interested in language-based AI systems

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in NLP Foundations Training, validating their understanding of Natural Language Processing concepts and applications.

Prerequisites: • Basic understanding of computers and digital systems• Familiarity with text-based applications and documents• No prior AI, machine learning, or programming experience required

Introduction to Natural Language Understanding• Difference between NLP, NLU, and NLG• Role of NLU in AI systems• Common applications of NLUText Representation and Language Basics• Tokens, vocabulary, and text preprocessing• Bag-of-words and word embeddings• Un...

• AI and data science beginners
• Business and technical professionals
• Product managers and analysts
• Students and academic researchers
• Anyone interested in NLP and language AI

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in NLU Foundations Training, validating their understanding of Natural Language Understanding concepts and applications.

Prerequisites: • Basic understanding of computers and data concepts• Familiarity with language-based applications or systems• No prior AI, NLP, or programming experience required

Introduction to Local LLMs and Ollama• Overview of large language models• Local vs cloud-based LLMs• Use cases and benefits of OllamaGetting Started with Ollama• Installing and setting up Ollama• Running and managing models• Understanding system requirements a...

• Developers and engineers
• IT and infrastructure professionals
• AI enthusiasts and beginners
• Data privacy-focused organizations
• Students and technical professionals

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in Ollama LLM Essentials, validating their foundational skills in using Ollama for local LLM deployments.

Prerequisites: • Basic computer and system usage skills• Familiarity with command-line or desktop applications• No prior AI, machine learning, or programming experience required

Understanding LLM Behavior and Failure Modes• How LLMs generate responses• Common failure patterns in local models• Differences between prompt issues and model issuesDebugging Prompt–Model Interactions• Isolating prompt-related errors• Testing instruction clar...

• AI engineers and developers
• Machine learning practitioners
• Research engineers and analysts
• Platform and infrastructure teams
• Organizations deploying local LLM solutions

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in Ollama Model Debugging & Evaluation, validating their advanced skills in local LLM analysis and performance evaluation.

Prerequisites: • Prior experience using Ollama or local LLMs• Familiarity with prompt engineering concepts• Basic understanding of AI or LLM behavior

Introduction to Multimodal AI• Understanding multimodal models and use cases• Text, image, and cross-modal interactions• Advantages of local multimodal AI deploymentsOverview of Ollama Multimodal Capabilities• Multimodal models supported by Ollama• System requ...

• Developers and engineers
• AI practitioners and researchers
• Product and solution architects
• IT and infrastructure professionals
• Organizations adopting local AI solutions

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in Ollama Multimodal AI Training, validating their skills in building multimodal applications using Ollama.

Prerequisites: • Basic computer and system usage skills• Familiarity with AI or LLM fundamentals• No prior multimodal or deep learning experience required

Introduction to Local LLMs and Ollama• Understanding local vs cloud-based LLMs• Overview of Ollama architecture and capabilities• Use cases for offline and private AI deploymentsGetting Started with Ollama• Installing and running Ollama locally• Managing model...

• Developers and engineers
• AI enthusiasts and practitioners
• IT professionals and system administrators
• Researchers and technical consultants
• Organizations adopting local or offline LLMs

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in Ollama Prompt Engineering Training, validating their expertise in prompt engineering for local LLM environments.

Prerequisites: • Basic computer and system usage skills• Familiarity with command-line or desktop applications• No prior AI, machine learning, or prompt engineering experience required

Introduction to Sentiment Analysis• What is sentiment analysis and opinion mining• Traditional vs LLM-based sentiment analysis• Business value of sentiment insightsFoundations of LLMs for Text Analysis• How LLMs understand context and sentiment• Tokenization a...

• Data analysts and business analysts
• Marketing and customer experience teams
• Product and brand managers
• AI and NLP beginners
• Professionals working with text-based insights

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in Sentiment Analysis with LLMs Training, validating their expertise in LLM-powered sentiment analysis.

Prerequisites: • Basic understanding of text data and digital applications• Familiarity with analytics or business intelligence concepts• No prior machine learning or NLP experience required

Introduction to SLMs and Smart City AI• What are Small Language Models and how they differ from LLMs• Role of AI in smart city transformation• Benefits of SLMs for public sector deploymentsSmart City Use Cases for SLMs• Citizen engagement and service chatbots•...

• Smart city planners and administrators
• Government and public sector professionals
• Urban technology and innovation teams
• Infrastructure and operations managers
• Consultants working on smart city projects

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in SLMs for Smart Cities Training, validating their understanding of applying Small Language Models in smart city environments.

Prerequisites: • Basic understanding of smart city concepts or urban systems• Familiarity with digital platforms and data-driven services• No prior AI or machine learning experience required