
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) trainings in Malaysia can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Testimonials
This is one of the best quality online trainings I have ever taken in my 13 year career. Keep up the great work!
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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
About face area.
中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
The topics referring to NLG. The team was able to learn something new in the end with topics that were interesting but it was only in the last day. There were also more hands on activities than slides which was good.
Accenture Inc
Course: Python for Natural Language Generation
the last day. generation part
Accenture Inc
Course: Python for Natural Language Generation
I did like the exercises
Office for National Statistics
Course: Natural Language Processing with Python
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
Very knowledgeable
Usama Adam - TWPI
Course: Natural Language Processing with TensorFlow
The way he present everything with examples and training was so useful
Ibrahim Mohammedameen - TWPI
Course: Natural Language Processing with TensorFlow
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 quality online trainings I have ever taken in my 13 year career. Keep up the great work!
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
Natural Language Processing (NLP) Subcategories in Malaysia
Natural Language Processing (NLP) Course Outlines in Malaysia
- 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.
- 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.
- 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.
- 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.
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
- Understand the fundamentals of building chatbots
- Build, test, deploy, and troubleshoot various chatbots using Python
- Developers
- Part lecture, part discussion, exercises and heavy hands-on practice
- To request a customized training for this course, please contact us to arrange.
- 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.
- 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
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
- 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
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
- Solve text-based data science problems with high-quality, reusable code
- Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems
- Build effective machine learning models using text-based data
- Create a dataset and extract features from unstructured text
- Visualize data with Matplotlib
- Build and evaluate models to gain insight
- Troubleshoot text encoding errors
- Developers
- Data Scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
- 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
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
- To request a customized training for this course, please contact us to arrange.
- To learn more about spaCy, please visit: https://spacy.io/
- 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
- Developers
- Data Scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
- 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
- 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.).
- 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.
- Perform common NLP tasks (Tokenization, WordNet, Sentiment analysis, Spelling correction, etc.)
- Execute advanced implementations with simple APIs and a few lines of codes.
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