Xiamen International Plaza

8/F, 8/F, International Bank Tower Centre, 8 Lujiang Road, Siming District, Xiamen, china, 361001
This premium business centre is located on the 8th floor of International Plaza, a 37th storey landmark on Lujiang Road in Xi...

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Explore Our Courses

Generative AI with Large Language Models (LLMs)

 21 Hours
This instructor-led, live training in Xiamen (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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Milvus: Open-Source Vector Database for AI Applications

 21 Hours
This instructor-led, live training in Xiamen (online or onsite) is aimed at beginner-level to intermediate-level data scientists and software developers who wish to learn about Milvus and its practical applications in various AI scenarios.By the end of this training, participants will be able to:
  • Understand the architecture and features of Milvus.
  • Implement vector databases in different AI applications.
  • Perform similarity searches with high accuracy and speed.
  • Apply Milvus to real-world AI challenges.
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LightGBM for Machine Learning

 21 Hours
This instructor-led, live training in Xiamen (online or onsite) is aimed at beginner to intermediate-level developers and data scientists who wish to learn the basics of LightGBM and explore advanced techniques. By the end of this training, participants will be able to:
  • Install and configure LightGBM.
  • Understand the theory behind gradient boosting and decision tree algorithms
  • Use LightGBM for basic and advanced machine learning tasks.
  • Implement advanced techniques such as feature engineering, hyperparameter tuning, and model interpretation.
  • Integrate LightGBM with other machine learning frameworks.
  • Troubleshoot common issues in LightGBM.
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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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Applied AI from Scratch in Python

 28 Hours
This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course. 
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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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Artificial Intelligence (AI) for City Planning

 14 Hours
What will cities look like in the future? How can Artificial Intelligence (AI) be used to improve city planning? How can AI be used to make cities more efficient, livable, safer and environmentally friendly? In this instructor-led, live training (onsite or remote), we examine the various technologies that make up AI, as well as the skill sets and mental framework required to put them to use for city planning. We also cover tools and approaches for gathering and organizing relevant data for use in AI, including data mining. Audience
  • City planners
  • Architects
  • Developers
  • Transportation officials
Format of the Course
  • Part lecture, part discussion, and a series of interactive exercises.
Note
  • To request a customized training for this course, please contact us to arrange.
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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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From Zero to AI

 35 Hours
This course is created for people who have no previous experience in probability and statistics.
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Algebra for Machine Learning

 14 Hours
Linear algebra is a branch of mathematics that deals with vectors, matrices, and linear transforms. Knowledge of linear algebra helps engineers and developers improve their machine learning capabilities. Understanding linear algebra concepts allows them to better understand the principles behind machine learning techniques and thus solve problems faster. In this instructor-led, live training, participants will learn the fundamentals of linear algebra as they step through solving a machine learning problem using linear algebra methods. By the end of this training, participants will be able to:
  • Understand fundamental linear algebra concepts
  • Learn the linear algebra skills needed for machine learning
  • Use linear algebra structures and concepts when working with data, images, algorithms, etc.
  • Solve a machine learning problem using linear algebra
Audience
  • Developers
  • Engineers
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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Azure Machine Learning (AML)

 21 Hours
This instructor-led, live training in Xiamen (online or onsite) is aimed at engineers who wish to use Azure ML's drag-and-drop platform to deploy Machine Learning workloads without having to purchase software and hardware and without having to worry about maintenance and deployment. By the end of this training, participants will be able to:
  • Write highly-accurate machine learning models using Python, R, or zero-code tools.
  • Leverage Azure's available data sets and algorithms to train and track machine learning and deep-learning models.
  • Use Azures interactive workspace to collaboratively develop ML models.
  • Choose from different Azure-supported ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
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Artificial Neural Networks, Machine Learning, Deep Thinking

 21 Hours
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
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Applied AI from Scratch

 28 Hours
This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course. 
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Applied Machine Learning

 14 Hours
This training course is for people that would like to apply Machine Learning in practical applications. Audience This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience.
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Amazon Web Services (AWS) SageMaker

 21 Hours
This instructor-led, live training in Xiamen (online or onsite) is aimed at data scientists and developers who wish to create and train machine learning models for deployment into production-ready hosting environments. By the end of this training, participants will be able to:
  • Use notebook instances to prepare and upload data for training.
  • Train machine learning models using training datasets.
  • Deploy trained models to an endpoint to create predictions.
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Azure Machine Learning

 14 Hours
This instructor-led, live training in Xiamen (online or onsite) is aimed at data scientists who wish to use Azure Machine Learning to build end-to-end machine learning models for predictive analysis. By the end of this training, participants will be able to:
  • Build machine learning models with zero programming experience.
  • Create predictive algorithms with Azure Machine Learning.
  • Deploy production ready machine learning algorithms.
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Core ML for iOS App Development

 14 Hours
This instructor-led, live training in Xiamen (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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Dataiku for Enterprise AI and Machine Learning

 21 Hours
This instructor-led, live training in Xiamen (online or onsite) is aimed at engineers, data scientists, and data analysts who wish to use Dataiku DSS for the development of machine learning pipelines, and leverage AI processes in generating strategic organizational initiatives. By the end of this training, participants will be able to:
  • Install and configure Dataiku DSS upon their preferred operating system.
  • Understand AI/ML concepts and principles that are fundamental to Dataiku features.
  • Create and implement their own data visualization codes in Dataiku DSS projects.
  • Deploy ML models and pipelines into production environments built around Dataiku DSS.
  • Optimize the agility and flexibility of data analysis methods of their enterprise applications.
  • Utilize Dataiku DSS to secure and manage how data flows through further business systems.
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DeepMind Lab

 14 Hours
This instructor-led, live training in Xiamen (online or onsite) is aimed at researchers and developers who wish to install, set up, customize, and use the DeepMind Lab platform to develop general artificial intelligence and machine learning systems. By the end of this training, participants will be able to:
  • Customize DeepMind Lab to build and run an environment that suits learning and training needs.
  • Use DeepMind Lab's 3D simulation environment to train learning agents in a first-person viewpoint.
  • Facilitate agent evaluation to develop intelligence in a 3D game-like world.
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Data Mining & Machine Learning with R

 14 Hours
R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.
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Feature Engineering for Machine Learning

 14 Hours
This instructor-led, live training in Xiamen (online or onsite) is aimed at engineers who wish to apply feature engineering techniques to better process data and achieve obtain better machine learning models. By the end of this training, participants will be able to:
  • Set up an optimal development environment, including all needed Python packages.
  • Obtain important insights by analyzing the features of a data set.
  • Optimize machine learning models through adaptation of the raw data itself.
  • Clean and transform data sets in preparation for machine learning.
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Fundamentals of Artificial Intelligence (AI) and Machine Learning

 28 Hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to:
  • Understand the fundamental concepts of deep learning.
  • Learn the applications and uses of deep learning in telecom.
  • Use Python, Keras, and TensorFlow to create deep learning models for telecom.
  • Build their own deep learning customer churn prediction model using Python.
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.
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GANs and Variational Autoencoders in Python

 14 Hours
This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to use GANs and variational autoencoders to generate new, synthetic instances of images, videos, and audio. By the end of this training, participants will be able to:
  • Build a GAN using machine learning libraries in Python.
  • Create a variational autoencoder with TensorFlow.
  • Improve the training stability of GANs.
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Machine Learning Algorithms in Julia

 21 Hours
This course is for people that already have a background in data science and statistics. The explanations given are designed to either serve as a reminder to those that are already familiar with the concepts or inform those with a suitable background.
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Kubeflow

 35 Hours
This instructor-led, live training in Xiamen (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes. By the end of this training, participants will be able to:
  • Install and configure Kubeflow on premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
  • Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
  • Run entire machine learning pipelines on diverse architectures and cloud environments.
  • Using Kubeflow to spawn and manage Jupyter notebooks.
  • Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
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Kubeflow Fundamentals

 28 Hours
This instructor-led, live training in Xiamen (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes. By the end of this training, participants will be able to:
  • Install and configure Kubeflow on premise and in the cloud.
  • Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
  • Run entire machine learning pipelines on diverse architectures and cloud environments.
  • Using Kubeflow to spawn and manage Jupyter notebooks.
  • Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
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Mathematica for Machine Learning

 14 Hours
This instructor-led, live training in Xiamen (online or onsite) is aimed at data scientists who wish to use machine learning in Mathematica for data analysis. By the end of this training, participants will be able to:
  • Build and train machine learning models.
  • Import and prepare data for machine learning.
  • Separate training data from test data.
  • Explore deep learning and neural network applications in data analysis.
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Artificial Intelligence (AI) for Mechatronics

 21 Hours
This instructor-led, live training in Xiamen (online or onsite) is aimed at engineers who wish to learn about the applicability of artificial intelligence to mechatronic systems. By the end of this training, participants will be able to:
  • Gain an overview of artificial intelligence, machine learning, and computational intelligence.
  • Understand the concepts of neural networks and different learning methods.
  • Choose artificial intelligence approaches effectively for real-life problems.
  • Implement AI applications in mechatronic engineering.
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Machine Learning for Banking (with Python)

 21 Hours
Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Machine Learning for Banking (with R)

 28 Hours
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects. Audience
  • Developers
  • Data scientists
  • Banking professionals with a technical background
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Testimonials (7)

We had an overview about Machine Learning, Neural Networks, AI with practical examples.

Catalin - DB Global Technology SRL
Course - Machine Learning and Deep Learning

Last day with the AI

Ovidiu - DB Global Technology SRL
Course - Machine Learning and Deep Learning

The trainer showed that he has a good understanding of the subject.

Marino - EQUS - The University of Queensland
Course - Machine Learning with Python – 2 Days

It was a great intro to ML!! I liked the whole thing, really. The organization was perfect. The right amount of time for lectures/ demos and just us playing around. Lots of topics were touched, just at the right level. He was also very good at keeping us super engaged, even without any camera being on.

Zsolt - EQUS - The University of Queensland
Course - Machine Learning with Python – 2 Days

Interesting knowledge

Gabriel - MINDEF
Course - Machine Learning with Python – 4 Days

Fantastic training, one of the best I have ever attended! The moderator, Rafal, provided excellent answers to the issues raised and explained all the methods very thoroughly. JestI am very satisfied and will gladly take advantage of the training conducted by this trainer again.

Darek Paszkowski - Orange Szkolenia Sp. z o.o.

Machine Translated

Drawings on a flipchart, the entire training.

Kasia Nawrot - Orange Szkolenia Sp. z o.o.

Machine Translated

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