Machine Learning Training in Seoul

This instructor-led, live training in Seoul (online or onsite) is designed for beginner-level professionals who aim to grasp the concept of pre-trained models and learn how to apply them to solve real-world problems without developing models from the ground up.

Upon completion of this training, participants will be able to:

• Comprehend the concept and advantages of pre-trained models.
• Explore various pre-trained model architectures and their respective use cases.
• Fine-tune a pre-trained model for specific tasks.
• Implement pre-trained models in basic machine learning projects.

This instructor-led, live training in Seoul (online or on-site) is designed for participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customized chatbots for various applications.

Upon completing this training, participants will be capable of:

• Gaining a solid understanding of chatbot development principles.
• Navigating the Google Cloud Platform to access AutoML.
• Preparing data for training chatbot models.
• Training and evaluating custom chatbot models using AutoML.
• Deploying and integrating chatbots across various platforms and channels.
• Monitoring and optimizing chatbot performance over time.

This instructor-led, live training in Seoul (online or on-site) is designed for intermediate-level AI developers, machine learning engineers, and system architects who aim to optimize AI models for edge deployment.

Upon completion, participants will be able to:

• Grasp the unique challenges and prerequisites of deploying AI models on edge devices.
• Employ model compression techniques to significantly reduce model size and complexity.
• Leverage quantization methods to boost efficiency when running models on edge hardware.
• Apply pruning and additional optimization strategies to enhance overall model performance.
• Successfully deploy optimized AI models across a variety of edge devices.

This instructor-led, live training in Seoul (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.

By the end of this training, participants will be able to:

• Understand the principles of Edge AI and its benefits.
• Set up and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

This instructor-led live training in Seoul (online or onsite) is designed for advanced-level AI engineers and data scientists with intermediate to advanced experience who aim to enhance DeepSeek model performance, minimize latency, and deploy AI solutions efficiently using modern MLOps practices.

By the end of this training, participants will be able to:

• Optimize DeepSeek models for efficiency, accuracy, and scalability.
• Implement best practices for MLOps and model versioning.
• Deploy DeepSeek models on cloud and on-premise infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

MLOps on Kubernetes serves as a framework that automates the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.

This instructor-led live training (available online or onsite) targets intermediate-level practitioners seeking to build automated, scalable MLOps pipelines on Kubernetes.

Upon completion of this training, participants will be able to:

• Design end-to-end CI/CD pipelines for machine learning.
• Implement GitOps workflows for model deployment and versioning.
• Automate the training, testing, and packaging of ML models.
• Integrate monitoring, alerting, and rollback strategies.

Course Format

• Instructor-guided presentations and technical deep dives.
• Hands-on exercises focused on building real-world CI/CD workflows.
• Live-lab practice for deploying ML workloads to Kubernetes.

Course Customization Options

• Organizations may request tailored content aligned with their internal MLOps tools and infrastructure.

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

• Guided presentations and collaborative discussions.
• Hands-on labs with real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customization Options

• Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.

TinyML involves deploying optimized machine learning models onto edge devices with limited resources.

This instructor-led live training, available online or onsite, is designed for advanced technical professionals who aim to design, optimize, and implement complete TinyML pipelines.

By the end of this training, participants will be able to:

• Gather, preprocess, and manage datasets tailored for TinyML applications.
• Train and optimize models for low-power microcontrollers.
• Transform models into lightweight formats compatible with edge devices.
• Deploy, test, and monitor TinyML applications on actual hardware.

Course Format

• Instructor-led lectures combined with technical discussions.
• Practical labs and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Customization Options

• To tailor the training to specific toolchains, hardware boards, or internal workflows, please contact us to arrange.

This instructor-led live training, offered in Seoul (online or onsite), targets intermediate developers, data scientists, and AI practitioners eager to harness TensorFlow Lite for Edge AI solutions.

By the conclusion of this training, participants will be able to:

• Comprehend the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Seoul (online or onsite) targets advanced-level professionals aiming to master the technologies underlying autonomous systems.

Upon completing this training, participants will be able to:

• Design and implement AI models for autonomous decision-making.
• Develop control algorithms for autonomous navigation and obstacle avoidance.
• Ensure safety and reliability in AI-driven autonomous systems.
• Integrate autonomous systems with existing robotics and AI frameworks.

This instructor-led, live training in Seoul (online or onsite) is designed for advanced-level professionals seeking to deepen their understanding of computer vision and explore TensorFlow’s capabilities for developing sophisticated vision models using Google Colab.

By the end of this training, participants will be able to:

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

TinyML represents a methodology for deploying machine learning models onto low-power, resource-constrained devices operating at the network edge.

This instructor-led, live training (available online or onsite) targets advanced-level professionals seeking to secure TinyML pipelines and implement privacy-preserving techniques within edge AI applications.

Upon completion of this course, participants will be capable of:

• Identifying security risks specific to on-device TinyML inference.
• Implementing privacy-preserving mechanisms for edge AI deployments.
• Hardening TinyML models and embedded systems against adversarial threats.
• Applying best practices for secure data handling in constrained environments.

Format of the Course

• Engaging lectures supported by expert-led discussions.
• Practical exercises emphasizing real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tooling.

Course Customization Options

• Organizations may request a tailored version of this training to align with their specific security and compliance needs.

This instructor-led live training in Seoul (online or onsite) is designed for advanced professionals looking to deepen their knowledge of machine learning models, improve hyperparameter tuning skills, and learn effective deployment methods using Google Colab.

By the end of this training, participants will be able to:

• Implement advanced machine learning models using popular frameworks like Scikit-learn and TensorFlow.
• Optimize model performance through hyperparameter tuning.
• Deploy machine learning models in real-world applications using Google Colab.
• Collaborate and manage large-scale machine learning projects in Google Colab.

This instructor-led, live training session (available online or onsite) is designed for intermediate-level professionals who wish to apply AI techniques to optimize yield management in semiconductor manufacturing.

Upon completion of this training, participants will be equipped to:

• Analyze production data to pinpoint factors influencing yield rates.
• Deploy AI algorithms to improve yield management processes.
• Optimize production parameters to decrease defects and enhance yields.
• Integrate AI-driven yield management systems into current production workflows.

This instructor-led live training in Seoul (online or onsite) is aimed at intermediate-level business and AI professionals who wish to apply machine learning in business, forecasting, and AI-driven systems using real case studies and Python-based tools.

By the end of this training, participants will be able to:

• Understand how machine learning fits within AI and business strategy.
• Apply supervised and unsupervised learning techniques to structured business problems.
• Preprocess and transform data for modeling.
• Use neural networks for classification and prediction tasks.
• Perform sales forecasting using statistical and ML-based methods.
• Implement clustering and association rule mining for customer segmentation and pattern discovery.

This instructor-led live training, conducted Seoul (either online or onsite), targets intermediate-level professionals who want to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to improve production efficiency and reduce unexpected equipment failures.

By the end of this training, participants will be able to:

• Implement AI models for predicting equipment failures in semiconductor manufacturing.
• Analyze maintenance data to identify patterns and trends indicative of potential issues.
• Integrate AI-driven predictive maintenance into existing manufacturing workflows.
• Reduce downtime and maintenance costs through proactive equipment management.

This instructor-led, live training in Seoul (online or onsite) is aimed at advanced-level professionals who wish to apply cutting-edge AI techniques to semiconductor design automation, improving efficiency, accuracy, and innovation in chip design and verification.

By the end of this training, participants will be able to:

• Apply advanced AI techniques to optimize semiconductor design processes.
• Integrate machine learning models into EDA tools for enhanced design verification.
• Develop AI-driven solutions for complex design challenges in chip fabrication.
• Leverage neural networks for improving the accuracy and speed of design automation.

This instructor-led, live training in Seoul (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

By the end of this training, participants will be able to:

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

This instructor-led, live training session in Seoul (online or onsite) is designed for intermediate professionals seeking to understand and apply AI techniques for optimizing semiconductor fabrication processes.

Upon completion of this training, participants will be capable of:

• Grasping AI methodologies used for process optimization in chip fabrication.
• Deploying AI models to improve yield and minimize defects.
• Examining process data to pinpoint critical parameters for optimization.
• Utilizing machine learning techniques to fine-tune semiconductor manufacturing workflows.

This instructor-led live training, offered Seoul (online or onsite), is designed for intermediate-level participants who aim to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

By the end of this training, participants will be able to:

• Set up Apache Airflow for orchestrating machine learning workflows.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimize machine learning workflows in production.

This instructor-led live training in Seoul (online or onsite) is tailored for intermediate-level data scientists and developers who aim to apply machine learning algorithms efficiently using the Google Colab environment.

By the conclusion of this training, participants will be able to:

• Set up and navigate Google Colab for machine learning projects.
• Understand and apply various machine learning algorithms.
• Use libraries like Scikit-learn to analyze and predict data.
• Implement supervised and unsupervised learning models.
• Optimize and evaluate machine learning models effectively.

TinyML refers to the practice of deploying machine learning models on highly resource-constrained hardware.

This instructor-led, live training (online or onsite) is aimed at advanced-level practitioners who wish to optimize TinyML models for low-latency, memory-efficient deployment on embedded devices.

Upon completing this training, participants will be able to:

• Apply quantization, pruning, and compression techniques to reduce model size without sacrificing accuracy.
• Benchmark TinyML models for latency, memory consumption, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Evaluate trade-offs between performance, accuracy, and hardware constraints.

Format of the Course

• Instructor-led presentations supported by technical demonstrations.
• Practical optimization exercises and comparative performance testing.
• Hands-on implementation of TinyML pipelines in a controlled lab environment.

Course Customization Options

• For tailored training aligned with specific hardware platforms or internal workflows, please contact us to customize the program.

This instructor-led, live training in Seoul (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems.

By the end of this training, participants will be able to:

• Understand the challenges of explainability in deep learning.
• Implement advanced XAI techniques for neural networks.
• Interpret decisions made by deep learning models.
• Evaluate the trade-offs between performance and transparency.

This instructor-led, live training in Seoul (online or onsite) is designed for beginner-level professionals who want to understand and apply AI technologies within the semiconductor manufacturing industry.

Upon completing this training, participants will be able to:

• Grasp the fundamental principles of AI and their application in semiconductor manufacturing.
• Identify specific areas within semiconductor manufacturing where AI can be effectively implemented.
• Leverage AI tools and techniques to enhance production efficiency and quality control.
• Deploy basic AI models to optimize manufacturing processes.

Docker serves as a containerization platform that enables the creation of reproducible, portable, and scalable environments for machine learning systems.

This instructor-led training, available in online or onsite formats, is designed for intermediate to advanced technical professionals seeking to containerize and operationalize complete ML pipelines using Docker.

Upon completing this training, participants will be equipped to:

• Containerize machine learning training, validation, and inference workloads.
• Design and orchestrate end-to-end ML pipelines utilizing Docker and complementary tools.
• Implement versioning, reproducibility, and CI/CD processes for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Course Format

• Interactive lectures accompanied by practical demonstrations.
• Hands-on exercises focused on constructing real-world ML pipeline components.
• Live-lab implementation of end-to-end containerized workflows.

Customization Options

• For training tailored to specific ML infrastructure requirements, please contact us to discuss available options.

This instructor-led live training in Seoul (online or onsite) is aimed at data scientists and developers who wish to use ML.NET machine learning models to automatically derive projections from executed data analysis for enterprise applications.

By the end of this training, participants will be able to:

• Install ML.NET and integrate it into the application development environment.
• Understand the machine learning principles behind ML.NET tools and algorithms.
• Build and train machine learning models to perform predictions with the provided data smartly.
• Evaluate the performance of a machine learning model using the ML.NET metrics.
• Optimize the accuracy of the existing machine learning models based on the ML.NET framework.
• Apply the machine learning concepts of ML.NET to other data science applications.

This instructor-led, live training in Seoul (online or onsite) is aimed at intermediate-level data professionals who wish to apply machine learning techniques to data-driven business problems, including sales forecasting and predictive modeling using neural networks.

By the end of this training, participants will be able to:

• Understand the core concepts and types of machine learning.
• Apply key algorithms for classification, regression, clustering, and association analysis.
• Perform exploratory data analysis and data preparation using Python.
• Use neural networks for nonlinear modeling tasks.
• Implement predictive analytics for business forecasting, including sales data.
• Evaluate and optimize model performance using visual and statistical techniques.

This instructor-led, live training in Seoul (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

By the end of this training, participants will be able to:

• Grasp advanced deep learning architectures and techniques for text-to-image generation.
• Develop complex models and implement optimizations for high-quality image synthesis.
• Enhance performance and scalability for large datasets and complex models.
• Refine hyperparameters to improve model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools

This instructor-led, live training in Seoul (online or onsite) targets cybersecurity professionals at the intermediate to advanced levels who seek to enhance their skills in AI-driven threat detection and incident response.

By the end of this training, participants will be able to:

• Deploy advanced AI algorithms for real-time threat detection.
• Tailor AI models to address specific cybersecurity challenges.
• Create automation workflows for effective threat response.
• Protect AI-driven security tools from adversarial attacks.

This instructor-led, live training in Seoul (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who want to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completing this training, participants will be able to:

• Grasp the core concepts of TinyML and its advantages for edge AI applications.
• Establish a development environment tailored for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Leverage TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
• Enhance AI models for improved power efficiency and to meet memory limitations.

This instructor-led, live training Seoul (online or onsite) is designed for beginner-level cybersecurity professionals who want to learn how to leverage AI to improve threat detection and response capabilities.

By the end of this training, participants will be able to:

• Understand AI applications in cybersecurity.
• Implement AI algorithms for threat detection.
• Automate incident response with AI tools.
• Integrate AI into existing cybersecurity infrastructure.

This instructor-led, live training in Seoul (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

This instructor-led live training in Seoul (online or onsite) is designed for intermediate-level data analysts who want to learn how to use RapidMiner to estimate and project values, as well as utilize analytical tools for time series forecasting.

By the end of this training, participants will be able to:

• Apply the CRISP-DM methodology, select appropriate machine learning algorithms, and enhance model construction and performance.
• Use RapidMiner to estimate and project values, and utilize analytical tools for time series forecasting.

This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

By the end of this training, participants will be able to:

• Understand the principles of Stable Diffusion and how it works for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

This instructor-led, live training in Seoul (online or onsite) is designed for beginner-level engineers and data scientists seeking to grasp the fundamentals of TinyML, explore its practical applications, and deploy AI models on microcontrollers.

Upon completing this training, participants will be able to:

• Comprehend the core principles of TinyML and its importance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models to minimize power consumption.
• Utilize TinyML for real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

This course aims to equip participants with practical proficiency in applying Machine Learning methods. Utilizing the Python programming language and its extensive ecosystem of libraries, alongside numerous hands-on examples, the curriculum covers the essential components of Machine Learning. Participants will learn to make informed decisions about data modeling, interpret algorithmic outputs, and validate results effectively.

Our objective is to empower you with the confidence to master and apply the fundamental tools of the Machine Learning toolkit, while helping you avoid common pitfalls encountered in Data Science applications.

This course provides programmers and data analysts with the foundational techniques required to build machine learning solutions from the ground up using Python. It covers the core principles of supervised learning (classification and regression), unsupervised learning (clustering and anomaly detection), and advanced neural network architectures. Participants will examine proven methods for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The program empowers professionals to implement practical ML models, assess algorithm limitations, and complete applied projects aimed at solving real-world problems.

Deep Reinforcement Learning (DRL) integrates the principles of reinforcement learning with deep learning structures, empowering agents to make decisions through environmental interaction. This technology drives numerous modern AI innovations, including autonomous vehicles, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to refine policies, learn strategies, and execute autonomous decisions by leveraging trial-and-error mechanisms and reward-based feedback.

This instructor-led training session, available both online and onsite, is designed for intermediate developers and data scientists looking to master and apply Deep Reinforcement Learning techniques. The goal is to equip participants with the skills necessary to develop intelligent agents capable of making autonomous decisions in complex scenarios.

Upon completing this course, participants will be able to:

• Grasp the theoretical foundations and mathematical core of Reinforcement Learning.
• Code essential RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents utilizing TensorFlow or PyTorch.
• Deploy DRL solutions for practical use cases, including gaming, robotics, and optimization tasks.
• Utilize contemporary tools to visualize, troubleshoot, and optimize training performance.

Course Format

• Engaging lectures paired with guided discussions.
• Practical exercises and real-world implementation scenarios.
• Live coding sessions and project-based applications.

Customization Options

• For customized course versions (e.g., switching from TensorFlow to PyTorch), please reach out to us to make arrangements.

An examination of the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automation, and decision-making processes throughout enterprise operations. This course covers core concepts including the history of AI, frameworks for problem-solving, methods for representing knowledge, reasoning under uncertainty, and various machine learning paradigms, as well as aspects of communication, perception, and autonomous action. It is designed to help executives and architects evaluate opportunities for AI-driven transformation, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This course explores the application of AI—specifically Machine Learning and Deep Learning—within the automotive industry. It aims to identify technologies suitable for various in-car scenarios, ranging from basic automation and image recognition to autonomous decision-making.

This 8-day programme takes participants on a comprehensive journey, starting with robust Python engineering principles and progressing to advanced AI system design. Attendees will cultivate disciplined coding habits, gain mastery over statistical and deep learning techniques, and construct production-grade generative AI and agent-based systems. The curriculum prioritises reliability, evaluation, safety, and real-world deployment over mere experimentation.

An Artificial Neural Network is a computational data model utilized in the development of Artificial Intelligence (AI) systems designed to perform 'intelligent' tasks. Neural Networks are widely employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning serves as a specialized subset of Machine Learning.

Enhance your data science proficiency through this in-depth Machine Learning training program. The course explores fundamental algorithms such as Naive Bayes, Decision Trees, Neural Networks, Support Vector Machines, and Clustering techniques. Participants will acquire practical experience alongside theoretical knowledge, applying real-world case studies. This curriculum is designed for data analysts, software engineers, AI enthusiasts, and business leaders aiming to implement machine learning solutions. You will master key concepts including classification performance metrics, cross-validation, the bias-variance trade-off, and deep learning principles to construct reliable predictive models.

This instructor-led, live training in Seoul (online or onsite) offers a foundational introduction to the fields of pattern recognition and machine learning. It explores practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

By the end of this training, participants will be able to:

• Apply fundamental statistical methods to pattern recognition.
• Leverage key models such as neural networks and kernel methods for effective data analysis.
• Deploy advanced techniques to solve complex problems.
• Enhance prediction accuracy by integrating various models.

This instructor-led, live training (online or onsite) is designed for data scientists who want to use TensorFlow to analyze potential fraud data.

By the end of this training, participants will be able to:

• Create a fraud detection model in Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

Machine learning constitutes a domain of Artificial Intelligence where computers acquire the ability to learn from experience without being explicitly programmed.

Deep learning is a specialized subset of machine learning that employs methods based on learning data representations and structures, such as neural networks.

Python is a high-level programming language renowned for its clear syntax and high code readability.

In this instructor-led live training, participants will learn how to implement deep learning models for the telecommunications sector using Python, guiding them through the creation of a deep learning credit risk model.

Upon completion of this training, participants will be able to:

• Grasp the fundamental concepts of deep learning.
• Identify the applications and uses of deep learning within the telecom industry.
• Utilize Python, Keras, and TensorFlow to construct deep learning models tailored for telecom.
• Develop their own deep learning customer churn prediction model using Python.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This hands-on, instructor-led program serves as a seamless follow-up to the Python for Data Analysis course.

Participants will be introduced to fundamental Machine Learning concepts and learn how to apply them to practical data analysis tasks, including prediction, classification, and segmentation.

The curriculum emphasizes understanding the practical application of Machine Learning using accessible tools like Python, Pandas, and Jupyter Notebook, requiring no advanced mathematical expertise.

This instructor-led, live training in Seoul (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.

By the end of this training, participants will be able to:

• Install and configure various MLOps frameworks and tools.
• Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
• Prepare, validate and version data for use by ML models.
• Understand the components of an ML Pipeline and the tools needed to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.

This instructor-led, live training in Seoul (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

By the end of this course, participants will be able to:

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.

This instructor-led, live training in Seoul (online or onsite) is designed for developers and data scientists who want to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and more.

By the end of this training, participants will be able to:

• Install and configure TensorFlow 2.x.
• Understand the advantages of TensorFlow 2.x over previous versions.
• Construct deep learning models.
• Implement an advanced image classifier.
• Deploy deep learning models to the cloud, mobile devices, and IoT devices.

This course provides foundational knowledge of neural networks and their broader role within machine learning and deep learning algorithms and applications.

The first segment (40%) of the training emphasizes core fundamentals, equipping you to select appropriate technologies such as TensorFlow, Caffe, Theano, DeepDrive, and Keras.

The second segment (20%) introduces Theano, a Python library designed to simplify the development of deep learning models.

The third segment (40%) focuses extensively on TensorFlow, the open-source software library for deep learning developed by Google. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers aiming to utilize TensorFlow for their deep learning projects.

Upon completion, participants will:

• gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend the structure and deployment mechanisms of TensorFlow
• possess the skills to manage installation, production environment setup, architecture tasks, and configuration
• be capable of assessing code quality, performing debugging, and monitoring systems
• be able to implement advanced production-level tasks, including model training, graph construction, and logging