AI-Driven Process Optimization in Chip Fabrication Training Course

This instructor-led, live training in South Korea (online or onsite) is aimed at data scientists and software engineers who wish to use AdaBoost to build boosting algorithms for machine learning with Python.

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

• Set up the necessary development environment to start building machine learning models with AdaBoost.
• Understand the ensemble learning approach and how to implement adaptive boosting.
• Learn how to build AdaBoost models to boost machine learning algorithms in Python.
• Use hyperparameter tuning to increase the accuracy and performance of AdaBoost models.

This instructor-led live training in South Korea (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.

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

• Automate the process of training highly efficient machine learning models.
• Automatically search for the best parameters for deep learning models.
• Build highly accurate machine learning models.
• Use the power of machine learning to solve real-world business problems.

This instructor-led, live training (online or onsite) is aimed at technical participants with a background in machine learning who wish to optimize models for detecting complex patterns in big data using AutoML frameworks.

This instructor-led, live training in South Korea (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 South Korea (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 in South Korea (online or onsite) is designed for data scientists and analysts who wish to automate, evaluate, and manage predictive models using DataRobot’s machine learning capabilities.

Upon completing this training, participants will be able to:

• Load datasets into DataRobot to analyze, assess, and perform quality checks on data.
• Build and train models to identify key variables and achieve prediction goals.
• Interpret models to derive actionable insights that support business decision-making.
• Monitor and manage models to ensure optimal prediction performance.

This instructor-led, live training in South Korea (online or onsite) is aimed at data scientists, data analysts, and developers who wish to explore AutoML products and features to create and deploy custom ML training models with minimal effort.

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

• Explore the AutoML product line to implement different services for various data types.
• Prepare and label datasets to create custom ML models.
• Train and manage models to produce accurate and fair machine learning models.
• Make predictions using trained models to meet business objectives and needs.

This instructor-led live training in South Korea (available online or on-site) is designed for 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.
• Use Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in South Korea (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server.

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

• Install and configure Kubernetes, Kubeflow and other needed software on AWS.
• Use EKS (Elastic Kubernetes Service) to simplify the work of initializing a Kubernetes cluster on AWS.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other AWS managed services to extend an ML application.

This instructor-led live training (available online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.

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

• Install and configure MLflow and related ML libraries and frameworks.
• Appreciate the importance of trackability, reproducibility and deployability of an ML model
• Deploy ML models to different public clouds, platforms, or on-premise servers.
• Scale the ML deployment process to accommodate multiple users collaborating on a project.
• Set up a central registry to experiment with, reproduce, and deploy ML models.

This instructor-led live training (available online or on-site) is designed for developers seeking to use Google’s ML Kit to build machine learning models optimized for mobile device processing.

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

• Set up the necessary development environment to start developing machine learning features for mobile apps.
• Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs.
• Enhance and optimize existing apps using the ML Kit SDK for on-device processing and deployment.

Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.

Format of the Course

• This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.

This instructor-led, live training in South Korea (online or onsite) is designed for data scientists and software engineers who wish to employ Random Forest for developing machine learning algorithms for large datasets.

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

• Set up the required development environment to initiate the construction of machine learning models with Random Forest.
• Understand the advantages of Random Forest and how to apply it to solve classification and regression problems.
• Learn techniques for handling large datasets and interpreting the ensemble of decision trees in Random Forest.
• Evaluate and optimize the performance of machine learning models by tuning hyperparameters.

This instructor-led live training in South Korea (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.

RapidMiner is an open-source data science software platform designed for rapid application prototyping and development. It provides an integrated environment encompassing data preparation, machine learning, deep learning, text mining, and predictive analytics.

In this instructor-led live training, participants will learn to utilize RapidMiner Studio for data preparation, machine learning, and the deployment of predictive models.

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

• Install and configure RapidMiner
• Prepare and visualize data using RapidMiner
• Validate machine learning models
• Integrate data sources and build predictive models
• Implement predictive analytics within business processes
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Note

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