AI-Driven Process Optimization in Chip Fabrication Training Course

This instructor-led, live training in Norway (available online or on-site) is designed for data scientists and software engineers who wish to employ AdaBoost to create boosting algorithms for machine learning using Python.

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

• Establish the necessary development environment to initiate the creation of machine learning models with AdaBoost.
• Comprehend the ensemble learning approach and how to execute adaptive boosting.
• Learn the process of constructing AdaBoost models to enhance machine learning algorithms in Python.
• Utilize hyperparameter tuning to improve the accuracy and performance of AdaBoost models.

This course explores the application of AI, with a focus on Machine Learning and Deep Learning, within the automotive sector. It guides participants in identifying suitable technologies for various in-car scenarios, ranging from basic automation and image recognition to autonomous decision-making systems.

Delving into the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automation processes, and decision-making capabilities within enterprise operations. This course examines essential concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside topics such as communication, perception, and autonomous action. It empowers executives and architects to evaluate opportunities for AI-driven transformation, assess emerging technological trends, and implement practical intelligent solutions to enhance business agility.

This instructor-led, live training in Norway (online or on-site) 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.

An Artificial Neural Network is a computational data model employed in creating Artificial Intelligence (AI) systems that can execute "intelligent" tasks. Neural Networks are widely utilized in Machine Learning (ML) applications, which serve as a specific implementation of AI. Deep Learning represents a specialized subset of Machine Learning.

This instructor-led, live training in Norway (online or onsite) is designed for participants with varying levels of expertise who wish to utilize Google's AutoML platform to construct customized chatbots for a range of applications.

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

• Grasp the fundamentals of chatbot development.
• Navigate the Google Cloud Platform and access AutoML.
• Prepare data for training chatbot models.
• Train and evaluate custom chatbot models using AutoML.
• Deploy and integrate chatbots into various platforms and channels.
• Monitor and optimize chatbot performance over time.

This instructor-led, live training in Norway (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on 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 core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

This instructor-led live training in Norway (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.

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

• Import datasets into DataRobot to analyze, evaluate, and perform quality checks.
• Construct and train models to identify key variables and achieve prediction objectives.
• Interpret models to generate valuable insights that support business decision-making.
• Monitor and manage models to ensure optimized prediction performance.

This live, instructor-led training, conducted in Norway (either online or onsite), is designed for intermediate-level developers, data scientists, and AI professionals seeking to apply TensorFlow Lite for Edge AI solutions.

By the conclusion of this training, participants will be capable of:

• Comprehending the basics of TensorFlow Lite and its function in Edge AI.
• Developing and optimizing AI models via TensorFlow Lite.
• Deploying TensorFlow Lite models onto various edge devices.
• Applying tools and methods for model conversion and optimization.
• Implementing functional Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Norway (online or onsite) is designed for data scientists, data analysts, and developers interested in exploring 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 various services for different 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.

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.

This instructor-led, live training in Norway (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.

This instructor-led, live training (available online or onsite) is intended for developers who wish to use Google’s ML Kit to build machine learning models optimized for mobile device processing.

Upon completing this training, participants will be able to:

• Set up the necessary development environment to begin 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.

This instructor-led, live training in Norway (online or onsite) is aimed at data scientists and software engineers who wish to use Random Forest to build machine learning algorithms for large datasets.

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

• Set up the necessary development environment to start building machine learning models with Random Forest.
• Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
• Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
• Evaluate and optimize machine learning model performance by tuning the hyperparameters.

This instructor-led live training in Norway (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilize analytical tools for time series forecasting.

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

• Learn 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.