AI-Enhanced Yield Management in Semiconductor Production Training Course

This instructor-led live training in Sweden (online or onsite) is designed for data scientists and software engineers who aim to use AdaBoost to develop 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 course explores the application of AI—focusing on Machine Learning and Deep Learning—within the automotive sector. It guides participants in identifying which technologies can be (potentially) applied across various in-car scenarios, ranging from basic automation and image recognition to autonomous decision-making.

Delving into the fundamentals of artificial intelligence uncovers how intelligent technologies are reshaping digital strategies, automation, and decision-making processes within enterprise operations. This exploration covers core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and various machine learning paradigms, alongside aspects of communication, perception, and autonomous action. It equips executives and architects to evaluate opportunities for AI-driven transformation, assess emerging technology trends, and integrate practical intelligent solutions to enhance business agility.

This instructor-led, live training in Sweden (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.

Artificial Neural Networks are computational models used to develop Artificial Intelligence (AI) systems that can perform "intelligent" tasks. These networks are widely utilized in Machine Learning (ML) applications, which represent one form of AI implementation, while Deep Learning constitutes a specific subset of Machine Learning.

This course empowers developers and data analysts with the essential techniques required to construct machine learning solutions from the ground up using Python. It explores the fundamental principles of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network structures. Participants will examine proven strategies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. The program supports professionals in deploying effective ML models, assessing algorithm constraints, and delivering applied projects tailored to real-world challenges.

This instructor-led live training in Sweden (online or onsite) is designed for researchers and developers who wish to use Chainer to build and train neural networks in Python while ensuring the code is easy to debug.

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

• Set up the necessary development environment to start developing neural network models.
• Define and implement neural network models using comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

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

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

• Understand 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 Sweden (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 Sweden (online or onsite) is intended for intermediate developers, data scientists, and AI professionals aiming to utilize TensorFlow Lite for Edge AI applications.

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

• Understand 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.

Kubeflow is an open-source platform engineered to simplify the process of constructing, training, and deploying machine learning workloads on Kubernetes.

This instructor-led live training (available online or onsite) targets beginner to intermediate-level professionals aiming to establish robust ML workflows using Kubeflow.

After completing this training, participants will acquire the ability to:

• Navigate the Kubeflow ecosystem and its core components.
• Create reproducible workflows utilizing Kubeflow Pipelines.
• Execute scalable training jobs on Kubernetes.
• Deploy machine learning models efficiently via Kubeflow Serving.

Course Format

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

Course Customization Options

• Tailored versions of this training can be organized to match your team’s technology stack and specific project requirements.

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

By the end 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.

This instructor-led live training in Sweden (available online or onsite) is tailored for data scientists and software engineers who want to utilize Random Forest to build machine learning algorithms for large datasets.

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

• Set up the required 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 Sweden (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.