Advanced AI Techniques for Semiconductor Design Automation Training Course

This instructor-led, live training in Turkey (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 Turkey (online or onsite) targets data scientists and less technical individuals who wish to utilize Auto-Keras to automate the process of selecting and optimizing machine learning models.

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 Turkey (online or onsite) targets participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customized chatbots for various applications.

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

• Grasping the core concepts of chatbot development.
• Navigating the Google Cloud Platform and accessing AutoML.
• Preparing data for training chatbot models.
• Training and assessing custom chatbot models using AutoML.
• Deploying and integrating chatbots into diverse platforms and channels.
• Monitoring and optimizing chatbot performance over time.

This instructor-led, live training in Turkey (online or on-site) 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 Turkey (online or onsite) is tailored for data scientists and data analysts seeking 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, assess, and verify data quality.
• Construct and train models to identify key variables and achieve prediction goals.
• Interpret models to derive valuable insights that support business decision-making.
• Monitor and manage models to ensure optimal prediction performance.

This instructor-led, live training in Turkey (online or onsite) is designed for data scientists, data analysts, and developers eager to explore AutoML products and features. It enables them to create and deploy custom ML training models with minimal effort.

Upon completion 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 develop custom ML models.
• Train and manage models to produce accurate and fair machine learning outcomes.
• Utilize trained models for predictions to address business objectives and needs.

This instructor-led, live training in Turkey (online or onsite) 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.
• Utilize Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Turkey (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, reproducability 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 onsite, is designed for developers who wish 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 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.

Pattern Matching is a method employed to identify specific patterns within an image. This technique helps verify whether certain characteristics exist in a captured image, such as confirming the presence of the correct label on a faulty item on a factory assembly line or verifying the exact dimensions of a component. Unlike "Pattern Recognition," which identifies general patterns by analyzing large sets of related samples, Pattern Matching explicitly defines the target pattern and simply confirms its presence or absence.

Course Format

• This course explores the approaches, technologies, and algorithms utilized in pattern matching within the context of Machine Vision.

This instructor-led, live training in Turkey (online or onsite) is tailored for data scientists and software engineers who aim to utilize Random Forest for building machine learning algorithms on large datasets.

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

• Set up the required development environment to initiate the creation of machine learning models with Random Forest.
• Comprehend the advantages of Random Forest and its implementation for solving classification and regression issues.
• Gain proficiency in handling large datasets and interpreting the multiple decision trees inherent in Random Forest.
• Evaluate and optimize machine learning model performance through hyperparameter tuning.

This instructor-led live training in Turkey (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.

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

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

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

• Install and configure RapidMiner
• Prepare and visualize data with RapidMiner
• Validate machine learning models
• Mashup data and create predictive models
• Operationalize predictive analytics within a business process
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

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.