Introduction to Machine Learning Training Course

This instructor-led live training in Greece (online or on-site) is designed for data scientists and software engineers who wish to use AdaBoost to build boosting algorithms for machine learning with Python.

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

• Set up the necessary development environment to begin 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—specifically Machine Learning and Deep Learning—within the automotive sector. It aims to identify technologies that can be (potentially) utilized across various automotive scenarios: from basic automation and image recognition to autonomous decision-making.

An exploration of artificial intelligence fundamentals demonstrates how intelligent technologies are transforming digital strategies, automation processes, and decision-making across enterprise operations. The course covers core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, as well as communication, perception, and autonomous action. It equips executives and architects with the insights needed to evaluate AI-driven transformation opportunities, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This instructor-led live training, offered in Greece (via online or onsite delivery), is designed for biologists who wish to understand the mechanisms of AlphaFold and apply its models to guide their experimental studies.

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

• Understand the core principles of AlphaFold.
• Learn how AlphaFold operates.
• Learn how to interpret AlphaFold predictions and results.

An Artificial Neural Network is a computational data model utilized in the creation of Artificial Intelligence (AI) systems that can perform "intelligent" tasks. These networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning constitutes a specialized subset of Machine Learning.

This instructor-led, live training in Greece (online or onsite) is designed for participants with diverse backgrounds who want to utilize Google's AutoML platform to develop customized chatbots for various 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 Greece (online or onsite) provides an introduction to the fields of pattern recognition and machine learning. It covers 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 Greece (online or onsite) is designed for data scientists and analysts looking to automate, assess, and manage predictive models using DataRobot’s machine learning capabilities.

Upon completing this training, participants will be able to:

• Import datasets into DataRobot to analyze, evaluate, and ensure data quality.
• Construct and train models to identify key variables and achieve prediction objectives.
• Interpret models to generate actionable insights for business decision-making.
• Monitor and manage models to sustain optimal prediction performance.

This instructor-led live training in Greece (online or onsite) is tailored for intermediate-level developers, data scientists, and AI practitioners looking to leverage 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.

This instructor-led, live training in Greece (online or onsite) is designed for data scientists, data analysts, and developers seeking to discover AutoML products and features to build and deploy custom ML training models with minimal effort.

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

• Navigate the AutoML product line to implement various services for different data types.
• Prepare and label datasets to generate custom ML models.
• Train and manage models to ensure they are accurate and fair.
• Generate predictions using trained models to address 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 Greece (online or onsite) targets developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Installing and configuring Kubeflow on-premise and in the cloud.
• Building, deploying, and managing ML workflows based on Docker containers and Kubernetes.
• Running entire machine learning pipelines across diverse architectures and cloud environments.
• Utilizing Kubeflow to spawn and manage Jupyter notebooks.
• Building ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led live training, offered online or onsite, targets developers who wish to utilize Google’s ML Kit to develop machine learning models optimized for mobile device processing.

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

• Establish the necessary development environment to begin creating machine learning features for mobile apps.
• Integrate new machine learning technologies into Android and iOS applications using the ML Kit APIs.
• Improve and optimize existing apps by leveraging the ML Kit SDK for on-device processing and deployment.

This instructor-led, live training in Greece (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 Greece (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.