Introduction to Machine Learning Training Course

This instructor-led live training in Slovakia (online or onsite) targets data scientists and software engineers who wish to use AdaBoost for building boosting algorithms in machine learning with Python.

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

• Set up the required development environment to start building machine learning models with AdaBoost.
• Understand the ensemble learning approach and how to implement adaptive boosting.
• Learn how to construct AdaBoost models to enhance 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 focusing on Machine Learning and Deep Learning—within the automotive industry. It enables participants to identify which technologies can be (potentially) applied across various vehicle scenarios, ranging from basic automation and image recognition to autonomous decision-making.

Delving into the fundamentals of artificial intelligence uncovers how intelligent systems are transforming digital strategy, automation, and decision-making processes within 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 tools to evaluate AI-driven transformation opportunities, analyze emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This instructor-led, live training in Slovakia (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 fundamental principles of AlphaFold.
• Learn the operational mechanics of AlphaFold.
• Gain skills in interpreting AlphaFold predictions and results.

An Artificial Neural Network is a computational data model utilized in the development of Artificial Intelligence (AI) systems designed to perform "intelligent" tasks. Neural Networks are widely applied in Machine Learning (ML) applications, which represent one specific implementation of AI. Deep Learning is a specialized subset of Machine Learning.

This instructor-led, live training in Slovakia (online or onsite) is designed for participants with varying levels of experience who want to leverage Google's AutoML platform to build 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 Slovakia (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 Slovakia (online or onsite) is tailored for data scientists and data 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 their data.
• Build and train models to identify key variables and achieve prediction goals.
• Interpret models to generate valuable insights that support business decision-making.
• Monitor and manage models to ensure optimized prediction performance.

This instructor-led live training in Slovakia (online or on-site) is designed for intermediate developers, data scientists, and AI practitioners aiming to utilize TensorFlow Lite for Edge AI applications.

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

• Comprehend 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 Slovakia (online or onsite) targets data scientists, data analysts, and developers who want to explore AutoML products and features to create and deploy custom ML 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.

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 Slovakia (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, offered online or on-site, is designed for developers who want 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 required development environment to start creating machine learning features for mobile applications.
• Integrate new machine learning technologies into Android and iOS apps via the ML Kit APIs.
• Enhance and optimize existing applications by leveraging the ML Kit SDK for on-device processing and deployment.

This instructor-led, live training session Slovakia (online or onsite) is intended for data scientists and software engineers who aim to use Random Forest to develop machine learning algorithms for large-scale datasets.

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

• Establishing the necessary development environment to initiate the creation of machine learning models with Random Forest.
• Comprehending the advantages of Random Forest and implementing it to address classification and regression issues.
• Learning how to process large datasets and interpret the multiple decision trees utilized in Random Forest.
• Evaluating and optimizing the performance of machine learning models by tuning hyperparameters.

This instructor-led, live training in Slovakia (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:

• Apply the CRISP-DM methodology, select appropriate machine learning algorithms, and improve model construction and performance.
• Use RapidMiner to estimate and project values, and utilize analytical tools for time series forecasting.