Introduction to Machine Learning Training Course

This instructor-led, live training in Romania (available online or onsite) is designed for data scientists and software engineers who want 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 course explores the application of AI, with a particular emphasis on Machine Learning and Deep Learning, within the automotive industry. It aids in identifying technologies that can be (potentially) applied across various automotive scenarios, ranging from basic automation and image recognition to autonomous decision-making.

An exploration of artificial intelligence fundamentals reveals how intelligent technology reshapes digital strategy, automation, and decision making across enterprise operations. Examines core concepts spanning AI history, problem-solving frameworks, knowledge representation, uncertain reasoning, and machine learning paradigms alongside communication, perception, and autonomous action. Guides executives and architects to evaluate AI-driven transformation opportunities, assess emerging technology trends, and integrate practical intelligent solutions to accelerate business agility.

This instructor-led, live training in Romania (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.

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

This instructor-led, live training in Romania (online or onsite) is designed for participants with varying levels of experience who want to utilize Google's AutoML platform to develop customized chatbots for a wide range of applications.

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

• Grasp the core principles of chatbot development.
• Navigate the Google Cloud Platform and access AutoML.
• Prepare data for training chatbot models.
• Train and assess 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 Romania (online or onsite) offers a comprehensive introduction to the fields of pattern recognition and machine learning. It explores practical applications across statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

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

• Apply fundamental statistical methods to pattern recognition tasks.
• Leverage essential models such as neural networks and kernel methods for data analysis.
• Implement advanced techniques to address complex problem-solving scenarios.
• Enhance prediction accuracy by integrating various models.

This instructor-led training, offered online or onsite, is designed for data scientists and analysts who want to automate, evaluate, and manage predictive models using DataRobot’s machine learning capabilities.

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

• Load datasets into DataRobot to analyze, assess, and verify data quality.
• Build and train models to identify critical variables and meet prediction objectives.
• Interpret models to derive valuable insights that aid in business decision-making.
• Monitor and manage models to sustain optimized prediction performance.

This instructor-led live training, held in Romania (online or onsite), is designed for intermediate-level developers, data scientists, and AI practitioners looking to utilize TensorFlow Lite for Edge AI applications.

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

• Comprehend the fundamental aspects of TensorFlow Lite and its function in Edge AI.
• Build and enhance AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on a variety of edge devices.
• Apply tools and techniques for model conversion and optimization.
• Execute practical Edge AI applications leveraging TensorFlow Lite.

This instructor-led, live training in Romania (online or onsite) is designed for data scientists, data analysts, and developers who wish to explore 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 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 Romania (online or on-site) 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.
• 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 live, instructor-led training (available online or on-site) is designed for developers looking to use Google’s ML Kit to build machine learning models optimized for mobile devices.

Upon completing 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, offered in Romania (online or onsite), is tailored for data scientists and software engineers who aim to utilize Random Forest to develop machine learning algorithms for large datasets.

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

• Set up the necessary development environment to commence building machine learning models with Random Forest.
• Comprehend the advantages of Random Forest and its implementation for resolving classification and regression issues.
• Learn techniques for handling large datasets and interpreting the multiple decision trees inherent in Random Forest.
• Evaluate and optimize the performance of machine learning models through hyperparameter tuning.

This instructor-led, live training in Romania (online or onsite) is designed for data analysts at an intermediate level who want to learn how to use RapidMiner to estimate and project values, as well as to leverage analytical tools for time series forecasting.

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

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