Introduction to Machine Learning 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 course provides a comprehensive overview of AI, with a focus on Machine Learning and Deep Learning, applied within the automotive industry. It helps participants identify which technologies can be (potentially) utilized across various scenarios in vehicles: ranging from basic automation and image recognition to autonomous decision-making.

A deep dive into the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategy, automation, and decision-making across enterprise operations. This course examines core concepts such as the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, along with aspects of communication, perception, and autonomous action. It guides executives and architects in evaluating AI-driven transformation opportunities, assessing emerging technology trends, and integrating practical intelligent solutions to enhance business agility.

This instructor-led, live training in Turkey (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 the creation of Artificial Intelligence (AI) systems that can execute "intelligent" tasks. Neural Networks are frequently applied within Machine Learning (ML) environments, which serve as one of the primary implementations of AI. Deep Learning represents a specialized subset of Machine Learning.

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) targets intermediate-level developers, data scientists, and AI practitioners who intend to leverage TensorFlow Lite for Edge AI applications.

Upon completing this training, participants will be able to:

• Grasp the fundamentals of TensorFlow Lite and its function in Edge AI.
• Create and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models across various edge devices.
• Apply tools and techniques for model conversion and optimization.
• Develop practical Edge AI applications using TensorFlow Lite.

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.

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 Turkey (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, 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.

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.