Machine Learning Training in Istanbul

This instructor-led, live training session in Istanbul (online or on-site) is tailored for beginner-level professionals who want to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.

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

• Grasp the concept and benefits of pre-trained models.
• Explore various pre-trained model architectures and their use cases.
• Fine-tune a pre-trained model for specific tasks.
• Implement pre-trained models in simple machine learning projects.

This instructor-led, live training in Istanbul (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 guided, live training in Istanbul (online or in-person) is designed for AI developers, machine learning engineers, and system architects at an intermediate level who aim to adapt AI models for edge deployment.

Upon completing this training, participants will be equipped to:

• Grasp the challenges and prerequisites associated with deploying AI models on edge devices.
• Employ model compression strategies to decrease the size and intricacy of AI models.
• Leverage quantization approaches to boost model efficiency on edge hardware.
• Execute pruning and other optimization methods to enhance model effectiveness.
• Roll out optimized AI models across diverse edge devices.

This instructor-led live training in Istanbul (offered online or onsite) is tailored for intermediate-level developers, data scientists, and tech enthusiasts seeking practical expertise in deploying AI models on edge devices for diverse applications.

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

• Understand the fundamental principles and benefits of Edge AI.
• Set up and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

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 Istanbul (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 Istanbul (online or on-site) targets advanced professionals who wish to master the technologies behind autonomous systems.

Upon completing this training, participants will be capable of:

• Designing and implementing AI models for autonomous decision-making.
• Creating control algorithms for autonomous navigation and obstacle avoidance.
• Ensuring safety and reliability within AI-powered autonomous systems.
• Integrating autonomous systems with established robotics and AI frameworks.

This instructor-led, live training in Istanbul (available online or onsite) is designed for advanced professionals aiming to expand their expertise in computer vision and investigate TensorFlow's potential for creating sophisticated vision models via Google Colab.

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

• Construct and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led, live training in Istanbul (online or onsite) is designed for advanced professionals seeking to enhance their expertise in machine learning models, improve hyperparameter tuning skills, and learn effective model deployment using Google Colab.

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

• Build advanced machine learning models using popular frameworks like Scikit-learn and TensorFlow.
• Optimize model performance through hyperparameter tuning.
• Deploy machine learning models in real-world applications using Google Colab.
• Collaborate and manage large-scale machine learning projects in Google Colab.

This instructor-led, live training in Istanbul (online or onsite) is aimed at intermediate-level professionals who wish to apply AI techniques to optimize yield management in semiconductor manufacturing.

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

• Analyze production data to identify factors affecting yield rates.
• Implement AI algorithms to enhance yield management processes.
• Optimize production parameters to reduce defects and improve yields.
• Integrate AI-driven yield management into existing production workflows.

This instructor-led, live training in Istanbul (online or on-site) targets intermediate-level business and AI professionals who wish to apply machine learning in business, forecasting, and AI-driven systems using real case studies and Python-based tools.

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

• Comprehend the role of machine learning within artificial intelligence and business strategy.
• Apply supervised and unsupervised learning techniques to structured business problems.
• Preprocess and transform data for modeling purposes.
• Utilize neural networks for classification and prediction tasks.
• Conduct sales forecasting using statistical and ML-based methods.
• Implement clustering and association rule mining for customer segmentation and pattern discovery.

This instructor-led live training, located in Istanbul (available online or onsite), is intended for advanced professionals who want to apply cutting-edge AI techniques to semiconductor design automation, thereby enhancing efficiency, accuracy, and innovation in chip design and verification.

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

• Apply advanced AI techniques to optimize semiconductor design processes.
• Integrate machine learning models into EDA tools for enhanced design verification.
• Develop AI-driven solutions for complex design challenges in chip fabrication.
• Leverage neural networks for improving the accuracy and speed of design automation.

This instructor-led, live training in Istanbul (online or onsite) targets intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

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

• Configure and navigate Google Colab for deep learning projects.
• Grasp the core principles of neural networks.
• Develop deep learning models utilizing TensorFlow.
• Train and assess deep learning models.
• Leverage advanced TensorFlow features for deep learning applications.

This instructor-led, live training in Istanbul (online or in-person) targets intermediate-level professionals eager to understand and apply AI methods for optimizing semiconductor fabrication processes.

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

• Comprehend AI methodologies for process optimization in chip fabrication.
• Deploy AI models to boost yield and reduce defects.
• Analyze process data to identify key parameters for optimization.
• Apply machine learning techniques to fine-tune semiconductor manufacturing processes.

This instructor-led, live training in Istanbul (online or onsite) targets intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

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

• Set up Apache Airflow for machine learning workflow orchestration.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimize machine learning workflows in production.

This instructor-led live training in Istanbul (online or onsite) is aimed at intermediate-level data scientists and developers who wish to apply machine learning algorithms efficiently using the Google Colab environment.

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

• Set up and navigate Google Colab for machine learning projects.
• Understand and apply various machine learning algorithms.
• Use libraries like Scikit-learn to analyze and predict data.
• Implement supervised and unsupervised learning models.
• Optimize and evaluate machine learning models effectively.

Designed for advanced professionals seeking to apply state-of-the-art XAI techniques to deep learning models and build interpretable AI systems, this instructor-led live training is available both online and onsite.

Upon completing this training, participants will be able to:

• Grasp the challenges associated with explainability in deep learning.
• Apply advanced XAI techniques to neural networks.
• Interpret the decisions generated by deep learning models.
• Assess the trade-offs between model performance and transparency.

This instructor-led, live training in Istanbul (online or onsite) is aimed at data scientists and developers who wish to use ML.NET machine learning models to automatically derive projections from executed data analysis for enterprise applications.

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

• Install ML.NET and integrate it into the application development environment.
• Understand the machine learning principles behind ML.NET tools and algorithms.
• Build and train machine learning models to perform predictions with the provided data smartly.
• Evaluate the performance of a machine learning model using the ML.NET metrics.
• Optimize the accuracy of the existing machine learning models based on the ML.NET framework.
• Apply the machine learning concepts of ML.NET to other data science applications.

This instructor-led, live training in Istanbul (online or onsite) is aimed at intermediate-level data professionals who wish to apply machine learning techniques to data-driven business problems, including sales forecasting and predictive modeling using neural networks.

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

• Understand the core concepts and types of machine learning.
• Apply key algorithms for classification, regression, clustering, and association analysis.
• Perform exploratory data analysis and data preparation using Python.
• Use neural networks for nonlinear modeling tasks.
• Implement predictive analytics for business forecasting, including sales data.
• Evaluate and optimize model performance using visual and statistical techniques.

This instructor-led, live training in Istanbul (online or onsite) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision experts aiming to expand their knowledge and skills in deep learning for text-to-image generation.

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

• Understand advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimizations for high-quality image synthesis.
• Optimize performance and scalability for large datasets and complex models.
• Tune hyperparameters for better model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools.

This instructor-led, live training in Istanbul (online or onsite) targets intermediate to advanced cybersecurity professionals seeking to enhance their skills in AI-driven threat detection and incident response.

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

• Implement advanced AI algorithms for real-time threat detection.
• Customize AI models for specific cybersecurity challenges.
• Develop automation workflows for threat response.
• Secure AI-driven security tools against adversarial attacks.

This instructor-led, live training in Istanbul (online or onsite) is designed for cybersecurity professionals at the beginner level who aim to learn how to use AI to improve threat detection and response capabilities.

Upon completing this training, participants will be able to:

• Comprehend the role of AI in cybersecurity.
• Deploy AI algorithms for the purpose of threat detection.
• Utilize AI tools to automate incident response procedures.
• Incorporate AI into current cybersecurity frameworks.

This instructor-led, live training in Istanbul (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.

This instructor-led live training in Istanbul (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.

This course aims to equip participants with practical proficiency in applying Machine Learning techniques. Leveraging Python and its extensive ecosystem of libraries, along with numerous hands-on examples, learners will master the essential components of Machine Learning. The curriculum covers making informed data modeling decisions, interpreting algorithmic outputs, and validating results effectively.

Our objective is to empower you with the confidence to utilize fundamental Machine Learning tools while steering clear of common pitfalls associated with Data Science applications.

This course empowers programmers and data analysts with the essential techniques for constructing machine learning solutions from the ground up using Python. It explores the fundamental principles of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network architectures. The curriculum reviews established practices for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. It enables professionals to implement effective ML models, assess algorithm constraints, and execute applied projects designed to solve real-world challenges.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make intelligent decisions through interaction with their surroundings. This technology is foundational to numerous modern AI breakthroughs, including autonomous vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. By leveraging reward-based learning, DRL enables artificial agents to refine strategies, optimize policies, and execute autonomous decisions via a trial-and-error process.

This instructor-led live training, available online or onsite, targets intermediate-level developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. The goal is to equip participants with the skills to build intelligent agents capable of autonomous decision-making within complex environments.

Upon completing this training, participants will be able to:

• Grasp the theoretical foundations and mathematical principles underpinning Reinforcement Learning.
• Implement core RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL techniques to practical domains like gaming, robotics, and decision optimization.
• Diagnose, visualize, and enhance training performance using contemporary tools.

Course Format

• Interactive lectures paired with guided discussions.
• Practical exercises and hands-on implementation projects.
• Live coding demonstrations and application-focused projects.

Customization Options

• For customized course variations (such as switching from TensorFlow to PyTorch), please reach out to us to make arrangements.

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 Istanbul (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.

Over the course of eight days, this program guides participants through a comprehensive journey, starting with robust Python engineering principles and advancing to sophisticated AI system architecture. Attendees will cultivate disciplined coding habits, gain expertise in statistical and deep learning methodologies, and construct generative AI and agent-driven systems ready for industrial deployment. The curriculum prioritizes reliability, assessment, security, and practical implementation over mere experimentation.

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.

Enhance your data science proficiency with this in-depth Machine Learning training program, which covers essential algorithms such as Naive Bayes, Decision Trees, Neural Networks, Support Vector Machines, and Clustering methods. Acquire practical skills alongside theoretical knowledge through real-world case studies. This course is perfect for data analysts, software developers, AI enthusiasts, and business leaders looking to implement machine learning solutions. Learn to master classification performance metrics, cross-validation, the bias-variance trade-off, and deep learning fundamentals to develop strong predictive models.

This instructor-led live training in Istanbul (online or onsite) is targeted at researchers and developers who wish to utilize Chainer to build and train neural networks in Python, ensuring the code is easy to debug.

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

• Set up the necessary development environment to start developing neural network models.
• Define and implement neural network models using comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

This instructor-led, live training in Istanbul (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 Istanbul (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 Istanbul (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

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

• Install the OpenVINO toolkit.
• Accelerate a computer vision application using an FPGA.
• Execute different CNN layers on the FPGA.
• Scale the application across multiple nodes in a Kubernetes cluster.

This instructor-led, live training in Istanbul (online or onsite) targets data scientists aiming to use TensorFlow for analyzing potential fraud data.

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

• Create a fraud detection model using Python and TensorFlow.
• Build linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

Machine learning represents a subset of Artificial Intelligence that enables computers to learn from data without explicit programming.

Deep learning, a specialized area within machine learning, employs methods based on learning data representations and structures, such as neural networks.

Python is a high-level programming language renowned for its clear syntax and code readability.

During this instructor-led live training, participants will learn how to implement deep learning models for the telecom sector by stepping through the creation of a deep learning credit risk model.

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

• Comprehend the fundamental concepts of deep learning.
• Understand the applications and uses of deep learning in telecom.
• Utilize Python, Keras, and TensorFlow to create deep learning models for telecom.
• Construct their own deep learning customer churn prediction model using Python.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Istanbul (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 Istanbul (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

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

• Set up the necessary development environment to start running deep learning trainings.
• Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
• Scale deep learning training with Horovod to run on multiple GPUs.

This hands-on, instructor-led training serves as a logical follow-up to the Python for Data Analysis course.

It introduces participants to the fundamental principles of Machine Learning and demonstrates how to apply them directly to data analysis tasks such as prediction, classification, and segmentation.

The emphasis is on grasping how Machine Learning functions in real-world scenarios, utilizing accessible tools like Python, Pandas, and Jupyter Notebook, without the need for an advanced mathematical foundation.

This course is for people that already have a background in data science and statistics. The explanations given are designed to either serve as a reminder to those that are already familiar with the concepts or inform those with a suitable background.

This instructor-led live training in Istanbul (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 training course is designed for individuals seeking to implement fundamental Machine Learning techniques in real-world applications.

Audience

Data scientists and statisticians who possess some familiarity with machine learning and proficiency in programming with R. This course emphasizes the practical dimensions of data and model preparation, execution, post-hoc analysis, and visualization. Its goal is to provide a hands-on introduction to machine learning for participants interested in applying these methods within their professional work.

Industry-specific examples are utilized to ensure the training content is relevant to the audience.

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 course provides an introduction to machine learning methodologies applied within robotics.

It offers a comprehensive survey of current techniques, their underlying motivations, and core concepts, specifically framed within the domain of pattern recognition.

Following a brief theoretical foundation, participants will engage in practical exercises utilizing open-source tools, predominantly R, or other widely-used software platforms.

This instructor-led, live training in Istanbul (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

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

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.

This instructor-led, live training in Istanbul (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 Istanbul (online or onsite) is designed for developers and data scientists who intend to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and related applications.

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

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile and IoT devices.

This course provides a comprehensive conceptual foundation in neural networks, machine learning algorithms, and deep learning (including algorithms and their practical applications).

Part 1 (40%) of the training focuses on fundamentals, assisting you in selecting the appropriate technology, such as TensorFlow, Caffe, Theano, DeepDrive, and Keras.

Part 2 (20%) introduces Theano, a Python library designed to simplify the creation of deep learning models.

Part 3 (40%) is extensively dedicated to TensorFlow, the open-source software library for Deep Learning developed by Google. All examples and hands-on exercises will utilize TensorFlow.

Audience

This course is designed for engineers looking to implement TensorFlow in their Deep Learning projects.

Upon completing this course, participants will:

• possess a solid understanding of Deep Neural Networks (DNN), CNNs, and RNNs
• comprehend TensorFlow's architecture and deployment mechanisms
• be capable of handling installation, production environment setup, architecture design, and configuration
• be able to assess code quality, perform debugging, and monitor performance
• be able to implement advanced production tasks, including model training, graph construction, and logging