Machine Learning Training in Amsterdam

This instructor-led, live training in Amsterdam (online or onsite) is tailored for beginner-level professionals seeking to understand 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:

• Understand 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 Amsterdam (online or onsite) is aimed at participants with varying levels of expertise who wish 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 Amsterdam (online or onsite) is aimed at intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment.

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

• Understand the challenges and requirements of deploying AI models on edge devices.
• Apply model compression techniques to reduce the size and complexity of AI models.
• Utilize quantization methods to enhance model efficiency on edge hardware.
• Implement pruning and other optimization techniques to improve model performance.
• Deploy optimized AI models on various edge devices.

This instructor-led, live training in Amsterdam (online or onsite) is designed for intermediate developers, data scientists, and technology enthusiasts who want to develop practical skills in deploying AI models on edge devices for various applications.

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

• Grasp the fundamental principles of Edge AI and its advantages.
• Establish and configure an edge computing environment.
• Create, train, and optimize AI models for edge deployment.
• Implement functional AI solutions on edge devices.
• Assess and enhance the performance of models deployed on the edge.
• Tackle ethical and security aspects in Edge AI applications.

This instructor-led, live training in Amsterdam (online or onsite) is designed for advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and deploy AI solutions efficiently using modern MLOps practices.

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

• Optimize DeepSeek models for efficiency, accuracy, and scalability.
• Implement best practices for MLOps and model versioning.
• Deploy DeepSeek models on cloud and on-premise infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

MLOps on Kubernetes provides a framework for automating the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.

This instructor-led live training, available online or onsite, is designed for intermediate-level practitioners looking to build automated, scalable MLOps pipelines on Kubernetes.

Upon completion, participants will be able to:

• Design end-to-end CI/CD pipelines for machine learning.
• Implement GitOps workflows for model deployment and versioning.
• Automate the training, testing, and packaging of ML models.
• Integrate monitoring, alerting, and rollback strategies.

Course Format

• Instructor-guided presentations and technical deep dives.
• Hands-on exercises to build real-world CI/CD workflows.
• Live-lab practice for deploying ML workloads to Kubernetes.

Course Customization Options

• Organizations can request tailored content aligned with their internal MLOps tools and infrastructure.

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.

TinyML involves deploying optimized machine learning models onto edge devices with limited resources.

This guided, live training session (available online or on-site) targets advanced technical professionals looking to design, optimize, and deploy complete TinyML pipelines.

Upon completing this course, participants will be able to:

• Gather, prepare, and manage datasets for TinyML applications.
• Train and optimize models specifically for low-power microcontrollers.
• Convert models into lightweight formats suitable for edge devices.
• Deploy, test, and monitor TinyML applications on actual hardware.

Course Format

• Guided lectures and technical discussions led by an instructor.
• Practical labs and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Customization Options

• To tailor the training to specific toolchains, hardware boards, or internal workflows, please contact us to arrange.

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

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

• Understand the core principles of TensorFlow Lite and its function within Edge AI.
• Construct and refine AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on a variety of edge devices.
• Employ tools and techniques for model conversion and optimization.
• Build practical Edge AI applications leveraging TensorFlow Lite.

This instructor-led, live training in Amsterdam (online or on-site) is aimed at advanced-level professionals who wish to master the technologies behind autonomous systems.

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

• Design and implement AI models for autonomous decision-making.
• Develop control algorithms for autonomous navigation and obstacle avoidance.
• Ensure safety and reliability in AI-driven autonomous systems.
• Integrate autonomous systems with existing robotics and AI frameworks.

This instructor-led, live training in Amsterdam (online or on-site) is aimed at experienced professionals who wish to deepen their knowledge of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the end 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.

TinyML involves deploying machine learning models on low-power, resource-constrained devices at the network edge.

This instructor-led live training (available online or onsite) is designed for advanced professionals seeking to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications.

Upon completing this course, participants will be capable of:

• Recognizing security risks specific to on-device TinyML inference.
• Deploying privacy-preserving mechanisms for edge AI solutions.
• Strengthening TinyML models and embedded systems against adversarial threats.
• Applying best practices for secure data management in constrained environments.

Course Format

• Interactive lectures complemented by expert-led discussions.
• Practical exercises focused on real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tools.

Customization Options

• Organizations can request a customized version of this training to meet their specific security and compliance requirements.

This instructor-led, live training in Amsterdam (online or onsite) is designed for advanced professionals seeking to deepen their knowledge of machine learning models, improve their hyperparameter tuning expertise, and learn how to effectively deploy models using Google Colab.

By the end 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 Amsterdam (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.

TinyML represents a framework designed to deploy machine learning models onto low-power microcontrollers and embedded platforms utilized in robotics and autonomous systems.

This instructor-led, live training (available online or onsite) is tailored for advanced-level professionals seeking to incorporate TinyML-driven perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

Upon completing this course, participants will be able to:

• Design optimized TinyML models specifically for robotics applications.
• Implement on-device perception pipelines to enable real-time autonomy.
• Integrate TinyML solutions into existing robotic control frameworks.
• Deploy and test lightweight AI models on embedded hardware platforms.

Format of the Course

• Technical lectures combined with interactive discussions.
• Hands-on labs focusing on embedded robotics tasks.
• Practical exercises simulating real-world autonomous workflows.

Course Customization Options

• For organization-specific robotics environments, customization can be arranged upon request.

This instructor-led live training in Amsterdam (online or onsite) 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:

• Understand how machine learning aligns with AI 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 machine learning methods.
• Implement clustering and association rule mining for customer segmentation and pattern discovery.

This instructor-led, live training in Amsterdam (online or onsite) targets intermediate-level professionals who aim to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to enhance production efficiency and reduce unexpected equipment failures.

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

• Implement AI models for predicting equipment failures in semiconductor manufacturing.
• Analyze maintenance data to identify patterns and trends indicative of potential issues.
• Integrate AI-driven predictive maintenance into existing manufacturing workflows.
• Reduce downtime and maintenance costs through proactive equipment management.

This instructor-led, live training in Amsterdam (online or onsite) is designed for advanced professionals who want to apply state-of-the-art AI techniques to semiconductor design automation, thereby improving efficiency, accuracy, and innovation in chip design and verification.

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

• Utilize advanced AI techniques to optimize semiconductor design workflows.
• Integrate machine learning models into EDA tools to strengthen design verification.
• Create AI-driven solutions for complex design challenges encountered in chip fabrication.
• Apply neural networks to enhance the speed and accuracy of design automation.

This instructor-led live training in Amsterdam (online or onsite) is designed for 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:

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

This guided, live training in Amsterdam (online or on-site) is designed for intermediate-level professionals who aim to understand and apply AI techniques for optimizing semiconductor fabrication processes.

Upon completing this course, participants will be capable of:

• Grasping AI methodologies used for process optimization in chip manufacturing.
• Deploying AI models to improve yield rates and minimize defects.
• Examining process data to pinpoint critical parameters for improvement.
• Utilizing machine learning techniques to adjust semiconductor production processes precisely.

This instructor-led, live training in Amsterdam (online or onsite) is aimed at 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 Amsterdam (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.

TinyML involves the deployment of machine learning models onto hardware with severe resource limitations.

This guided, live training session (available online or on-site) targets advanced professionals looking to optimize TinyML models for low-latency and memory-efficient execution on embedded devices.

After finishing this course, participants will be capable of:

• Utilizing quantization, pruning, and compression methods to decrease model size while maintaining accuracy.
• Assessing TinyML models for latency, memory usage, and energy efficiency.
• Deploying optimized inference pipelines on microcontrollers and edge devices.
• Analyzing the trade-offs between performance, accuracy, and hardware limitations.

Course Format

• Instructor-led lectures complemented by technical demonstrations.
• Practical exercises focused on optimization and comparative performance testing.
• Hands-on creation of TinyML pipelines within a controlled lab setting.

Customization Options for the Course

• To receive training tailored to specific hardware platforms or internal processes, please reach out to us for program customization.

This instructor-led, live training in Amsterdam (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems.

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

• Understand the challenges of explainability in deep learning.
• Implement advanced XAI techniques for neural networks.
• Interpret decisions made by deep learning models.
• Evaluate the trade-offs between performance and transparency.

This instructor-led, live training session in Amsterdam (online or onsite) is aimed at beginner-level professionals who wish to understand and apply AI technologies within the semiconductor manufacturing industry.

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

• Understand the basic principles of AI and how they apply to semiconductor manufacturing.
• Identify areas within semiconductor manufacturing where AI can be effectively implemented.
• Utilize AI tools and techniques to enhance production efficiency and quality control.
• Implement basic AI models to optimize manufacturing processes.

Docker serves as a containerization platform designed to build reproducible, portable, and scalable environments for ML systems.

This instructor-led live training (available online or onsite) targets intermediate to advanced technical professionals who aim to containerize and operationalize complete ML pipelines using Docker.

After completing this training, participants will be able to:

• Containerize ML training, validation, and inference workloads.
• Design and orchestrate end-to-end ML pipelines using Docker and supporting tools.
• Implement versioning, reproducibility, and CI/CD for ML components.
• Deploy, monitor, and scale ML services in containerized environments.

Course Format

• Interactive lectures supported by practical demonstrations.
• Hands-on exercises focused on building real ML pipeline components.
• Live-lab implementation for end-to-end containerized workflows.

Course Customization Options

• For customized training aligned with specific ML infrastructure needs, please contact us to discuss options.

This instructor-led live training in Amsterdam (online or on-site) targets data scientists and developers seeking 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 Amsterdam (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 Amsterdam (online or onsite) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish 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 Amsterdam (online or onsite) is aimed at intermediate to advanced cybersecurity professionals who wish to enhance their skills in AI-driven threat detection and incident response.

By the conclusion 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 Amsterdam (online or onsite) targets intermediate-level embedded systems engineers and AI developers aiming to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completing this training, participants will be capable of:

• Grasping the core principles of TinyML and its advantages for edge AI applications.
• Configuring a development environment suitable for TinyML projects.
• Training, optimizing, and deploying AI models on low-power microcontrollers.
• Leveraging TensorFlow Lite and Edge Impulse to build practical TinyML solutions.
• Enhancing AI models for improved power efficiency and adherence to memory limitations.

This instructor-led live training in Amsterdam (online or onsite) targets cybersecurity professionals at the beginner level who want to learn how to use AI to enhance their threat detection and response capabilities.

Upon completing this training, participants will be able to:

• Grasp how AI is applied in cybersecurity.
• Deploy AI algorithms for identifying threats.
• Automate incident response using AI tools.
• Incorporate AI into current cybersecurity infrastructure.

This instructor-led live training in Amsterdam (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 suitable machine learning algorithms, and improve model construction and performance.
• Utilize RapidMiner to estimate and project values, and employ analytical tools for time series forecasting.

This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

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

• Understand the principles of Stable Diffusion and how it works for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

This guided, live training in Amsterdam (online or in person) is designed for engineers and data scientists at the beginner level who want to grasp the core principles of TinyML, investigate its practical uses, and deploy AI models on microcontrollers.

Upon completion of this training, participants will be capable of:

• Grasping the core concepts of TinyML and its importance.
• Deploying compact AI models on microcontrollers and edge devices.
• Optimizing and refining machine learning models to minimize power usage.
• Utilizing TinyML for real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

This course aims to build general proficiency in applying Machine Learning methods in real-world scenarios. By leveraging the Python programming language and its extensive library ecosystem, along with a wide range of practical examples, this course teaches you how to utilize the core components of Machine Learning. You will learn to make informed data modeling decisions, interpret algorithm outputs, and validate results effectively.

Our objective is to equip you with the confidence to understand and apply the most fundamental tools in the Machine Learning toolbox, while helping you avoid common pitfalls associated with Data Science applications.

Applied AI from Scratch in Python provides programmers and data analysts with the foundational techniques needed to build machine learning solutions from the ground up using Python. The course covers core principles of supervised learning (classification and regression), unsupervised learning (clustering and anomaly detection), and advanced neural network architectures. It examines proven methods for working with scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. This training helps professionals implement practical ML models, evaluate algorithm limitations, and complete applied projects aimed at solving real-world problems.

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning architectures, empowering agents to make informed decisions through interaction with their surroundings. This technology drives numerous contemporary AI breakthroughs, including self-driving cars, robotic control systems, algorithmic trading platforms, and adaptive recommendation engines. DRL enables artificial agents to acquire strategies, refine policies, and execute autonomous choices by leveraging trial-and-error mechanisms grounded in reward-based learning.

This instructor-led live training, available online or on-site, targets intermediate developers and data scientists eager to master and apply Deep Reinforcement Learning techniques for constructing intelligent agents capable of making autonomous decisions within complex environments.

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

• Grasp the theoretical foundations and mathematical underpinnings of Reinforcement Learning.
• Implement core RL algorithms such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents utilizing TensorFlow or PyTorch.
• Apply DRL to practical scenarios including games, robotics, and decision optimization tasks.
• Diagnose issues, visualize results, and optimize training performance using contemporary tools.

Format of the Course

• Interactive lectures accompanied by guided discussions.
• Practical exercises and real-world implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course (for example, using PyTorch instead of TensorFlow), please contact us to arrange.

An exploration of artificial intelligence fundamentals illustrates how intelligent technologies are transforming digital strategies, automation, and decision-making processes across enterprise operations. This curriculum covers essential concepts such as the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and various machine learning paradigms, alongside areas like communication, perception, and autonomous action. It guides executives and architects in evaluating opportunities for AI-driven transformation, assessing emerging technology trends, and integrating practical intelligent solutions to enhance business agility.

This course provides an overview of AI applications within the automotive industry, with a specific focus on Machine Learning and Deep Learning. It explores how various technologies can be applied across different scenarios inside a vehicle, ranging from basic automation and image recognition to autonomous decision-making processes.

This eight-day programme offers a comprehensive journey, taking participants from robust Python engineering principles to the design of advanced AI systems. Learners will cultivate disciplined coding habits, gain mastery over statistical and deep learning techniques, and construct production-grade generative AI and agent-based solutions. The curriculum emphasizes reliability, evaluation, safety, and real-world deployment, moving beyond mere experimentation.

An Artificial Neural Network is a computational data model employed in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are frequently utilized in Machine Learning (ML) applications, which constitute one implementation of AI. Deep Learning represents a specialized subset of Machine Learning.

Enhance your data science capabilities through this comprehensive Machine Learning training program. The course covers essential algorithms such as Naive Bayes, Decision Trees, Neural Networks, Support Vector Machines, and Clustering techniques. You will gain practical experience by combining theoretical foundations with real-world applications. This course is ideal for data analysts, software engineers, AI enthusiasts, and business professionals looking to implement machine learning solutions. You will master classification performance metrics, cross-validation strategies, the bias-variance trade-off, and deep learning fundamentals to develop robust predictive models.

This instructor-led, live training in Amsterdam (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 Amsterdam (online or onsite) is intended for data scientists aiming to utilize TensorFlow for the analysis of potential fraud data.

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

• Creating a fraud detection model using Python and TensorFlow.
• Building linear regressions and linear regression models to predict fraud.
• Developing an end-to-end AI application for analyzing fraud data.

Machine learning is a discipline within Artificial Intelligence that enables computers to learn from data without being explicitly programmed.

Deep learning represents a subfield of machine learning, utilizing 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 high code readability.

In this instructor-led live training, participants will learn how to implement deep learning models for the telecom sector using Python, guided by the step-by-step creation of a deep learning credit risk model.

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

• Grasp the fundamental concepts of deep learning.
• Explore the applications and uses of deep learning within the telecom industry.
• Employ Python, Keras, and TensorFlow to develop deep learning models for telecom scenarios.
• Construct a custom deep learning customer churn prediction model using Python.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation within a live-lab environment.

Course Customization Options

• To request customized training for this course, please contact us to make arrangements.

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

The module introduces the fundamental principles of Machine Learning and demonstrates their direct application to data analysis activities, including prediction, classification, and segmentation.

The emphasis is on grasping how Machine Learning functions in real-world scenarios by utilizing well-known tools like Python, Pandas, and Jupyter Notebook. No advanced mathematical expertise is required.

This instructor-led, live training in Amsterdam (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.

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

• Install and configure various MLOps frameworks and tools.
• Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
• Prepare, validate and version data for use by ML models.
• Understand the components of an ML Pipeline and the tools needed to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.

This instructor-led live training in Amsterdam (online or onsite) is designed for intermediate-level data analysts, developers, or aspiring data scientists who want to leverage 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 Amsterdam (online or onsite) targets developers and data scientists wishing to utilize TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and similar 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 foundational knowledge of neural networks, machine learning algorithms, and deep learning (both algorithms and applications).

Part 1 (40%) of this training focuses on fundamentals but also assists you in selecting the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part 2 (20%) introduces Theano - a Python library that simplifies writing deep learning models.

Part 3 (40%) of the training will be extensively based on TensorFlow - Google's open-source software library API for Deep Learning. All examples and hands-on exercises will be conducted in TensorFlow.

Audience

This course is intended for engineers seeking to utilize TensorFlow for their Deep Learning projects.

Upon completion, delegates will be able to:

• Gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs.
• Comprehend TensorFlow’s structure and deployment mechanisms.
• Perform installation, production environment setup, architecture tasks, and configuration.
• Assess code quality, perform debugging, and monitoring.
• Implement advanced production-like training models, building graphs, and logging.