Machine Learning Training in Stockholm

This instructor-led, live training in Stockholm (online or onsite) is aimed at beginner-level professionals who wish 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:

• 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 Stockholm (online or on-site) is designed for participants with varying levels of expertise who wish to leverage Google's AutoML platform to create 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 Stockholm (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 Stockholm (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.

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

• Understand the principles of Edge AI and its benefits.
• 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.

This instructor-led, live training in Stockholm (online or onsite) targets advanced-level AI engineers and data scientists with intermediate-to-advanced expertise who seek to improve DeepSeek model performance, reduce latency, and efficiently deploy AI solutions using contemporary MLOps practices.

Upon completing 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 across 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 completing this training, 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 that build real-world CI/CD workflows.
• Live-lab practice deploying ML workloads to Kubernetes.

Course Customization Options

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

Kubeflow is an open-source platform engineered to simplify the process of constructing, training, and deploying machine learning workloads on Kubernetes.

This instructor-led live training (available online or onsite) targets beginner to intermediate-level professionals aiming to establish robust ML workflows using Kubeflow.

After completing this training, participants will acquire the ability to:

• Navigate the Kubeflow ecosystem and its core components.
• Create reproducible workflows utilizing Kubeflow Pipelines.
• Execute scalable training jobs on Kubernetes.
• Deploy machine learning models efficiently via Kubeflow Serving.

Course Format

• Guided presentations coupled with collaborative discussions.
• Hands-on labs involving real-world Kubeflow components.
• Practical exercises designed to build end-to-end ML workflows.

Course Customization Options

• Tailored versions of this training can be organized to match your team’s technology stack and specific project requirements.

TinyML refers to the implementation of streamlined machine learning models on edge devices with limited resources.

This instructor-led training session (available online or in-person) targets advanced technical professionals aiming to design, refine, and deploy comprehensive TinyML pipelines.

Upon completing this training, participants will acquire the skills to:

• Gather, preprocess, and manage datasets tailored for TinyML solutions.
• Train and optimize models for energy-efficient microcontrollers.
• Transform models into compact formats ideal for edge devices.
• Deploy, test, and oversee TinyML applications on physical hardware.

Course Format

• Instructor-led lectures and technical discourse.
• Practical laboratory sessions and iterative experimentation.
• Direct deployment exercises on microcontroller platforms.

Customization Options

• To tailor the training with specific toolchains, hardware boards, or internal processes, please contact us to make arrangements.

This instructor-led live training in Stockholm (online or onsite) is intended for intermediate developers, data scientists, and AI professionals aiming to utilize TensorFlow Lite for Edge AI applications.

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

• Understand 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 Stockholm (online or onsite) is designed for experienced professionals aiming to gain mastery over the technologies underpinning autonomous systems.

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

• Design and deploy AI models for autonomous decision-making processes.
• Create control algorithms for autonomous navigation and obstacle detection.
• Maintain high standards of safety and reliability within AI-powered autonomous systems.
• Seamlessly integrate autonomous systems with established robotics and AI frameworks.

This instructor-led, live training in Stockholm (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding 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:

• Build 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 represents a methodology for deploying machine learning models onto low-power devices with limited resources, operating at the network edge.

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

Upon completing this course, participants will be equipped to:

• Recognize security risks specific to on-device TinyML inference.
• Deploy privacy-preserving mechanisms for edge AI solutions.
• Strengthen TinyML models and embedded systems against adversarial threats.
• Implement best practices for secure data handling in resource-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 tools and TinyML platforms.

Course Customization Options

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

This instructor-led, live training in Stockholm (online or onsite) is designed for advanced-level professionals seeking to deepen their understanding of machine learning models, refine their hyperparameter tuning skills, and master effective model deployment techniques with Google Colab.

Upon completing this training, participants will be able to:

• Implement advanced machine learning models using widely-used frameworks such as Scikit-learn and TensorFlow.
• Enhance model performance through effective hyperparameter tuning.
• Deploy machine learning models in practical, real-world scenarios using Google Colab.
• Collaborate on and manage large-scale machine learning projects within Google Colab.

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

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

• Understand how machine learning fits within AI and business strategy.
• Apply supervised and unsupervised learning techniques to structured business problems.
• Preprocess and transform data for modeling.
• Use neural networks for classification and prediction tasks.
• Perform 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 in Stockholm (online or onsite) is designed for 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 Stockholm (online or on-site) is designed for advanced professionals seeking to apply cutting-edge AI techniques to semiconductor design automation. The goal is to improve efficiency, precision, 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 Stockholm (online or on-site) is designed for intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

Upon completion 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 instructor-led, live training in Stockholm (online or onsite) is designed for intermediate-level professionals who wish to understand and apply AI techniques for optimizing semiconductor fabrication processes.

Upon completing this training, participants will be able to:

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

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

Upon completing this training, participants will be able to:

• Configure Apache Airflow for orchestrating machine learning workflows.
• Automate tasks related to data preprocessing, model training, and validation.
• Integrate Airflow with various machine learning frameworks and tools.
• Deploy machine learning models through automated pipelines.
• Monitor and optimize machine learning workflows in production environments.

This instructor-led, live training in Stockholm (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 significant resource limitations.

This guided, live training session (available online or at your location) is designed for advanced practitioners seeking to optimize TinyML models for low-latency and memory-efficient operation on embedded devices.

After completing this course, participants will be capable of:

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

Course Format

• Guided presentations complemented by technical demonstrations.
• Practical exercises in optimization and comparative performance analysis.
• Hands-on development of TinyML pipelines within a controlled lab setting.

Customization Options

• For training tailored to specific hardware platforms or internal processes, please reach out to us to customize the curriculum.

This instructor-led, live training in Stockholm (online or onsite) is designed for advanced professionals who wish to explore state-of-the-art XAI techniques for deep learning models, focusing on the development of 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 in Stockholm (online or onsite) is tailored for 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 machine learning systems.

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

After completing this course, participants will be equipped to:

• Containerize workloads for ML training, validation, and inference.
• Design and orchestrate end-to-end ML pipelines utilizing Docker and complementary tools.
• Establish versioning, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Course Format

• Interactive lectures accompanied by practical demonstrations.
• Hands-on exercises centered on constructing real-world ML pipeline components.
• Live-lab implementation for end-to-end containerized workflows.

Customization Options

• For training tailored to specific ML infrastructure requirements, please reach out to discuss available options.

This instructor-led, live training in Stockholm (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 Stockholm (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 Stockholm (online or onsite) targets intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts looking 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 Stockholm (online or onsite) is designed for cybersecurity professionals at intermediate to advanced levels who wish to enhance their expertise in AI-powered threat detection and incident response.

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

• Deploying advanced AI algorithms for real-time threat detection.
• Tailoring AI models to address specific cybersecurity challenges.
• Creating automation workflows for threat response.
• Protecting AI-driven security tools from adversarial attacks.

This instructor-led, live training in Stockholm (online or onsite) is tailored for intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completing this training, participants will be able to:

• Grasp the fundamentals of TinyML and its advantages for edge AI applications.
• Configure a development environment tailored for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Leverage TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
• Optimize AI models to meet power efficiency and memory limitations.

This instructor-led, live training in Stockholm (online or onsite) targets beginner-level cybersecurity professionals eager to learn how to leverage AI for enhanced threat detection and response capabilities.

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

• Grasp AI applications within cybersecurity.
• Deploy AI algorithms for threat detection.
• Automate incident response using AI tools.
• Integrate AI into existing cybersecurity infrastructure.

This instructor-led, live training in Stockholm (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 Stockholm (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 instructor-led, live training (online or onsite) is designed for data scientists, machine learning engineers, and computer vision researchers who want to leverage Stable Diffusion to create high-quality images for diverse use cases.

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

• Understand the core principles of Stable Diffusion and its image generation mechanism.
• Build and train Stable Diffusion models for image creation tasks.
• Apply Stable Diffusion to various image generation scenarios, including inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

This live, instructor-led training located in Stockholm (available online or on-site) is designed for beginner-level engineers and data scientists who want to learn the basics of TinyML, explore its applications, and deploy AI models on microcontrollers.

Upon completing this training, participants will be able to:

• Understand the fundamentals of TinyML and its significance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models for low-power consumption.
• Apply TinyML for real-world applications such as gesture recognition, anomaly detection, and audio processing.

This course is designed to equip participants with the practical skills necessary to apply Machine Learning techniques effectively. By leveraging the Python programming language 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 algorithm outputs, and validating results to ensure accuracy.

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

This course empowers developers and data analysts with the essential techniques required to construct 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 structures. Participants will examine proven strategies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. The program supports professionals in deploying effective ML models, assessing algorithm constraints, and delivering applied projects tailored to real-world challenges.

Deep Reinforcement Learning (DRL) integrates the core 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 contemporary AI breakthroughs, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to acquire strategies, refine policies, and execute autonomous decisions through a process of trial and error driven by reward mechanisms.

This instructor-led live training (available online or onsite) is designed for intermediate-level developers and data scientists who aim to master and apply Deep Reinforcement Learning techniques to construct intelligent agents capable of making autonomous decisions within complex environments.

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

• Grasp the theoretical foundations and mathematical concepts underlying Reinforcement Learning.
• Execute essential RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Develop and train Deep Reinforcement Learning agents utilizing TensorFlow or PyTorch.
• Deploy DRL solutions in real-world scenarios, including gaming, robotics, and decision optimization.
• Diagnose, visualize, and enhance training performance using contemporary tools.

Course Format

• Interactive lectures accompanied by guided discussions.
• Practical exercises and hands-on implementation tasks.
• Live coding demonstrations and project-based applications.

Customization Options

• To request a tailored version of this course (for instance, substituting PyTorch for TensorFlow), please get in touch with us to make arrangements.

Delving into the fundamentals of artificial intelligence uncovers how intelligent technologies are reshaping digital strategies, automation, and decision-making processes within enterprise operations. This exploration covers core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and various machine learning paradigms, alongside aspects of communication, perception, and autonomous action. It equips executives and architects to evaluate opportunities for AI-driven transformation, assess emerging technology trends, and integrate practical intelligent solutions to enhance business agility.

This course explores the application of AI—focusing on Machine Learning and Deep Learning—within the automotive sector. It guides participants in identifying which technologies can be (potentially) applied across various in-car scenarios, ranging from basic automation and image recognition to autonomous decision-making.

Spanning 8 days, this programme takes participants on a comprehensive journey, starting with robust Python engineering foundations and progressing to advanced AI system design. You will cultivate disciplined coding habits, gain mastery over statistical and deep learning techniques, and construct generative AI and agent-based systems ready for production. The emphasis is placed on reliability, evaluation, safety, and real-world deployment, moving beyond mere experimentation.

Artificial Neural Networks are computational models used to develop Artificial Intelligence (AI) systems that can perform "intelligent" tasks. These networks are widely utilized in Machine Learning (ML) applications, which represent one form of AI implementation, while Deep Learning constitutes a specific subset of Machine Learning.

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

This instructor-led, live training in Stockholm (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 Stockholm (online or onsite) is aimed at data scientists who wish to use TensorFlow to analyze potential fraud data.

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

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

Machine learning constitutes a branch of Artificial Intelligence that enables computers to learn without explicit programming.

Deep learning is a specialized subfield of machine learning that utilizes 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.

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

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

• Grasp 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.
• Develop their own deep learning customer churn prediction model using Python.

Format of the Course

• Interactive lecture and discussion.
• Abundant 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 hands-on, instructor-led training serves as a logical follow-up to the Python for Data Analysis course.

It introduces participants to the fundamental concepts of Machine Learning and demonstrates how these techniques can be directly applied to data analysis tasks, including prediction, classification, and segmentation.

The emphasis is on grasping how Machine Learning functions in real-world scenarios using well-known tools like Python, Pandas, and Jupyter Notebook, without necessitating an advanced mathematical foundation.

This instructor-led, live training in Stockholm (online or onsite) is designed for engineers who wish to evaluate current approaches and tools, allowing them to make informed decisions about adopting MLOps within their organization.

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

• Install and configure various MLOps frameworks and tools.
• Assemble a team with the appropriate skills to build and support an MLOps system.
• Prepare, validate, and version data for use by ML models.
• Understand the components of an ML Pipeline and identify the tools required to construct one.
• Experiment with different machine learning frameworks and servers for production deployment.
• Operationalize the entire Machine Learning process to ensure it is reproducible and maintainable.

This instructor-led, live training in Stockholm (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, conducted at Stockholm (online or on-site), is intended for developers and data scientists who aim to utilize TensorFlow 2.x to develop predictors, classifiers, generative models, neural networks, and related applications.

By the conclusion 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 you with a solid conceptual foundation in neural networks, covering machine learning algorithms, deep learning methodologies, and their practical applications.

Part 1 (40%) of the training focuses heavily on fundamental principles, empowering you to select the most appropriate technology for your needs, such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

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

Part 3 (40%) is extensively dedicated to TensorFlow, Google's open-source software library for Deep Learning. All examples and hands-on exercises will be conducted within the TensorFlow environment.

Audience

This course is designed for engineers who wish to utilize TensorFlow for their Deep Learning projects.

Upon completion of this course, participants will:

• possess a comprehensive understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow’s architecture and deployment mechanisms
• be capable of performing installation, production environment setup, and architectural configuration
• be able to assess code quality, perform debugging, and monitor performance
• be able to implement advanced production-level tasks, such as training models, constructing graphs, and managing logs