Instructor-led live Machine Learning (ML) training courses, available both online and onsite, use hands-on practice to show how to apply machine learning techniques and tools to solve real-world problems across various industries. NobleProg ML courses cover different programming languages and frameworks, including Python, R language and Matlab. Machine Learning courses are offered for a number of industry applications, including Finance, Banking and Insurance and cover the fundamentals of Machine Learning as well as more advanced approaches such as Deep Learning.
Machine Learning training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Machine Learning training can be carried out locally on customer premises in Sofia or in NobleProg corporate training centers in Sofia.
NobleProg -- Your Local Training Provider
Crystal Business Center
ул. "Осогово" 40, Sofia, Bulgaria, 1303
Crystal Business Center is located in the central part of Sofia, on the corner of "Osogovo" street. and "Todor Aleksandrov" blvd. The building is easily accessible by metro (only 50 m from Opalchenska station) and other public transport. Its total area is 8000 sq.m. The office area is 6171 sq.m.
This instructor-led, live training in Sofia (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 Sofia (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 Sofia (online or onsite) is tailored for intermediate-level AI developers, machine learning engineers, and system architects who seek to optimize AI models for edge deployment.
Upon completion of this training, participants will be able to:
Comprehend the challenges and requirements associated with deploying AI models on edge devices.
Apply model compression techniques to decrease the size and complexity of AI models.
Leverage quantization methods to boost model efficiency on edge hardware.
Implement pruning and additional optimization techniques to enhance model performance.
Deploy optimized AI models across various edge devices.
This instructor-led, live training conducted in Sofia (online or onsite) is tailored for intermediate-level developers, data scientists, and tech enthusiasts aiming to acquire practical skills in deploying AI models on edge devices for diverse applications.
By the conclusion 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.
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 Sofia (online or onsite) is designed for advanced professionals seeking to master the technologies underlying autonomous systems.
Upon completion 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-powered autonomous systems.
Integrate autonomous systems with existing robotics and AI frameworks.
This instructor-led live training in Sofia (online or onsite) is designed for advanced professionals who wish to deepen their knowledge of machine learning models, improve their hyperparameter tuning skills, and learn how to effectively deploy models using Google Colab.
By the end of this training, participants will be able to:
Implement 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 Sofia (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 Sofia (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:
Grasp how machine learning integrates with AI and business strategy.
Apply supervised and unsupervised learning techniques to solve structured business problems.
Preprocess and transform data for modeling purposes.
Utilize neural networks for classification and prediction tasks.
Conduct sales forecasting using both statistical and ML-based methods.
Implement clustering and association rule mining for customer segmentation and pattern discovery.
This instructor-led, live training in Sofia (online or onsite) targets advanced professionals seeking to apply state-of-the-art AI techniques to semiconductor design automation, thereby enhancing efficiency, accuracy, and innovation in chip design and verification.
Upon completion of this training, participants will be equipped to:
Utilize advanced AI techniques to optimize semiconductor design workflows.
Integrate machine learning models into EDA tools to improve design verification.
Create AI-powered solutions for complex design challenges in chip fabrication.
Harness neural networks to boost the speed and accuracy of design automation.
This instructor-led, live training in Sofia (online or on-site) is designed for intermediate-level professionals seeking to understand and apply AI techniques for optimizing semiconductor fabrication processes.
By the conclusion of this training, participants will be capable of:
Understanding AI methodologies for process optimization in chip fabrication.
Implementing AI models to enhance yield and reduce defects.
Analyzing process data to identify key parameters for optimization.
Applying machine learning techniques to fine-tune semiconductor manufacturing processes.
This instructor-led live training, delivered Sofia (online or onsite), is designed for intermediate-level participants who wish to automate and manage machine learning workflows. The curriculum covers model training, validation, and deployment using Apache Airflow.
Upon completion of this training, participants will be equipped to:
Configure Apache Airflow specifically for orchestrating machine learning workflows.
Automate essential tasks such as data preprocessing, model training, and validation.
Seamlessly integrate Airflow with various machine learning frameworks and tools.
Deploy machine learning models through the use of automated pipelines.
Monitor and optimize machine learning workflows within production environments.
This instructor-led, live training in Sofia (online or onsite) is designed for intermediate-level data scientists and developers who want to efficiently apply machine learning algorithms 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.
This instructor-led live training in Sofia (online or onsite) is designed for data scientists and developers who intend to use ML.NET machine learning models to automatically derive projections from 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 Sofia (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:
Grasp the fundamental concepts and categories of machine learning.
Utilize essential algorithms for classification, regression, clustering, and association analysis.
Conduct exploratory data analysis and prepare data using Python.
Leverage neural networks for nonlinear modeling tasks.
Deploy predictive analytics for business forecasting, including sales data.
Assess and enhance model performance through visual and statistical techniques.
This instructor-led, live training in Sofia (online or onsite) is aimed at intermediate-level to advanced-level cybersecurity professionals who wish to elevate 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 Sofia (online or onsite) is designed for beginner-level cybersecurity professionals eager to learn how to utilize 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 identification.
Automate incident response using AI tools.
Incorporate AI into current cybersecurity infrastructure.
This instructor-led, live training Sofia (online or on-site) is designed for biologists who wish to understand how AlphaFold functions and apply its 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 session (available online or on-site) is designed for technical professionals with a background in machine learning who seek to optimize models for detecting complex patterns in big data utilizing AutoML frameworks.
This instructor-led, live training in Sofia (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 is designed to help participants gain foundational skills in applying Machine Learning techniques in real-world scenarios. Utilizing the Python programming language and its extensive ecosystem of libraries, along with numerous practical examples, the course covers the essential components of Machine Learning. Participants will learn how to make informed decisions about data modeling, interpret algorithm outputs, and validate results effectively.
Our objective is to equip you with the confidence to understand and utilize key tools from the Machine Learning toolkit, while helping you avoid common pitfalls associated with Data Science applications.
This course is designed to equip participants with the essential skills needed to effectively apply Machine Learning techniques in real-world scenarios. Utilizing Python and its extensive ecosystem of libraries, alongside numerous hands-on examples, the curriculum guides learners through the core components of Machine Learning. It focuses on making informed decisions during data modeling, interpreting algorithm outputs, and validating results.
Our objective is to empower you with the confidence to leverage fundamental Machine Learning tools and help you steer clear of common pitfalls associated with Data Science applications.
This instructor-led live training in Sofia (online or onsite) is designed for data scientists and software engineers who aim 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.
Over the course of eight days, this programme offers a comprehensive journey from robust Python engineering principles to the design of advanced AI systems. Participants will cultivate disciplined coding habits, gain expertise in statistical and deep learning techniques, and construct generative AI and agent-based systems ready for production environments. The curriculum emphasizes reliability, evaluation, safety, and real-world deployment, moving beyond mere experimentation.
This instructor-led, live training in Sofia (online or onsite) is designed for data scientists as well as less technical participants who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.
By the end of this training, participants will be able to:
Automate the process of training highly efficient machine learning models.
Automatically search for the best parameters for deep learning models.
Build highly accurate machine learning models.
Use the power of machine learning to solve real-world business problems.
This instructor-led, live training in Sofia (online or onsite) offers an introduction to the fields of pattern recognition and machine learning. It covers practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.
Upon completion of this training, participants will be able to:
Apply fundamental statistical methods to pattern recognition.
Utilize essential models such as neural networks and kernel methods for data analysis.
Implement advanced techniques to solve complex problems.
Enhance prediction accuracy by integrating various models.
This instructor-led live training in Sofia (online or onsite) is designed for data scientists and analysts seeking to automate, evaluate, and manage predictive models leveraging DataRobot's machine learning capabilities.
By the end of this training, participants will be able to:
Import datasets into DataRobot to analyze, assess, and perform quality checks on data.
Develop and train models to pinpoint critical variables and achieve prediction goals.
Interpret model outputs to generate actionable insights that support business decision-making.
Monitor and oversee models to ensure optimal prediction performance is maintained.
This instructor-led live training in Sofia (online or onsite) is designed for engineers who wish to apply feature engineering techniques to improve data processing and achieve better machine learning models.
By the end of this training, participants will be able to:
Set up an optimal development environment, including all needed Python packages.
Obtain important insights by analyzing the features of a data set.
Optimize machine learning models through adaptation of the raw data itself.
Clean and transform data sets in preparation for machine learning.
Machine learning is a domain of Artificial Intelligence that enables computers to learn from data without explicit programming instructions.
Deep learning, a specialized subfield of machine learning, employs methods based on learning data representations and structures, such as neural networks.
Python is a prominent high-level programming language known for its clear syntax and high code readability.
This instructor-led live training guides participants through implementing deep learning models for the telecom industry using Python, specifically by walking through the creation of a deep learning credit risk model.
Upon completion of this training, participants will be capable of:
Comprehending the core concepts of deep learning.
Identifying applications and use cases of deep learning within the telecom sector.
Utilizing Python, Keras, and TensorFlow to develop deep learning models tailored for telecom.
Constructing a custom deep learning model for customer churn prediction using Python.
Format of the Course
Interactive lectures and discussions.
Extensive exercises and practice sessions.
Practical implementation in a live-lab environment.
Course Customization Options
To request a customized training session for this course, please contact us to arrange it.
This instructor-led, live training in Sofia (online or on-site) targets data scientists, data analysts, and developers who wish to explore AutoML products and features to create and deploy custom ML training models with minimal effort.
By the end of this training, participants will be able to:
Explore the AutoML product line to implement different services for various data types.
Prepare and label datasets to create custom ML models.
Train and manage models to produce accurate and fair machine learning models.
Make predictions using trained models to meet business objectives and needs.
This hands-on, instructor-led training serves as a logical next step after completing the Python for Data Analysis course.
It introduces key Machine Learning concepts and demonstrates their direct application to data analysis tasks, including prediction, classification, and segmentation.
The curriculum emphasizes practical understanding of Machine Learning using familiar tools like Python, Pandas, and Jupyter Notebook, without requiring an advanced mathematical background.
This course targets individuals with prior knowledge of data science and statistics. The instructional content is structured to either refresh the knowledge of those familiar with these concepts or to inform those who have a suitable foundational background.
This instructor-led live training in Sofia (online or onsite) is designed for developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.
By the end of this training, participants will be able to:
Install and configure Kubeflow on-premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
Run entire machine learning pipelines on diverse architectures and cloud environments.
Use Kubeflow to spawn and manage Jupyter notebooks.
Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
This instructor-led, live training in Sofia (online or on-site) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server.
By the end of this training, participants will be able to:
Install and configure Kubernetes, Kubeflow and other needed software on AWS.
Use EKS (Elastic Kubernetes Service) to simplify the work of initializing a Kubernetes cluster on AWS.
Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
Leverage other AWS managed services to extend an ML application.
This instructor-led, live training in Sofia (online or onsite) targets 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 complete machine learning pipelines on diverse architectures and cloud environments.
Utilize Kubeflow to create and manage Jupyter notebooks.
Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.
Machine Learning is a subfield of Artificial Intelligence that enables computers to learn patterns without being explicitly programmed. Python, a widely used programming language, is renowned for its clear syntax and readability. It provides a robust collection of well-tested libraries and techniques ideal for developing machine learning applications.
Through this instructor-led live training, participants will gain the skills to apply machine learning techniques and tools to solve real-world challenges within the banking sector.
Participants will first grasp the fundamental principles, then apply their knowledge by constructing their own machine learning models and utilizing them to complete several team-based projects.
Audience
Developers
Data scientists
Format of the course
A mix of lectures, discussions, exercises, and extensive hands-on practice
This instructor-led, live training in Sofia (available online or onsite) is designed for technical professionals seeking to master the implementation of machine learning strategies while fully leveraging big data capabilities.
Upon completing this training, participants will be able to:
Grasp the evolution and current trends in machine learning.
Recognize how machine learning is applied across various industries.
Become proficient with the tools, skills, and services necessary to deploy machine learning within an organization.
Comprehend how machine learning enhances data mining and analysis processes.
Understand the concept of a data middle backend and its business applications.
Appreciate the role of big data and intelligent applications in driving industry innovation.
This course is designed for professionals seeking to implement Machine Learning solutions within their teams' practical workflows. The curriculum avoids deep technical jargon, focusing instead on fundamental concepts and their direct business and operational applications.
Target Audience
Investors and AI-focused entrepreneurs
Managers and engineers transitioning their organizations into the AI domain
Machine learning represents a subset of Artificial Intelligence where computers acquire the ability to learn from data without being explicitly programmed. Python is widely recognized for its clear syntax and readability, providing an extensive collection of robust, well-tested libraries and techniques ideal for developing machine learning applications.
Through this instructor-led live training, participants will discover how to apply machine learning techniques and tools to solve real-world problems within the finance industry.
Participants will first grasp the core principles before putting their knowledge into practice by building their own machine learning models and utilizing them to complete various team projects.
By the end of this training, participants will be able to:
Comprehend the fundamental concepts of machine learning
Explore the applications and uses of machine learning in finance
Develop their own algorithmic trading strategy using machine learning with Python
Audience
Developers
Data scientists
Format of the course
A mix of lectures, discussions, exercises, and extensive hands-on practice
This instructor-led live training, offered online or onsite, is designed for data scientists aiming to optimize the ML model creation, tracking, and deployment processes beyond simply building models.
By the end of this training, participants will be able to:
Install and configure MLflow along with related ML libraries and frameworks.
Understand the critical importance of trackability, reproducibility, and deployability in ML models.
Deploy ML models across various public clouds, platforms, or on-premise servers.
Scale the ML deployment process to support multiple users collaborating on a single project.
Establish a central registry to experiment with, reproduce, and deploy ML models.
This training course is designed for individuals who wish to implement fundamental Machine Learning techniques in practical, real-world scenarios.
Audience
Data scientists and statisticians with a foundational understanding of machine learning and proficiency in programming with R. The course focuses on 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 eager to apply these methods in their professional roles.
Industry-specific examples are incorporated to ensure the training is relevant and applicable to the audience.
In this instructor-led, live training, participants will learn how to leverage the iOS Machine Learning (ML) technology stack by stepping through the creation and deployment of a functional iOS mobile app.
By the end of this training, participants will be able to:
Develop a mobile application capable of performing image processing, text analysis, and speech recognition.
Integrate pre-trained ML models into iOS applications.
Build custom ML models from scratch.
Incorporate Siri Voice support into iOS apps.
Gain a thorough understanding and practical usage of frameworks such as CoreML, Vision, CoreGraphics, and GameplayKit.
Utilize programming languages and tools including Python, Keras, Caffe, TensorFlow, scikit-learn, libsvm, Anaconda, and Spyder.
Audience
Developers
Format of the course
A blend of lectures, discussions, exercises, and extensive hands-on practice.
This live, instructor-led training (available online or on-site) targets developers who aim to utilize Google’s ML Kit to construct 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 start creating 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 applications using the ML Kit SDK for on-device processing and deployment.
This instructor-led, live training in Sofia (online or onsite) is aimed at intermediate-level business and technical professionals who wish to apply machine learning techniques to solve real-world business challenges using practical case studies and hands-on tools.
By the end of this training, participants will be able to:
Understand how machine learning fits into modern AI systems and business strategies.
Identify appropriate machine learning methods for different business problems.
Preprocess and transform business data for machine learning tasks.
Apply core machine learning techniques such as classification, regression, clustering, and time series forecasting.
Interpret and evaluate machine learning models in the context of business decision-making.
Gain hands-on experience through case studies and apply learned techniques to practical scenarios.
This course provides an introduction to machine learning techniques applied in robotics.
It offers a comprehensive overview of current methodologies, underlying motivations, and core concepts within the field of pattern recognition.
Following a concise theoretical foundation, participants will engage in practical exercises using open-source tools (typically R) or other widely-used software.
This instructor-led, live training in Sofia (online or onsite) is designed for intermediate-level data analysts, developers, or aspiring data scientists who aim to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.
Upon completion of this course, participants will be able to:
Comprehend and distinguish between key machine learning paradigms.
Explore data preprocessing techniques and model evaluation metrics.
Apply machine learning algorithms to address real-world data challenges.
Utilize Python libraries and Jupyter notebooks for practical development.
Construct models for prediction, classification, recommendation, and clustering.
Pattern matching is a technique employed to identify specific patterns within an image. It allows for the determination of whether certain defined characteristics exist in a captured image, such as verifying the presence of the correct label on a faulty product on a factory assembly line or checking if a component meets specified dimensions. This process differs from "Pattern Recognition," which identifies general patterns by analyzing larger collections of related samples. In pattern matching, the goal is to define exactly what is being sought and then confirm whether that specific expected pattern is present.
Course Format
This course provides an introduction to the approaches, technologies, and algorithms used in pattern matching as applied to Machine Vision.
This instructor-led, live training in Sofia (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 necessary development environment to start building machine learning models with Random forest.
Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
Evaluate and optimize machine learning model performance by tuning the hyperparameters.
RapidMiner is an open-source data science software platform designed for rapid application prototyping and development. It offers an integrated environment covering data preparation, machine learning, deep learning, text mining, and predictive analytics.
During this instructor-led live training, participants will learn how to utilize RapidMiner Studio for data preparation, machine learning, and the deployment of predictive models.
By the conclusion of this training, participants will be able to:
Install and configure RapidMiner
Prepare and visualize data using RapidMiner
Validate machine learning models
Combine data and build predictive models
Implement predictive analytics within a business process
Troubleshoot and optimize RapidMiner
Audience
Data scientists
Engineers
Developers
Course Format
Combination of lectures, discussions, exercises, and extensive hands-on practice
Note
To request customized training for this course, please contact us to make arrangements.
I thoroughly enjoyed the training and appreciated the deeper dive into the subject of Machine Learning. I appreciated the balance between theory and practical applications, especially the hands-on coding sessions. The trainer provided engaging examples and well-designed exercises that enhanced the learning experience. The course covered a wide range of topics, and Abhi demonstrated excellent expertise by answering all questions with clarity and ease.
Valentina
Course - Machine Learning
The training provided an interesting overview of deep learning models and related methods. The topic was quite new to me, but now I feel like I actually have an idea of what AI and ML can involve, what these terms consist of and how they can be used advantageously. In general, I liked the approach of starting with the statistical background and the basic learning models, such as linear regression, especially emphasizing the exercises in between.
Konstantin - REGNOLOGY ROMANIA S.R.L.
Course - Fundamentals of Artificial Intelligence (AI) and Machine Learning
The trainer answered my questions precisely, provided me with tips. The trainer engaged the training participants a lot, which I also liked. As for the substance, Python exercises.
Dawid - P4 Sp z o. o.
Course - Introduction to Machine Learning
We had an overview about Machine Learning, Neural Networks, AI with practical examples.
Catalin - DB Global Technology SRL
Course - Machine Learning and Deep Learning
The trainer showed that he has a good understanding of the subject.
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