Python Training in Dobrogea

Python serves as the foundational language for developing and orchestrating autonomous AI agents. This course emphasizes practical implementation using modern SDKs and frameworks such as LangChain and AutoGen to build, integrate, and manage agent workflows.

Delivered by an expert instructor, this live training (available online or onsite) targets intermediate-level backend engineers, platform engineers, and ML engineers who aim to implement and orchestrate autonomous agents using Python tooling and APIs.

Upon completing this training, participants will be able to:

• Set up and configure Python-based environments suitable for agentic systems.
• Leverage popular agent SDKs like LangChain and AutoGen to develop functional agents.
• Integrate tools and APIs to expand agent capabilities.
• Orchestrate multi-agent workflows and establish communication patterns.
• Apply best practices for debugging, testing, and maintaining agentic codebases.

Format of the Course

• Interactive lectures and discussions.
• Hands-on programming exercises and live demonstrations.
• Practical projects focused on building end-to-end agent workflows.

Course Customization Options

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

This course provides practical engineering techniques for designing, building, testing, and deploying agentic (autonomous) systems using Python. It covers the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production considerations.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level ML engineers, AI developers, and software engineers who wish to build robust, production-ready autonomous agents using Python.

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

• Design and implement the agent loop and decision-making workflows.
• Integrate external tools and APIs to extend agent capabilities.
• Implement short-term and long-term memory architectures for agents.
• Coordinate multi-step orchestrations and agent composability.
• Apply safety, access control, and observability best practices for deployed agents.

Course Format

• Interactive lecture and discussion.
• Hands-on labs building agents with Python and popular SDKs.
• Project-based exercises that produce deployable prototypes.

Course Customization Options

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

Artificial Intelligence with Python involves creating intelligent systems by leveraging Python’s robust ecosystem of AI and machine learning libraries.

This instructor-led live training, available either online or on-site, is designed for intermediate-level Python developers who aim to design, implement, and deploy AI solutions using Python.

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

• Implementing AI algorithms using Python’s core AI libraries.
• Working with supervised, unsupervised, and reinforcement learning models.
• Integrating AI solutions into existing applications and workflows.
• Evaluating model performance and optimizing for accuracy and efficiency.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Dobrogea (online or onsite) is aimed at intermediate-level Python developers who wish to enhance their Python development experience using AWS Cloud9.

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

• Set up and configure AWS Cloud9 for Python development.
• Understand the AWS Cloud9 IDE interface and features.
• Write, debug, and deploy Python applications in AWS Cloud9.
• Collaborate with other developers using the AWS Cloud9 platform.
• Integrate AWS Cloud9 with other AWS services for advanced deployments.

This instructor-led, live training in Dobrogea (online or onsite) targets expert-level data analysts seeking to harness Python's analytical strengths within Power BI, thereby improving their efficiency in data analysis and visualization.

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

• Understand how to integrate Python into Power BI for data analysis purposes.
• Employ Python scripts to load, clean, and preprocess data within the Power BI environment.
• Expand data visualization options by developing custom and interactive visuals using Python.
• Develop advanced data analysis competencies using Python.

Python is a versatile programming language widely used for data manipulation, automation, and analytics. Libraries like Pandas and Polars provide powerful, practical tools for working with tabular data at scale.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to apply Python for everyday data analysis, file processing, and process automation using Pandas and Polars.

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

• Use Python to read, transform, and write CSV and Excel files.
• Perform common data cleaning and transformation tasks with Pandas and Polars.
• Automate repetitive data processes with Python scripts.
• Package simple scripts into executables and follow best practices for projects.

Format of the Course

• Interactive coding demonstrations and short lectures.
• Hands-on exercises with guided code examples.
• Practical mini-projects to automate real-world tasks.

Course Customization Options

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

This intensive, hands-on course covers advanced Python techniques, engineering best practices, and commonly used design patterns to build maintainable, testable, and high-performance Python applications. It emphasizes modern tooling, typing, concurrency models, architecture patterns, and deployment-ready workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level Python developers who wish to adopt professional practices and patterns for production-grade Python systems.

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

• Apply Python typing, dataclasses, and type-checking to increase code reliability.
• Use design patterns and architecture principles to structure robust applications.
• Implement concurrency and parallelism correctly using asyncio and multiprocessing.
• Build well-tested code with pytest, property-based testing, and CI pipelines.
• Profile, optimize, and harden Python applications for production.
• Package, distribute, and deploy Python projects using modern tools and containers.

Format of the Course

• Interactive lectures and short demos.
• Hands-on labs and coding exercises each day.
• Capstone mini-project integrating patterns, testing, and deployment.

Course Customization Options

• To request a customized training or focus area (data, web, or infra), please contact us to arrange.

This live, instructor-led training in Dobrogea (online or onsite) is designed for beginner developers and data analysts who want to learn Python programming from scratch using Google Colab.

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

• Grasp the fundamental concepts of the Python programming language.
• Write and implement Python code within the Google Colab environment.
• Apply control structures to manage program execution flow.
• Define functions to structure and reuse code efficiently.
• Explore and utilize essential libraries for Python development.

This instructor-led live training, offered online or onsite, targets developers who want to use the FARM stack (FastAPI, React, and MongoDB) to build dynamic, high-performance, and scalable web applications.

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

• Set up a development environment that integrates FastAPI, React, and MongoDB.
• Understand the key concepts, features, and benefits of the FARM stack.
• Learn how to build REST APIs with FastAPI.
• Learn how to design interactive applications with React.
• Develop, test, and deploy applications (front end and back end) using the FARM stack.

This instructor-led, live training in Dobrogea (online or onsite) is aimed at beginner-level to intermediate-level and potentially advanced-level robotics developers who wish to learn how to use ROS to program mobile robots using Python.

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

• Set up a development environment that includes ROS, Python, and a mobile robot platform.
• Create and run ROS nodes, topics, services, and actions using Python.
• Use ROS tools and utilities to monitor and debug ROS applications.
• Use ROS packages and libraries to perform common tasks for mobile robots.
• Integrate ROS with other frameworks and tools.
• Troubleshooting and debugging ROS applications.

This course is tailored for individuals looking to master the Python programming language. The focus is on the Python language itself, its core libraries, and the selection of the best and most useful libraries developed by the Python community. Python drives businesses and is used by scientists all over the world – it is one of the most popular programming languages.

The course can be delivered using the latest Python version 3.x with practical exercises making use of the full power. This course can be delivered on any operating system (all flavours of UNIX, including Linux and Mac OS X, as well as Microsoft Windows).

The practical exercises constitute about 70% of the course time, and around 30% are demonstrations and presentations. Discussions and questions can be asked throughout the course.

Note: the training can be tailored to specific needs upon prior request ahead of the proposed course date.

This instructor-led, live training in Dobrogea (online or on-site) is designed for developers looking to master advanced Python programming techniques. Participants will learn how to leverage this versatile language to tackle challenges in areas such as distributed applications, data analysis and visualization, UI development, and maintenance scripting.

This course is tailored for individuals eager to master the Python programming language. It places a strong focus on Python itself, its core libraries, and the selection of the most valuable tools contributed by the Python community. Python powers businesses globally and is extensively utilized by scientists around the world, making it one of the most widely adopted programming languages today.

This instructor-led live training in Dobrogea is based on the popular book, "Automate the Boring Stuff with Python" by Al Sweigart. Designed for beginners, it covers essential Python programming concepts through practical, hands-on exercises and discussions. The focus is on learning to write code to dramatically increase office productivity.

By the end of this training, participants will know how to program in Python and apply this new skill for:

• Automating tasks by writing simple Python programs.
• Writing programs that can do text pattern recognition with "regular expressions".
• Programmatically generating and updating Excel spreadsheets.
• Parsing PDFs and Word documents.
• Crawling web sites and pulling information from online sources.
• Writing programs that send out email notifications.
• Use Python's debugging tools to quickly resolve bugs.
• Programmatically controlling the mouse and keyboard to click and type for you.

In this instructor-led, live training in Dobrogea, participants will explore the most relevant and cutting-edge machine learning techniques in Python by building a series of demonstration applications that process image, music, text, and financial data.

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

• Implement machine learning algorithms and techniques to solve complex problems.
• Apply deep learning and semi-supervised learning approaches to applications involving image, music, text, and financial data.
• Maximize the potential of Python algorithms.
• Utilize libraries and packages such as NumPy and Theano.

This instructor-led, live training in Dobrogea (online or onsite) is aimed at intermediate-level Python developers and data analysts who wish to enhance their skills in data analysis and manipulation using Pandas and NumPy.

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

• Set up a development environment that includes Python, Pandas, and NumPy.
• Create a data analysis application using Pandas and NumPy.
• Perform advanced data wrangling, sorting, and filtering operations.
• Conduct aggregate operations and analyze time series data.
• Visualize data using Matplotlib and other visualization libraries.
• Debug and optimize their data analysis code.

This course is designed to equip participants with practical proficiency in applying Machine Learning methods. Leveraging the Python programming language and its extensive ecosystem of libraries, alongside numerous hands-on examples, the curriculum teaches how to utilize the essential components of Machine Learning. You will learn to make informed data modeling decisions, interpret algorithm outputs, and validate results effectively.

Our objective is to empower you with the confidence to master the fundamental tools in the Machine Learning toolkit and to help you avoid common pitfalls encountered in Data Science applications.

Python has become an immensely popular programming language within the financial sector. It is widely adopted by major investment banks and hedge funds to develop a diverse array of financial applications, from core trading systems to risk management platforms.

In this instructor-led live training, participants will learn how to leverage Python to create practical solutions for a variety of specific finance-related challenges.

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

• Grasp the fundamental concepts of the Python programming language
• Download, install, and maintain optimal development tools for building financial applications in Python
• Choose and employ the most appropriate Python libraries and programming techniques to organize, visualize, and analyze financial data from multiple sources (CSV, Excel, databases, web, etc.)
• Develop applications that address issues such as asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize Python applications

Audience

• Developers
• Analysts
• Quants

Format of the course

• A mix of lectures, discussions, exercises, and extensive hands-on practice

Note

• This training is designed to offer solutions for key challenges faced by finance professionals. If you have a specific topic, tool, or technique you would like to include or expand upon, please contact us to arrange.

Explores practical methodologies for Data Science and AI using Python, empowering professionals with the expertise to analyze data, develop machine learning models, and implement AI-powered applications within business environments. Topics include CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data processing with Spark, and data-driven storytelling. This course is ideal for beginners pursuing a Python data science certification and career-focused analytics training.

This course empowers programmers and data analysts with the fundamental techniques required to construct machine learning solutions from the ground up using Python. It explores the core principles of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network architectures. Participants will examine proven methodologies for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. The curriculum supports professionals in implementing effective ML models, assessing algorithmic constraints, and completing applied projects aimed at solving real-world challenges.

Selenium is an open-source framework designed for automating web application testing across various browsers. With the release of Selenium 4, users have access to enhanced WebDriver APIs, native relative locators, and improved support for Selenium Grid. Python, known for its simplicity and robust integration with testing frameworks such as Pytest, offers a powerful choice for building scalable and maintainable test automation suites.

This instructor-led live training, available both online and onsite, targets beginner to intermediate testers and developers who want to leverage Selenium with Python to automate web application testing in real-world scenarios.

Upon completing this training, participants will be able to:

• Install and configure Selenium with Python within a test environment.
• Develop robust test automation scripts using Selenium WebDriver and Pytest.
• Implement the Page Object Model (POM) to create maintainable test frameworks.
• Execute tests across multiple browsers using Selenium Grid.
• Integrate automated tests into CI/CD pipelines.
• Troubleshoot common issues and apply best practices to ensure automation stability.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical sessions.
• Hands-on implementation in a live laboratory environment.

Customization Options

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

This instructor-led, live training in Dobrogea (online or onsite) is aimed at Matlab users who wish to explore or transition to Python for data analytics and visualization.

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

• Install and configure a Python development environment.
• Understand the differences and similarities between Matlab and Python syntax.
• Use Python to extract insights from various datasets.
• Convert existing Matlab applications to Python.
• Integrate Matlab and Python applications.

This instructor-led, live training in Dobrogea (online or onsite) is aimed at persons who wish to learn just enough Python to begin crunching numbers from sales data, traffic analytics, customer interactions, etc..

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

• Install and configure the necessary software, libraries and development environment to begin writing Python code for data analysis.
• Analyze data from sources such as Excel, CSV, JSON files and databases.
• Clean data to improve its usefulness before analyzing it.
• Perform simple statistical analysis.
• Generate reports that present the desired data in just the right format, from straight numbers to data visualizations.
• Gain valuable insight from data, including trends in performance, problematic areas.

Course Overview

This practical training program is tailored for data engineering professionals seeking to develop applied skills in artificial intelligence, Python programming, and large language models. The curriculum emphasizes real-world application, addressing model integration, prompt engineering, and the creation of AI-driven solutions. Through a series of progressive exercises, participants will advance from foundational principles to constructing deployable AI workflows.

Training Format

• On-site classroom instruction

• Instructor-led sessions accompanied by guided practice

• Interactive discussions and analysis of real-world case studies

• Daily practical exercises

Course Objectives

• Gain a solid understanding of core AI and machine learning concepts applicable to contemporary solutions

• Enhance Python proficiency for AI development and data workflow management

• Comprehend the mechanics of large language models and learn effective utilization strategies

• Design and optimize prompts to ensure consistent and reliable outputs

• Develop complete AI solutions utilizing APIs and frameworks

• Seamlessly integrate AI capabilities into data engineering pipelines

This instructor-led, live training in Dobrogea (online or onsite) is aimed at developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs easier and faster.

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

• Set up the necessary development environment to develop APIs with Python and FastAPI.
• Create APIs quicker and easier using the FastAPI library.
• Learn how to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication in APIs using the FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

This instructor-led live training, offered Dobrogea (online or on-site), targets data scientists interested in using TensorFlow for fraud data analysis.

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.

This instructor-led, live training in Dobrogea (online or onsite) is designed for network engineers who want to maintain, manage, and design computer networks using Python.

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

• Optimize and leverage Paramiko, Netmiko, Napalm, Telnet, and pyntc for network automation with Python.
• Master multi-threading and multiprocessing in network automation.
• Use GNS3 and Python for network programming.

This instructor-led, live training in Dobrogea (online or onsite) is aimed at business professionals and data analysts with intermediate Python skills who wish to apply Python to automate workflows, analyze business data, and generate dynamic Excel-based reports.

This live, instructor-led training (available online or at your facility) is designed for advanced-level users.

Course Format

• Engaging lectures and interactive discussions.
• Ample exercises and hands-on practice.
• Practical implementation within a live lab environment.

Customization Options

• For customized training tailored to this course, please get in touch with us to coordinate the details.

Computer Vision is a domain focused on automatically extracting, analyzing, and interpreting valuable information from digital media. Python, renowned for its clean syntax and high readability, serves as an ideal tool for this purpose.

During this instructor-led live training, participants will master the fundamentals of Computer Vision by constructing a series of practical applications using Python.

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

• Grasp the core principles of Computer Vision
• Utilize Python to execute Computer Vision tasks
• Develop custom systems for face, object, and motion detection

Audience

• Python developers looking to specialize in Computer Vision

Course Format

• A balanced mix of lectures, discussions, exercises, and intensive hands-on practice

This practical course is tailored for Unix and shell users aiming to boost their automation skills by utilizing Python. Although shell scripting is still effective for straightforward tasks, Python offers far greater flexibility, clarity, and scalability for intricate automation, system administration, and DevOps processes.