Python for Matlab Users Training Course

This intensive, practical course delves into advanced Python techniques, engineering best practices, and widely used design patterns to help you build maintainable, testable, and high-performance Python applications. The curriculum emphasizes modern tooling, type hinting, concurrency models, architectural patterns, and deployment-ready workflows.

Delivered as instructor-led live training (available online or onsite), this program is designed for intermediate to advanced Python developers aiming to adopt professional practices and patterns suitable for production-grade Python systems.

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

• Utilize Python typing, dataclasses, and type-checking to enhance code reliability.
• Leverage design patterns and architectural principles to structure robust applications.
• Correctly implement concurrency and parallelism using asyncio and multiprocessing.
• Develop well-tested code employing pytest, property-based testing, and CI pipelines.
• Profile, optimize, and secure Python applications for production environments.
• Package, distribute, and deploy Python projects using modern tools and containers.

Course Format

• Interactive lectures and concise demonstrations.
• Hands-on labs and coding exercises conducted daily.
• A capstone mini-project that integrates patterns, testing, and deployment.

Customization Options

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

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

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

Upon completion 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 lectures and discussions.
• Hands-on labs focused on building agents with Python and popular SDKs.
• Project-based exercises resulting in deployable prototypes.

Customization Options

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

This course explores practical applications of Data Science and AI through Python, equipping professionals with the ability to analyze data, construct machine learning models, and implement AI-powered solutions in business environments. It covers the CRISP-DM methodology, statistical analysis, supervised and unsupervised learning techniques, deep learning with TensorFlow, natural language processing, big data handling with Spark, and the art of data-driven storytelling. Designed for beginners seeking Python data science certification and career-focused analytics training.

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

This instructor-led live training, available either online or onsite, is designed for intermediate-level Python programmers who want to design, implement, and deploy AI solutions using Python.

Upon completing this training, participants will be able to:

• Implement AI algorithms using Python’s core AI libraries.
• Work with supervised, unsupervised, and reinforcement learning models.
• Integrate AI solutions into existing applications and workflows.
• Evaluate model performance and optimize for accuracy and efficiency.

Format of the Course

• Interactive lectures and discussions.
• Ample exercises and practice opportunities.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

The course 'Applied AI from Scratch in Python' provides programmers and data analysts with the essential techniques required to develop machine learning solutions entirely from the ground up using Python. The curriculum covers the fundamental principles of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network architectures. Participants will explore proven methodologies for leveraging tools like scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The program is designed to help professionals implement practical ML models, critically evaluate the limitations of various algorithms, and complete applied projects aimed at solving real-world problems.

This instructor-led live training, conducted Czech Republic (online or onsite), is designed for intermediate Python developers aiming to elevate their development experience using AWS Cloud9.

By the conclusion 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.

Course Overview

This practical training program is tailored for data engineering professionals aiming to develop tangible skills in artificial intelligence, Python, and large language models. The curriculum emphasizes real-world applications, including the utilization of models, prompt engineering, and the creation of AI-driven solutions. Participants will engage in a series of progressive exercises that transition from foundational concepts to the development of deployable AI workflows.

Format of Training

• In-person classroom instruction

• Instructor-led sessions featuring guided practice

• Interactive discussions supported by real-world case studies

• Daily hands-on exercises

Course Objectives

• Gain a solid understanding of core AI and machine learning concepts applicable to modern scenarios

• Enhance Python proficiency for AI development and data workflows

• Comprehend the mechanics of large language models and learn to utilize them effectively

• Design and optimize prompts to ensure reliable outputs

• Construct end-to-end AI solutions leveraging APIs and frameworks

• Seamlessly integrate AI capabilities into data engineering pipelines

This instructor-led, live training in Czech Republic (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 instructor-led, live training in (online or onsite) is aimed at developers who wish to use the FARM (FastAPI, React, and MongoDB) stack to build dynamic, high-performance, and scalable web applications.

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

• Set up the necessary 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 Czech Republic (online or onsite) is aimed at developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs more easily and quickly.

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 in Czech Republic (online or on-site) targets data scientists who wish to utilize 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.

This course is designed to equip participants with practical proficiency in applying Machine Learning techniques. Utilizing the Python programming language and its extensive ecosystem of libraries, along with numerous hands-on examples, the curriculum covers the essential components of Machine Learning. Participants will learn to make informed decisions regarding data modeling, interpret algorithm outputs, and validate results effectively.

The primary objective is to provide you with the confidence to understand and utilize the core tools from the Machine Learning toolkit, while avoiding common pitfalls associated with Data Science applications.

This instructor-led, live training in Czech Republic (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 utilize Paramiko, Netmiko, Napalm, Telnet, and pyntc for network automation with Python.
• Master multi-threading and multiprocessing in network automation.
• Utilize GNS3 and Python for network programming.

This instructor-led, live training in Czech Republic (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 course is tailored for individuals looking to master the Python programming language. It places strong emphasis on the core Python language and its essential libraries, while also guiding you in selecting the most valuable tools developed by the vibrant Python community. Widely used by scientists globally and integral to modern business operations, Python stands as one of the most popular programming languages today.