Python for Matlab Users Training Course

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 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.

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.

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 Romania (online or onsite) is designed for business analysts who wish to automate trading using algorithmic strategies, Python, and R.

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

• Use algorithms to rapidly buy and sell securities at specialized intervals.
• Reduce costs associated with trading through algorithmic trading.
• Automatically monitor stock prices and execute trades.

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.

This instructor-led, live training in Romania (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.

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, delivered Romania (online or onsite), is designed for data scientists and software engineers who aim to utilize Dask within the Python ecosystem to build, scale, and analyze large datasets.

Upon completing this training, participants will be capable of:

• Configuring the environment necessary to begin building big data processing workflows with Dask and Python.
• Exploring the features, libraries, tools, and APIs offered by Dask.
• Gaining insight into how Dask accelerates parallel computing capabilities in Python.
• Learning techniques to scale the Python ecosystem (including NumPy, SciPy, and Pandas) using Dask.
• Optimizing the Dask environment to ensure high performance when managing large datasets.

This instructor-led, live training in Romania (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, 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 Romania (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 Romania (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 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.

This instructor-led, live training in Romania (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.