Python Programming Fundamentals Training Course

This intensive, hands-on course explores advanced Python techniques, engineering best practices, and widely used design patterns to help you build maintainable, testable, and high-performance Python applications. It focuses on modern tooling, type hinting, concurrency models, architectural patterns, and deployment-ready workflows.

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced Python developers who want 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 enhance code reliability.
• Utilize design patterns and architectural 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.

Course Format

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

Customization Options

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

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

Delivered as instructor-led live training (available online or onsite), this program is tailored for intermediate to advanced ML engineers, AI developers, and software engineers who aim to construct robust, production-ready autonomous agents using Python.

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

• Designing and implementing the agent loop and decision-making workflows.
• Integrating external tools and APIs to extend agent capabilities.
• Implementing short-term and long-term memory architectures for agents.
• Coordinating multi-step orchestrations and agent composability.
• Applying safety, access control, and observability best practices for deployed agents.

Format of the Course

• 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.

This course explores practical methodologies for Data Science and AI via Python, empowering professionals with the expertise to analyze data, construct machine learning models, and implement AI-driven applications within business settings. It encompasses CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data technologies using Spark, and data-driven storytelling. This program is ideal for beginners aiming to obtain a Python data science certification and receive career-focused analytics training.

Artificial Intelligence with Python involves building intelligent systems by leveraging Python’s comprehensive 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 utilizing 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 lecture and discussion.
• Lots of 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.

Applied AI from Scratch in Python provides programmers and data analysts with the foundational techniques needed to build machine learning solutions from the ground up using Python. The course covers core principles of supervised learning (classification and regression), unsupervised learning (clustering and anomaly detection), and advanced neural network architectures. It examines proven methods for working with scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. This training helps professionals implement practical ML models, evaluate algorithm limitations, and complete applied projects aimed at solving real-world problems.

This instructor-led live training, held the Netherlands (online or onsite), is designed for intermediate-level Python developers seeking to enhance their development capabilities using AWS Cloud9.

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

• Set up and configure AWS Cloud9 for Python development.
• Navigate the AWS Cloud9 IDE interface and utilize its key features.
• Write, debug, and deploy Python applications directly within AWS Cloud9.
• Collaborate effectively with other developers via the AWS Cloud9 platform.
• Integrate AWS Cloud9 with additional AWS services to facilitate advanced deployments.

Course Overview

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

Training Format

• In-person classroom instruction

• Instructor-led sessions featuring guided practice

• Interactive discussions and real-world case studies

• Daily hands-on exercises

Course Objectives

• Grasp core AI and machine learning concepts pertinent to contemporary applications

• 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 using APIs and frameworks

• Integrate AI capabilities into data engineering pipelines

This instructor-led, live training in the Netherlands (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 (online or onsite) is designed for 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, located in the Netherlands (online or onsite), targets developers seeking to utilize FastAPI alongside Python to build, test, and deploy RESTful APIs with greater ease and speed.

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

• Set up the required development environment for developing APIs with Python and FastAPI.
• Create APIs more quickly and easily 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 the Netherlands (online or onsite) is intended for data scientists aiming to utilize TensorFlow for the analysis of potential fraud data.

By the conclusion of this training, participants will be capable of:

• Creating a fraud detection model using Python and TensorFlow.
• Building linear regressions and linear regression models to predict fraud.
• Developing an end-to-end AI application for analyzing fraud data.

This course aims to build general proficiency in applying Machine Learning methods in real-world scenarios. By leveraging the Python programming language and its extensive library ecosystem, along with a wide range of practical examples, this course teaches you how to utilize the core components of Machine Learning. You will learn to make informed data modeling decisions, interpret algorithm outputs, and validate results effectively.

Our objective is to equip you with the confidence to understand and apply the most fundamental tools in the Machine Learning toolbox, while helping you avoid common pitfalls associated with Data Science applications.

This instructor-led live training in the Netherlands (online or onsite) targets network engineers who wish 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 live, instructor-led training in the Netherlands (online or onsite) is designed for business professionals and data analysts with intermediate Python skills who wish to use Python to automate workflows, analyze business data, and generate dynamic Excel-based reports.

In this instructor-led, live training in the Netherlands, participants will acquire the most relevant and cutting-edge machine learning techniques in Python while developing a series of demo applications that utilize image, music, text, and financial data.

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

• Implementing machine learning algorithms and techniques to solve complex problems.
• Applying deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
• Maximizing the potential of Python algorithms.
• Utilizing libraries and packages such as NumPy and Theano.