Fraud Detection with Python and TensorFlow 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.

This instructor-led, live training in Czech Republic (online or onsite) is aimed at business analysts who wish to automate trading using algorithmic trading, Python, and R.

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

• Employ algorithms to buy and sell securities at specialized increments rapidly.
• Reduce costs associated with trade using algorithmic trading.
• Automatically monitor stock prices and place trades.

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 in Czech Republic (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

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

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led, live training in Czech Republic (online or onsite) is aimed at data scientists and software engineers who wish to use Dask with the Python ecosystem to build, scale, and analyze large datasets.

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

• Set up the environment to start building big data processing with Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Understand how Dask accelerates parallel computing in Python.
• Learn how to scale the Python ecosystem (Numpy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance in handling large datasets.

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 Czech Republic (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

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

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

Target Audience

This course is designed for researchers and engineers in the field of Deep Learning who wish to leverage available tools (primarily open source) to analyze digital images.

The curriculum includes practical, working examples.

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) is intended for developers and data scientists who aim to utilize TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and related applications.

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

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile and IoT devices.

This course provides conceptual knowledge of neural networks and, more broadly, of machine learning and deep learning algorithms and applications.

Part 1 (40%) of the training focuses on fundamentals, helping you select the appropriate technology such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

Part 2 (20%) introduces Theano, a Python library that simplifies the creation of deep learning models.

Part 3 (40%) of the training is extensively based on TensorFlow, the API for Google's open-source software library for Deep Learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers looking to utilize TensorFlow for their Deep Learning projects.

Upon completion of this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow’s structure and deployment mechanisms
• be capable of performing installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production-level tasks such as training models, building graphs, and logging