Fraud Detection with Python and TensorFlow 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. The curriculum emphasizes 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 aim to adopt professional practices and patterns for production-grade Python systems.

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

• Leverage Python typing, dataclasses, and type-checking to enhance code reliability.
• Utilize design patterns and architectural principles to structure robust applications.
• Correctly implement concurrency and parallelism using asyncio and multiprocessing.
• Develop well-tested code using pytest, property-based testing, and CI pipelines.
• Profile, optimize, and harden Python applications for production environments.
• 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.
• A capstone mini-project that integrates patterns, testing, and deployment.

Customization Options

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

This course imparts practical engineering methods for designing, constructing, evaluating, and deploying autonomous (agentic) systems using Python. Key topics include the agent loop, tool integration, memory and state management, orchestration patterns, safety controls, and production-ready considerations.

Delivered as an instructor-led, live training session (available online or onsite), this programme is designed for intermediate to advanced ML engineers, AI developers, and software engineers who aim to build robust, production-grade autonomous agents in Python.

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

• Designing and implementing agent loops and decision-making workflows.
• Integrating external tools and APIs to enhance agent functionality.
• Implementing both short-term and long-term memory architectures for agents.
• Coordinating multi-step orchestrations and enabling agent composability.
• Applying best practices for safety, access control, and observability in deployed agents.

Course Format

• Interactive lectures and discussions.
• Hands-on labs for building agents using Python and popular SDKs.
• Project-based exercises resulting in deployable prototypes.

Customization Options

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

This course explores practical methodologies for Data Science and AI through Python, empowering professionals with the expertise to analyze data, construct machine learning models, and deploy AI-powered solutions within business environments. The curriculum encompasses CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data analytics with Spark, and data-driven storytelling. It is ideal for beginners pursuing a Python data science certification and career-focused analytics training.

Artificial Intelligence with Python focuses on building intelligent systems by leveraging Python's comprehensive ecosystem of AI and machine learning libraries.

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

Upon completion of 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 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 Sweden (online or on-site) is designed for business analysts who wish to automate trading using algorithmic strategies, Python, and R.

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

• Utilize algorithms to rapidly buy and sell securities at specialized increments.
• Lower trade-related costs by leveraging algorithmic trading.
• Automatically track stock prices and execute trades.

This instructor-led, live training in Sweden (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 Sweden (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 Sweden (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 Sweden (online or on-site) is designed for intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

Upon completion 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 Deep Learning researchers and engineers who wish to leverage available tools (primarily open-source) for the analysis of computer images.

Participants will have the opportunity to work through practical examples.

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

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

• Set up the necessary development environment integrating 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 Sweden (online or onsite) is tailored for developers who want to use FastAPI with Python to build, test, and deploy RESTful APIs more efficiently and quickly.

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

• Set up the required development environment for creating APIs with Python and FastAPI.
• Develop APIs faster and with greater ease using the FastAPI library.
• Learn to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to databases using SQLAlchemy.
• Implement security and authentication in APIs using FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

This instructor-led, live training in Sweden (online or onsite) is aimed at data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis.

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

• Set up the necessary environment to start developing Pandas workflows at scale with Modin.
• Understand the features, architecture, and advantages of Modin.
• Know the differences between Modin, Dask, and Ray.
• Perform Pandas operations faster with Modin.
• Implement the entire Pandas API and functions.

This instructor-led live training, conducted at Sweden (online or on-site), is intended for developers and data scientists who aim to utilize TensorFlow 2.x to develop predictors, classifiers, generative models, neural networks, and related applications.

By the conclusion 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 you with a solid conceptual foundation in neural networks, covering machine learning algorithms, deep learning methodologies, and their practical applications.

Part 1 (40%) of the training focuses heavily on fundamental principles, empowering you to select the most appropriate technology for your needs, such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

Part 2 (20%) introduces Theano, a Python library designed to simplify the development of deep learning models.

Part 3 (40%) is extensively dedicated to TensorFlow, Google's open-source software library for Deep Learning. All examples and hands-on exercises will be conducted within the TensorFlow environment.

Audience

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

Upon completion of this course, participants will:

• possess a comprehensive understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow’s architecture and deployment mechanisms
• be capable of performing installation, production environment setup, and architectural configuration
• be able to assess code quality, perform debugging, and monitor performance
• be able to implement advanced production-level tasks, such as training models, constructing graphs, and managing logs