Fraud Detection with Python and TensorFlow Training Course

This rigorous, practical course delves into advanced Python methodologies, engineering standards, and widely adopted design patterns, empowering you to develop maintainable, testable, and high-performance Python applications. The curriculum highlights contemporary tooling, type hints, concurrency frameworks, architectural approaches, and workflows ready for production deployment.

This instructor-led, live training session (available online or on-site) targets intermediate to advanced Python developers aiming to integrate professional practices and patterns into their production-grade Python systems.

Upon completing this training, participants will be equipped to:

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

Course Format

• Interactive lectures complemented by concise demonstrations.
• Daily hands-on labs and coding exercises.
• A capstone mini-project that integrates patterns, testing, and deployment strategies.

Customization Options

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

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

Delivered as an instructor-led live training (online or onsite), this program is designed for intermediate to advanced ML engineers, AI developers, and software engineers aiming 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 expand 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.

Explores practical methodologies for Data Science and AI leveraging Python, empowering professionals to analyze data, develop machine learning models, and implement AI-driven solutions within business environments. The curriculum encompasses CRISP-DM workflows, statistical analysis, supervised and unsupervised learning techniques, deep learning with TensorFlow, natural language processing, big data handling via Spark, and data-driven storytelling. This course is well-suited for beginners pursuing a Python data science certification and aiming for career-ready 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 online or on-site, 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 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.

This instructor-led live training in Norway (online or onsite) is designed for business analysts who wish to automate trading using algorithmic trading, Python, and R.

Upon completing this training, participants will be able to:

• Use algorithms to rapidly buy and sell securities at specialized increments.
• Lower the costs associated with trading through the use of algorithmic trading.
• Automatically monitor stock prices and execute trades.

The course Applied AI from Scratch in Python empowers programmers and data analysts with the foundational techniques required to build machine learning solutions from the ground up using Python. It covers 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. The curriculum examines proven methodologies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. Participants will learn to implement functional ML models, assess algorithmic limitations, and complete applied projects designed for solving real-world problems.

This instructor-led, live training in Norway (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 Norway (online or onsite) is designed for 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:

• Configure the necessary environment to begin building big data processing solutions using Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Understand how Dask accelerates parallel computing within Python.
• Learn how to scale the Python ecosystem (including Numpy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to ensure high performance when handling large datasets.

This instructor-led, live training in Norway (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 live, instructor-led training in Norway (online or onsite) targets intermediate-level data scientists and developers eager to understand and apply deep learning techniques within the Google Colab ecosystem.

By the conclusion 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.

Audience

This course is designed for deep learning researchers and engineers who wish to leverage available tools (primarily open-source) to analyze computer images.

The course includes practical working examples.

This instructor-led live training (available online or onsite) is intended for developers who want to utilize the FARM stack (FastAPI, React, and MongoDB) to construct dynamic, high-performance, and scalable web applications.

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

• Setting up the required development environment that integrates FastAPI, React, and MongoDB.
• Understanding the fundamental concepts, features, and benefits of the FARM stack.
• Learning how to construct REST APIs using FastAPI.
• Learning how to design interactive applications with React.
• Developing, testing, and deploying applications (both front-end and back-end) using the FARM stack.

This instructor-led, live training in Norway (online or onsite) is designed for developers who want to use FastAPI with 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 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 Norway (online or onsite) is designed for developers and data scientists who wish to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and more.

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 initiates with a conceptual foundation in neural networks, covering broad aspects of machine learning algorithms and deep learning (including algorithms and their applications).

Part-1 (40%) of this training concentrates on fundamentals, aiding you in selecting the appropriate technology such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

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

Part-3 (40%) of the training is heavily focused on TensorFlow, the API for Google's open-source deep learning software library. All examples and hands-on exercises will utilize TensorFlow.

Audience

This course is designed for engineers aiming to utilize TensorFlow for their deep learning projects.

Upon completing 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 handling installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitor systems
• be able to implement advanced production tasks such as training models, building graphs, and logging