Fraud Detection with Python and TensorFlow Training Course

This intensive, practical course delves into advanced Python techniques, engineering standards, 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 workflows ready for production deployment.

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:

• 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.
• Create well-tested code employing 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.

Format of the Course

• Interactive lectures and brief demonstrations.
• Hands-on labs and coding exercises every day.
• A capstone mini-project that integrates patterns, testing, and deployment.

Course Customization Options

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

This course provides practical engineering methodologies for designing, building, testing, and deploying autonomous (agentic) systems using Python. It explores key topics such as the agent loop, tool integrations, memory and state management, orchestration patterns, safety mechanisms, and considerations for production environments.

Offered as an instructor-led live training session (available online or on-site), this program is designed for intermediate to advanced ML engineers, AI developers, and software engineers seeking to construct robust, production-grade autonomous agents using 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 functionalities.
• Developing short-term and long-term memory structures for agents.
• Coordinating multi-step orchestrations and ensuring agent composability.
• Applying best practices for safety, access control, and observability in deployed agents.

Course Format

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

Customization Options

• To request a customized training version of this course, please contact us to make arrangements.

This course explores practical methodologies for Data Science and Artificial Intelligence utilizing Python. It empowers professionals with the essential skills to analyze data, develop machine learning models, and implement AI-driven solutions within business environments. The curriculum covers 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. It is ideally suited for beginners aiming to obtain a Python data science certification and receive career-ready analytics training.

Artificial Intelligence with Python involves creating intelligent systems by leveraging Python's comprehensive ecosystem of AI and machine learning libraries.

This instructor-led live training, available online or onsite, targets intermediate-level Python programmers looking to design, implement, and deploy AI solutions using Python.

Upon completing 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.

Applied AI from Scratch in Python empowers programmers and data analysts with the fundamental techniques required to construct machine learning solutions entirely from the ground up using Python. The course covers essential principles of supervised learning, including classification and regression, as well as unsupervised learning methods like clustering and anomaly detection, alongside advanced neural network architectures. It explores established practices for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. Participants will learn to implement functional ML models, assess the limitations of various algorithms, and execute applied projects designed for real-world problem-solving.

This instructor-led, live training in Bulgaria (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 aiming to develop concrete expertise in artificial intelligence, Python, and large language models. The curriculum emphasizes real-world implementation, addressing model application, prompt engineering, and the creation of AI-driven solutions. Participants will engage in progressive exercises that transition from foundational concepts to the construction of deployable AI workflows.

Training Format

• In-person classroom instruction

• Instructor-led sessions featuring guided practice

• Interactive discussions alongside 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 master their effective utilization

• Design and optimize prompts to ensure reliable outputs

• Construct end-to-end AI solutions utilizing APIs and frameworks

• Integrate AI capabilities into data engineering pipelines

This live, instructor-led training in Bulgaria (online or onsite) is aimed at experienced 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 Bulgaria (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 Bulgaria (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.

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

Upon completing this training, participants will be able to:

• Configure the essential development environment integrating FastAPI, React, and MongoDB.
• Grasp the core concepts, features, and advantages of the FARM stack.
• Master the creation of REST APIs using FastAPI.
• Design interactive user interfaces with React.
• Develop, test, and deploy both front-end and back-end applications using the FARM stack.

This instructor-led live training in Bulgaria (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 course is designed to equip participants with the essential skills needed to effectively apply Machine Learning techniques in real-world scenarios. Utilizing Python and its extensive ecosystem of libraries, alongside numerous hands-on examples, the curriculum guides learners through the core components of Machine Learning. It focuses on making informed decisions during data modeling, interpreting algorithm outputs, and validating results.

Our objective is to empower you with the confidence to leverage fundamental Machine Learning tools and help you steer clear of common pitfalls associated with Data Science applications.

This instructor-led live training in Bulgaria (available online or onsite) is designed for developers and data scientists who wish to utilize TensorFlow 2.x to build 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 starts by providing conceptual knowledge about neural networks and machine learning algorithms, including deep learning (algorithms and applications).

Part-1 (40%) of this training focuses more on fundamentals, but will help you choose the right technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2 (20%) of this training introduces Theano, a Python library that makes writing deep learning models easy.

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

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects.

After completing this course, delegates will:

• have a good understanding of deep neural networks (DNN), CNN, and RNN.
• understand TensorFlow’s structure and deployment mechanisms.
• be able to carry out installation / production environment / architecture tasks and configuration.
• be able to assess code quality, perform debugging, and monitoring.
• be able to implement advanced production-like training models, building graphs, and logging.