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. It focuses on 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 looking to adopt professional practices and patterns for production-grade Python systems.

Upon completing this training, participants will be able to:

• Enhance code reliability by applying Python typing, dataclasses, and type-checking.
• Structure robust applications using established design patterns and architectural principles.
• Correctly implement concurrency and parallelism using asyncio and multiprocessing.
• Create well-tested code through pytest, property-based testing, and CI pipelines.
• Profile, optimize, and harden Python applications for production environments.
• Package, distribute, and deploy Python projects utilizing modern tools and containerization.

Course Format

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

Course Customization Options

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

This course imparts practical engineering skills 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-ready considerations.

This instructor-led live training is available online or on-site. It is designed for intermediate to advanced ML engineers, AI developers, and software engineers looking to construct robust, production-ready autonomous agents with Python.

Upon completing 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 ensuring 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.

Course Customization Options

• For customized training requests, please contact us to make arrangements.

This course offers a hands-on exploration of Data Science and AI applications via Python. It empowers professionals with the essential skills to analyze data, develop machine learning models, and implement AI-driven solutions within business environments. Topics include CRISP-DM methodologies, statistical analysis, supervised and unsupervised learning, deep learning with Tensorflow, natural language processing, big data processing with Spark, and data-driven storytelling. It is ideal for beginners looking to obtain a Python data science certification and receive career-focused analytics training.

Artificial Intelligence with Python involves building intelligent systems using Python’s rich ecosystem of AI and machine learning libraries.

This instructor-led, live training (available online or on-site) is designed for intermediate-level Python developers who aim to design, implement, and deploy AI solutions using Python.

By the end 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.

Course Format

• Interactive lectures and group discussions.
• Abundant hands-on exercises and practical practice.
• Real-time implementation in a live lab environment.

Customization Options

• To request a customized version of this training, please contact us to arrange.

Applied AI from Scratch in Python empowers programmers and data analysts with the essential techniques needed to construct machine learning solutions from the ground up using Python. The course 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 effective methods for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. This training enables professionals to implement real-world machine learning models, assess algorithmic limitations, and complete applied projects aimed at solving practical problems.

This instructor-led, live training in Uzbekistan (online or onsite) is designed for intermediate-level Python developers who want 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 is tailored for data engineering professionals aiming to develop tangible expertise in artificial intelligence, Python programming, and large language models. The curriculum emphasizes real-world application, including model utilization, prompt engineering, and the creation of AI-driven solutions. Participants will engage in progressive exercises that advance from foundational concepts to the development of deployable AI workflows.

Training Format

• In-person classroom instruction

• Instructor-led sessions with guided practice

• Interactive discussions and real-world case studies

• Daily hands-on exercises

Course Objectives

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

• Enhance Python proficiency for AI development and data workflows

• Understand the mechanics of large language models and learn to utilize them effectively

• Design and refine prompts for consistent and reliable outputs

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

• Integrate AI capabilities into data engineering pipelines

This instructor-led, live training in Uzbekistan (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 Uzbekistan (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 Uzbekistan (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 on-site, targets developers aiming to leverage the FARM (FastAPI, React, and MongoDB) stack for building dynamic, high-performance, and scalable web applications.

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

• Set up a 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 Uzbekistan (online or on-site) is designed 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 building APIs with Python and FastAPI.
• Create APIs faster and more 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 FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

This course aims to equip participants with practical proficiency in applying Machine Learning techniques. Utilizing the Python programming language and its extensive library ecosystem, alongside numerous hands-on examples, the curriculum covers the essential components of Machine Learning. Learners will gain the ability to make informed data modeling decisions, interpret algorithm outputs, and validate results effectively.

The primary objective is to empower you with the confidence to leverage core Machine Learning tools while helping you avoid common pitfalls associated with Data Science applications.

This instructor-led, live training in Uzbekistan (online or on-site) is designed for developers and data scientists who aim to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and more.

Upon completing this training, participants will be able to:

• Install and configure TensorFlow 2.x.
• Understand the advantages of TensorFlow 2.x compared to earlier versions.
• Develop deep learning models.
• Implement advanced image classifiers.
• Deploy deep learning models to cloud platforms, mobile devices, and IoT systems.

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

Part-1 (40%) of this training focuses on fundamentals but helps 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 would be extensively based on Tensorflow - API of Google's open source software library for Deep Learning. The examples and handson would all be made 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 on 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, monitoring
• be able to implement advanced production like training models, building graphs and logging