Fraud Detection with Python and TensorFlow Training Course

This intensive, hands-on course covers advanced Python techniques, engineering best practices, and commonly used design patterns to build maintainable, testable, and high-performance Python applications. It emphasizes modern tooling, typing, concurrency models, architecture patterns, and deployment-ready workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level Python developers who wish to adopt professional practices and patterns for production-grade Python systems.

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

• Apply Python typing, dataclasses, and type-checking to increase code reliability.
• Use design patterns and architecture principles to structure robust applications.
• Implement concurrency and parallelism correctly using asyncio and multiprocessing.
• Build well-tested code with pytest, property-based testing, and CI pipelines.
• Profile, optimize, and harden Python applications for production.
• Package, distribute, and deploy Python projects using modern tools and containers.

Format of the Course

• Interactive lectures and short demos.
• Hands-on labs and coding exercises each day.
• Capstone mini-project integrating patterns, testing, and deployment.

Course Customization Options

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

This course provides practical engineering techniques for designing, building, testing, and deploying agentic (autonomous) systems using Python. It explores key topics such as the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production 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 want to build robust, production-ready autonomous agents using Python.

By the end 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.

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.

This course explores practical methodologies for Data Science and AI utilizing Python. It empowers professionals with the necessary skills to analyze data, construct machine learning models, and deploy 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 handling 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 involves the creation of intelligent systems leveraging Python’s vast ecosystem of AI and machine learning libraries.

This instructor-led live training, available both online and onsite, targets intermediate-level Python programmers who want 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.

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 course provides programmers and data analysts with the foundational techniques required to build machine learning solutions from the ground up using Python. It covers the core principles of supervised learning (classification and regression), unsupervised learning (clustering and anomaly detection), and advanced neural network architectures. Participants will examine proven methods for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The program empowers professionals to implement practical ML models, assess algorithm limitations, and complete applied projects aimed at solving real-world problems.

This instructor-led, live training in South Korea (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 acquire actionable expertise in artificial intelligence, Python, and large language models. The curriculum emphasizes real-world implementation, addressing model utilization, prompt engineering, and the creation of AI-driven solutions. Participants will engage in a series of progressive exercises that transition from fundamental principles to the development of deployable AI workflows.

Training Format

• On-site classroom instruction

• Instructor-guided sessions with practical practice

• Interactive discussions and real-world case studies

• Daily hands-on exercises

Course Objectives

• Comprehend core AI and machine learning concepts applicable to contemporary solutions

• Enhance Python proficiency for AI development and data processing

• Gain insight into the functionality of large language models and effective usage strategies

• Design and refine prompts to ensure consistent and reliable outputs

• Construct comprehensive AI solutions using APIs and frameworks

• Seamlessly integrate AI capabilities into data engineering pipelines

This instructor-led, live training in South Korea (online or onsite) is designed for advanced-level professionals seeking 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 South Korea (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 South Korea (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 (available online or onsite) targets developers who intend to use the FARM (FastAPI, React, and MongoDB) stack to build dynamic, high-performance, and scalable web applications.

By the conclusion 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 South Korea (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 aims to equip participants with practical proficiency in applying Machine Learning methods. Utilizing the Python programming language and its extensive ecosystem of libraries, alongside numerous hands-on examples, the curriculum covers the essential components of Machine Learning. Participants will learn to make informed decisions about data modeling, interpret algorithmic outputs, and validate results effectively.

Our objective is to empower you with the confidence to master and apply the fundamental tools of the Machine Learning toolkit, while helping you avoid common pitfalls encountered in Data Science applications.

This instructor-led, live training in South Korea (online or onsite) is designed for developers and data scientists who want 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 advantages of TensorFlow 2.x over previous versions.
• Construct deep learning models.
• Implement an advanced image classifier.
• Deploy deep learning models to the cloud, mobile devices, and IoT devices.

This course provides foundational knowledge of neural networks and their broader role within machine learning and deep learning algorithms and applications.

The first segment (40%) of the training emphasizes core fundamentals, equipping you to select appropriate technologies such as TensorFlow, Caffe, Theano, DeepDrive, and Keras.

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

The third segment (40%) focuses extensively on TensorFlow, the open-source software library for deep learning developed by Google. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

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

Upon completion, participants will:

• gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend the structure and deployment mechanisms of TensorFlow
• possess the skills to manage installation, production environment setup, architecture tasks, and configuration
• be capable of assessing code quality, performing debugging, and monitoring systems
• be able to implement advanced production-level tasks, including model training, graph construction, and logging