Fraud Detection with Python and TensorFlow Training Course

This intensive, practical course explores advanced Python techniques, engineering best practices, and widely used design patterns to help you create maintainable, testable, and high-performance Python applications. The curriculum focuses on modern tooling, type annotations, concurrency models, architectural patterns, and workflows ready for production deployment.

Delivered as instructor-led live training (available online or onsite), this course is designed for intermediate to advanced Python developers looking to adopt professional standards and patterns suitable for production-grade systems.

Upon completing this training, participants will be able to:

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

Course Format

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

Customization Options

• For customized training or specific focus areas (data, web, or infrastructure), please contact us to arrange your session.

This program provides practical engineering methodologies for designing, constructing, testing, and deploying autonomous (agentic) systems using Python. The curriculum encompasses the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and critical production considerations.

This instructor-led, live training is available online or onsite. It targets intermediate to advanced ML engineers, AI developers, and software engineers who aim to build robust, production-ready 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 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 focused on building agents with Python and popular SDKs.
• Project-based exercises resulting in deployable prototypes.

Course Customization Options

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

This course provides an in-depth exploration of practical Data Science and AI techniques leveraging Python, empowering professionals with the capabilities 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 methodologies, deep learning with TensorFlow, natural language processing, big data handling via Spark, and the art of data-driven storytelling. It is ideally suited for beginners aiming to obtain a Python data science certification and acquire career-ready analytics training.

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

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

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

• Implement AI algorithms utilizing 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 empowers developers and data analysts with the essential techniques required to construct machine learning solutions from the ground up using Python. It explores core concepts in supervised learning, including classification and regression, as well as unsupervised methods such as clustering and anomaly detection, alongside advanced neural network structures. Participants will study effective strategies for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. The curriculum supports professionals in deploying functional ML models, assessing algorithmic constraints, and executing applied projects designed to address real-world challenges.

This instructor-led live training in Vietnam (online or onsite) is designed for intermediate-level Python developers seeking to enhance their development experience with AWS Cloud9.

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

• Configure and set up AWS Cloud9 for Python projects.
• Navigate the AWS Cloud9 IDE interface and utilize its key features.
• Create, debug, and deploy Python applications within AWS Cloud9.
• Work collaboratively with other developers on the AWS Cloud9 platform.
• Connect AWS Cloud9 with additional AWS services to facilitate advanced deployment strategies.

Course Overview

This practical training program is tailored for data engineering professionals aiming to develop hands-on capabilities in artificial intelligence, Python programming, and large language models (LLMs). The curriculum emphasizes real-world utility, addressing model implementation, prompt design, and the creation of AI-driven solutions. Participants will engage in progressive exercises that transition from foundational principles to the development of deployable AI workflows.

Training Format

• On-site classroom instruction

• Instructor-guided sessions featuring practical application

• Collaborative discussions and analysis of real-world case studies

• Daily hands-on laboratory exercises

Course Objectives

• Grasp essential AI and machine learning concepts applicable to contemporary systems.

• Enhance Python proficiency for AI development and data management tasks.

• Comprehend the mechanics of large language models and their effective utilization.

• Design and refine prompts to ensure consistent and accurate outputs.

• Construct complete AI solutions leveraging APIs and frameworks.

• Seamlessly integrate artificial intelligence into data engineering pipelines.

This instructor-led, live training in Vietnam (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:

• Construct and train convolutional neural networks (CNNs) utilizing TensorFlow.
• Utilize Google Colab for scalable and efficient cloud-based model development.
• Apply image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Employ transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

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

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

• Set up a development environment integrating FastAPI, React, and MongoDB.
• Comprehend the key concepts, features, and benefits of the FARM stack.
• Learn how to construct REST APIs using FastAPI.
• Learn how to design interactive applications with React.
• Develop, test, and deploy applications (covering both front end and back end) using the FARM stack.

This instructor-led live training in Vietnam (online or onsite) targets developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs more easily and quickly.

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

• Set up the necessary development environment for developing APIs with Python and FastAPI.
• Create APIs faster 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 practical proficiency in applying Machine Learning techniques. Utilizing the Python programming language and its extensive ecosystem of libraries, alongside numerous hands-on examples, the curriculum covers essential Machine Learning components. Participants will learn how 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 fundamental Machine Learning tools while steering clear of common pitfalls associated with Data Science applications.

This instructor-led live training in Vietnam (online or onsite) targets developers and data scientists who want to utilize Tensorflow 2.x to build predictors, classifiers, generative models, neural networks, and similar applications.

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 provides a strong conceptual foundation in neural networks, machine learning algorithms, and the principles and applications of deep learning.

Part 1 (40% of the training) emphasizes foundational concepts, helping you select the appropriate technology stack, such as TensorFlow, Caffe, Theano, DeepDrive, or Keras.

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

Part 3 (40% of the training) focuses extensively on TensorFlow, Google's open-source software library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers who wish to implement TensorFlow in their deep learning projects.

Upon completion of this course, participants will:

• possess a solid understanding of Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)
• grasp TensorFlow's architecture and deployment mechanisms
• be capable of managing installation, production environments, architectural tasks, and configurations
• evaluate code quality, perform debugging, and monitor system performance
• implement advanced production-level tasks such as training models, constructing graphs, and logging