Fraud Detection with Python and TensorFlow Training Course

This intensive, hands-on course explores advanced Python techniques, engineering best practices, and commonly utilized 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 deployment-ready workflows.

Delivered as instructor-led, live training (available online or onsite), this program targets intermediate to advanced Python developers seeking to adopt professional practices and patterns for production-grade Python systems.

Upon completion of this training, participants will be capable of:

• Applying Python typing, dataclasses, and type-checking to enhance code reliability.
• Utilizing design patterns and architectural principles to structure robust applications.
• Correctly implementing concurrency and parallelism using asyncio and multiprocessing.
• Constructing well-tested code through pytest, property-based testing, and CI pipelines.
• Profiling, optimizing, and hardening Python applications for production environments.
• Packaging, distributing, and deploying Python projects using modern tools and containerization.

Course Format

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

Customization Options

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

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

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level ML engineers, AI developers, and software engineers who wish 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 delves into practical methodologies for Data Science and AI utilizing Python. It empowers professionals with the essential skills to explore data, construct machine learning models, and deploy AI-driven applications within business environments. The curriculum covers CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data with Spark, and data-driven storytelling. It is ideal for beginners seeking a Python data science certification and 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 onsite, is designed for intermediate-level Python programmers who aim 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.

Course Format

• Interactive lectures and discussions.
• Ample exercises and practice sessions.
• Hands-on implementation within a live-lab environment.

Course Customization Options

• To request customized training for this course, please contact us to make arrangements.

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

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

• Utilize algorithms to rapidly buy and sell securities at specific increments.
• Lower trading costs by leveraging algorithmic trading techniques.
• Automatically monitor stock prices and execute trades without manual intervention.

Applied AI from Scratch in Python provides programmers and data analysts with the essential techniques required 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. It explores proven strategies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. This helps professionals implement functional ML models, assess algorithmic constraints, and complete applied projects designed to solve real-world problems.

This instructor-led, live training in Taiwan (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.

This instructor-led, live training in Taiwan (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 Taiwan (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:

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

This instructor-led, live training in Taiwan (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 Taiwan (online or onsite) is designed for intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

Upon completing this training, participants will be able to:

• Set up and navigate Google Colab for deep learning projects.
• Grasp the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Leverage advanced TensorFlow features for deep learning.

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

By the end 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 Taiwan (online or onsite) is tailored for developers who wish to utilize 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:

• Configure the necessary development environment for creating APIs with Python and FastAPI.
• Develop APIs more quickly and effortlessly using the FastAPI library.
• Learn to create data models and schemas based on Pydantic and OpenAPI standards.
• Integrate APIs with databases using SQLAlchemy.
• Implement security measures and authentication within APIs using FastAPI tools.
• Construct container images and deploy web APIs to cloud servers.

This instructor-led live training in Taiwan (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 more.

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 provides foundational knowledge of neural networks and broader machine learning algorithms, with a specific focus on deep learning techniques and their practical applications.

The first section (40% of the curriculum) emphasizes core fundamentals, equipping you with the insights needed to select the most appropriate technology for your needs, such as TensorFlow, Caffe, Theano, DeepDrive, or Keras.

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

The final section (40% of the curriculum) focuses extensively on TensorFlow, Google’s open-source software library for deep learning. All examples and hands-on exercises will be conducted within the TensorFlow framework.

Audience

This course is designed for engineers looking to leverage TensorFlow for their deep learning projects.

Upon completion, participants will:

• Possess a solid understanding of Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)
• Understand the architecture and deployment mechanisms of TensorFlow
• Be capable of handling installation, production environment setup, and architectural configuration tasks
• Assess code quality, perform debugging, and implement monitoring systems
• Implement advanced production-level tasks such as training models, constructing computational graphs, and setting up logging