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 imparts practical engineering strategies for designing, constructing, 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.

Delivered as instructor-led, live training (available online or onsite), this program is tailored for intermediate to advanced-level ML engineers, AI developers, and software engineers looking to create robust, production-ready autonomous agents using Python.

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

• Design and implement agent loops and decision-making workflows.
• Integrate external tools and APIs to expand agent capabilities.
• Develop short-term and long-term memory architectures for agents.
• Coordinate multi-step orchestrations and enhance agent composability.
• Apply 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 that result in deployable prototypes.

Course Customisation Options

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

This course explores practical applications of Data Science and Artificial Intelligence leveraging Python. It empowers professionals with the necessary skills to analyse data, construct machine learning models, and implement AI-driven solutions within business environments. Key topics include CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with Tensorflow, natural language processing, big data management using Spark, and data-driven storytelling. It is an ideal choice 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 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 wish 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 optimise for accuracy and efficiency.

Format of the Course

• Interactive lecture and discussion.
• Ample exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

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

This instructor-led live training in Australia (online or onsite) is designed for business analysts wishing to automate trading using algorithmic strategies, Python, and R.

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

• Use algorithms to rapidly buy and sell securities at specific increments.
• Reduce trading costs by employing algorithmic trading techniques.
• Automatically monitor stock prices and execute trades.

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. It 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. The course examines effective strategies for utilising scikit-learn, Apache Spark MLlib, and Jupyter notebooks for practical AI development. It assists professionals in implementing functional ML models, assessing algorithmic limitations, and completing applied projects to solve real-world problems.

This instructor-led, live training in Australia (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 Australia (online or on-site) is tailored for advanced-level professionals seeking to deepen their grasp of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models via Google Colab.

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

• Constructing and training convolutional neural networks (CNNs) using TensorFlow.
• Leveraging Google Colab for scalable and efficient cloud-based model development.
• Implementing image preprocessing techniques for computer vision tasks.
• Deploying computer vision models for real-world applications.
• Employing transfer learning to enhance the performance of CNN models.
• Visualising and interpreting the results of image classification models.

This instructor-led, live training in Australia (online or onsite) is aimed at 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 start 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 (Numpy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance in handling large datasets.

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

Upon completion 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 (online or onsite) is designed for developers wishing 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 Australia (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 instructor-led live training in Australia (online or onsite) is tailored for developers and data scientists who wish to utilise 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 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, as well as broader concepts within machine learning algorithms, deep learning frameworks, and their practical applications.

The first part of this training (40%) concentrates on fundamental principles, aiding you in selecting the appropriate technology stack, such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

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

The final portion of the training (40%) focuses extensively on TensorFlow, which is Google's open-source 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 utilise TensorFlow for their deep learning projects.

Upon completion of this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• understand the structure and deployment mechanisms of TensorFlow
• be capable of managing installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production-level tasks such as training models, building graphs, and logging