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 emphasises 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, optimise, and harden Python applications for production.
• Package, distribute, and deploy Python projects using modern tools and containers.

Course Format

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

Course Customisation Options

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

This course teaches practical engineering techniques to design, build, test, and deploy 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 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.

Course Format

• Interactive lectures and discussions.
• Hands-on labs building agents with Python and popular SDKs.
• Project-based exercises that produce deployable prototypes.

Course Customisation Options

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

This course explores practical approaches to Data Science and AI using Python, equipping professionals with the skills to explore data, build machine learning models, and deploy AI-driven applications in business contexts. It 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. 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 rich ecosystem of AI and machine learning libraries.

This instructor-led, live training (available online or on-site) is designed for 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 optimise for accuracy and efficiency.

Course Format

• Interactive lectures and discussions.
• Abundant exercises and practical sessions.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request a customised version of this course, please contact us to arrange.

This instructor-led, live training in New Zealand (online or onsite) is aimed at business analysts who wish to automate trading using algorithmic strategies, Python, and R.

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

• Use algorithms to buy and sell securities rapidly in specialised increments.
• Reduce trading-related costs through the application of algorithmic trading.
• Automatically monitor stock prices and execute trades.

Applied AI from Scratch in Python equips programmers and data analysts with foundational techniques for building machine learning solutions from the ground up using Python. Covers core principles of supervised learning classification and regression, unsupervised learning clustering and anomaly detection, and advanced neural network architectures. Examines proven methods for working with scikit-learn, Apache Spark MLlib, and Jupyter notebooks for hands-on AI development. Helps professionals implement practical ML models, evaluate algorithm limitations, and complete applied projects for real-world problem solving.

This instructor-led, live training in New Zealand (online or onsite) is designed for intermediate-level Python developers who wish to enhance their Python development skills 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 its features.
• Write, debug, and deploy Python applications within AWS Cloud9.
• Collaborate with other developers using the AWS Cloud9 platform.
• Integrate AWS Cloud9 with other AWS services for advanced deployment scenarios.

Course Overview

This practical training is tailored for professionals with a data engineering background who wish to develop hands-on expertise in artificial intelligence, Python, and large language models. The curriculum emphasises real-world applications, encompassing model utilisation, prompt engineering, and the creation of AI-driven solutions. Participants will navigate through progressive exercises that transition from foundational concepts to the development of deployable AI workflows.

Training Format

• In-person classroom-based training

• Instructor-led sessions featuring guided practice

• Interactive discussions and real-world case studies

• Daily hands-on exercises

Course Objectives

• Comprehend core AI and machine learning concepts pertinent to contemporary applications

• Enhance Python skills for AI development and data workflows

• Gain an understanding of how large language models function and how to utilise them effectively

• Design and optimise prompts to ensure reliable outputs

• Construct end-to-end AI solutions using APIs and frameworks

• Integrate AI capabilities into data engineering pipelines

This instructor-led, live training in New Zealand (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.
• Visualise and interpret the results of image classification models.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level Python developers and data analysts seeking 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 including 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 analyse time series data.
• Visualise data using Matplotlib and other visualisation libraries.
• Debug and optimise their data analysis code.

This instructor-led, live training in New Zealand (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.
• Utilise advanced features of TensorFlow for deep learning.

This instructor-led, live training (available online or on-site) is designed for developers eager to harness 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:

• Establish the essential development environment integrating FastAPI, React, and MongoDB.
• Grasp the core concepts, features, and advantages of the FARM stack.
• Learn how to construct REST APIs using FastAPI.
• Master the art of designing interactive applications with React.
• Develop, test, and deploy both front-end and back-end applications using the FARM stack.

This instructor-led, live training in New Zealand (online or on-site) is designed for developers who wish to leverage FastAPI with Python to build, test, and deploy RESTful APIs more efficiently and rapidly.

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

• Set up the necessary development environment to build APIs using Python and FastAPI.
• Create APIs more quickly and easily using the FastAPI library.
• Learn how to define data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication mechanisms in APIs using FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

This instructor-led, live training in New Zealand (online or on-site) is designed for developers and data scientists who wish 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 earlier versions.
• Build deep learning models.
• Implement advanced image classifiers.
• Deploy deep learning models to the cloud, mobile devices, and IoT platforms.

This course begins by providing you with conceptual knowledge of neural networks, machine learning algorithms, and deep learning (including algorithms and applications).

Part 1 (40%) of this training focuses on fundamentals while helping you select the right technology, such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

Part 2 (20%) introduces Theano—a Python library that simplifies writing deep learning models.

Part 3 (40%) is extensively based on TensorFlow, the API for Google's open-source software library for Deep Learning. All examples and hands-on activities will be conducted using TensorFlow.

Audience

This course is designed for engineers seeking to use TensorFlow for their Deep Learning projects.

Upon completing this course, delegates will:

• have a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow's structure and deployment mechanisms
• be able to carry out installation, production environment, and architecture tasks and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production tasks such as training models, building graphs, and logging