Introduction to Machine Learning Training Course

This instructor-led, live training in New Zealand (online or on-site) is designed for data scientists and software engineers who wish to use AdaBoost to develop boosting algorithms for machine learning with Python.

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

• Set up the necessary development environment to begin building machine learning models with AdaBoost.
• Understand the ensemble learning approach and how to implement adaptive boosting.
• Learn how to build AdaBoost models to enhance machine learning algorithms in Python.
• Apply hyperparameter tuning to improve the accuracy and performance of AdaBoost models.

This course covers AI (emphasising Machine Learning and Deep Learning) in the automotive industry. It helps determine which technologies can potentially be used in a variety of automotive scenarios—from simple automation and image recognition to autonomous decision-making.

A deep dive into the fundamentals of artificial intelligence illustrates how intelligent technology is transforming digital strategy, automation, and decision-making processes within enterprise operations. This course examines core concepts, including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside areas such as communication, perception, and autonomous action. It equips executives and architects with the insights needed to evaluate AI-driven transformation opportunities, assess emerging technology trends, and integrate practical intelligent solutions to accelerate business agility.

This instructor-led, live training in New Zealand (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

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

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

An Artificial Neural Network is a computational data model employed in the development of Artificial Intelligence (AI) systems capable of performing “intelligent” tasks. Neural Networks are widely used in Machine Learning (ML) applications, which represent one implementation of AI. Deep Learning is a specialised subset of ML.

This instructor-led, live training in New Zealand (online or on-site) is aimed at participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customised chatbots for various applications.

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

• Understand the fundamentals of chatbot development.
• Navigate the Google Cloud Platform and access AutoML.
• Prepare data for training chatbot models.
• Train and evaluate custom chatbot models using AutoML.
• Deploy and integrate chatbots into various platforms and channels.
• Monitor and optimise chatbot performance over time.

This instructor-led, live training in New Zealand (online or onsite) introduces participants to the field of pattern recognition and machine learning. It explores practical applications across statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

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

• Apply core statistical methods to pattern recognition.
• Use key models such as neural networks and kernel methods for data analysis.
• Implement advanced techniques to tackle complex problem-solving challenges.
• Improve prediction accuracy by combining different models.

This instructor-led, live training in New Zealand (online or onsite) is aimed at data scientists and data analysts who wish to automate, evaluate and manage predictive models using DataRobot's machine learning capabilities.

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

• Load datasets into DataRobot to analyse, assess and perform quality checks on data.
• Build and train models to identify key variables and meet prediction targets.
• Interpret models to generate valuable insights that support business decision-making.
• Monitor and manage models to maintain optimised prediction performance.

This instructor-led, live training in New Zealand (online or on-site) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

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

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimise AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Use tools and techniques for model conversion and optimisation.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in New Zealand (online or on-site) is designed for data scientists, data analysts, and developers who wish to explore AutoML products and features to create and deploy custom ML training models with minimal effort.

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

• Explore the AutoML product line to implement various services for different data types.
• Prepare and label datasets to build custom ML models.
• Train and manage models to produce accurate and fair machine learning outcomes.
• Make predictions using trained models to meet business objectives and needs.

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Course Format

• Guided presentations and collaborative discussions.
• Hands-on labs using real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customisation Options

• Customised versions of this training can be arranged to align with your team’s technology stack and project requirements.

This instructor-led, live training in New Zealand (online or on-site) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow both on-premises and in the cloud.
• Build, deploy, and manage ML workflows using Docker containers and Kubernetes.
• Execute complete machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to launch and manage Jupyter notebooks.
• Construct ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use Google’s ML Kit to build machine learning models that are optimised for processing on mobile devices.

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

• Set up the necessary development environment to start developing machine learning features for mobile apps.
• Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs. 
• Enhance and optimise existing apps using the ML Kit SDK for on-device processing and deployment.

This instructor-led, live training in New Zealand (online or on-site) is designed for data scientists and software engineers who wish to harness Random Forest to build machine learning models for large-scale datasets.

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

• Set up the required development environment to begin building machine learning models using Random Forest.
• Understand the benefits of Random Forest and how to apply it to solve classification and regression problems.
• Learn how to manage large datasets and interpret the output of multiple decision trees within the Random Forest framework.
• Evaluate and optimise machine learning model performance through hyperparameter tuning.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level data analysts who want to learn how to use RapidMiner to estimate and forecast values, and to leverage analytical tools for time series forecasting.

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

• Apply the CRISP-DM methodology, select appropriate machine learning algorithms, and improve model construction and performance.
• Use RapidMiner to estimate and forecast values, and utilise analytical tools for time series forecasting.