Machine Learning and Big Data 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 instructor-led, live training in New Zealand (delivered online or on-site) is designed for data scientists, as well as less technical professionals, who wish to use Auto-Keras to automate the selection and optimisation of machine learning models.

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

• Automate the process of training highly efficient machine learning models.
• Automatically search for the best parameters for deep learning models.
• Build highly accurate machine learning models.
• Apply machine learning to solve real-world business challenges.

This instructor-led, live training (online or on-site) is designed for technical participants with a background in machine learning who wish to optimise models for detecting complex patterns in big data using AutoML frameworks.

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 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.

This instructor-led, live training in New Zealand (available online or onsite) is tailored for 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 using AWS EKS (Elastic Kubernetes Service).
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Execute complete machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to spawn and manage Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in New Zealand (online or on-site) is tailored for engineers who wish to deploy Machine Learning workloads to an AWS EC2 server.

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

• Install and configure Kubernetes, Kubeflow, and other required software on AWS.
• Utilise EKS (Elastic Kubernetes Service) to simplify the process of initialising a Kubernetes cluster on AWS.
• Create and deploy a Kubernetes pipeline to automate and manage ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines operating in parallel.
• Leverage other AWS managed services to extend an ML application.

This instructor-led, live training (available online or on-site) is aimed at data scientists who wish to move beyond building machine learning models and instead optimise the entire model creation, tracking, and deployment process.

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

• Install and configure MLflow along with related machine learning libraries and frameworks.
• Appreciate the importance of trackability, reproducibility, and deployability in machine learning models.
• Deploy machine learning models across different public clouds, platforms, or on-premise servers.
• Scale the machine learning deployment process to support multiple users collaborating on a project.
• Establish a central registry to experiment with, reproduce, and deploy machine learning models.

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.

Pattern Matching is a method employed to identify specific patterns within an image. This technique allows users to confirm the presence of defined characteristics in a captured image, such as verifying the correct label on a defective item along a factory line or checking the specified dimensions of a component. Unlike "Pattern Recognition," which identifies general patterns from broad collections of related data, pattern matching is specific: it defines exactly what to look for and confirms whether that expected pattern exists.

Course Format

• This course covers the approaches, technologies, and algorithms utilised in pattern matching as it applies to Machine Vision.

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.

RapidMiner is an open-source data science platform designed for rapid application prototyping and development. It features an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.

In this instructor-led, live training, participants will learn how to use RapidMiner Studio for data preparation, machine learning, and the deployment of predictive models.

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

• Install and configure RapidMiner
• Prepare and visualise data using RapidMiner
• Validate machine learning models
• Mash up data and build predictive models
• Operationalise predictive analytics within business processes
• Troubleshoot and optimise RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Course Format

• A blend of lecture, discussion, exercises, and extensive hands-on practice

Note

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