Introduction to Machine Learning Training Course

This instructor-led live training in Australia (online or onsite) is designed for data scientists and software engineers who wish to use AdaBoost to build boosting algorithms for machine learning with Python.

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

• Configure the necessary development environment to begin building machine learning models with AdaBoost.
• Gain an understanding of ensemble learning approaches and how to implement adaptive boosting.
• Learn how to construct AdaBoost models to enhance machine learning algorithms in Python.
• Utilise hyperparameter tuning to improve the accuracy and performance of AdaBoost models.

This course explores the application of AI—specifically Machine Learning and Deep Learning—within the automotive sector. It assists learners in identifying technologies that can be applied across various automotive scenarios, ranging from basic automation and image recognition to complex autonomous decision-making systems.

An exploration of artificial intelligence fundamentals reveals how intelligent technology reshapes digital strategy, automation, and decision making across enterprise operations. Examines core concepts spanning AI history, problem-solving frameworks, knowledge representation, uncertain reasoning, and machine learning paradigms alongside communication, perception, and autonomous action. Guides executives and architects 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 Australia (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.

Artificial Neural Networks serve as a computational data model employed in creating Artificial Intelligence (AI) systems capable of executing tasks considered 'intelligent'. Neural Networks are frequently utilised within Machine Learning (ML) applications, which represent one specific implementation of AI. Deep Learning constitutes a specialised subset of Machine Learning.

This instructor-led live training in Australia (online or onsite) 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 Australia (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in 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 like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

This instructor-led live training in Australia (online or onsite) is tailored for data scientists and data analysts who wish to automate, evaluate, and manage predictive models leveraging DataRobot's machine learning capabilities.

Upon completing this training, participants will be able to:

• Load datasets into DataRobot to analyse, assess, and quality-check data.
• Construct and train models to identify key variables and achieve prediction targets.
• Interpret models to generate valuable insights that inform business decisions.
• Monitor and manage models to ensure optimised prediction performance.

This instructor-led, live training in Australia (available online or onsite) is geared towards intermediate-level developers, data scientists, and AI practitioners who aim to utilise TensorFlow Lite for Edge AI applications.

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

• Comprehend the fundamentals of TensorFlow Lite and its function in Edge AI.
• Construct and enhance AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models across various edge devices.
• Apply tools and techniques for model conversion and optimisation.
• Develop practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Australia (online or onsite) 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 different services for various data types.
• Prepare and label datasets to create custom ML models.
• Train and manage models to produce accurate and fair machine learning models.
• 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.

Format of the Course

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

Course Customization 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 Australia (online or onsite) is tailored for developers and data scientists who aim to build, deploy, and manage machine learning workflows on Kubernetes.

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

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

This instructor-led, live training (available online or onsite) is targeted at developers looking to utilise Google’s ML Kit to construct machine learning models optimised for mobile device processing.

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

• Configure the required development environment to begin building machine learning features for mobile applications.
• Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs. 
• Improve and optimise existing applications by leveraging the ML Kit SDK for on-device processing and deployment.

This instructor-led, live training in Australia (online or onsite) is aimed at data scientists and software engineers who wish to use Random Forest to build machine learning algorithms for large datasets.

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

• Set up the necessary development environment to start building machine learning models with Random forest.
• Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
• Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
• Evaluate and optimize machine learning model performance by tuning the hyperparameters.

This instructor-led, live training in Australia (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilise analytical tools for time series forecasting.

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

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