RapidMiner for Machine Learning and Predictive Analytics Training Course

In this instructor-led, live training in New Zealand, participants will develop the mindset required to approach Big Data technologies, evaluate their impact on existing processes and policies, and implement these tools to identify criminal activity and prevent crime. Case studies from law enforcement agencies worldwide will be examined to gain insights into their adoption strategies, challenges, and outcomes.

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

• Integrate Big Data technology with traditional data collection methods to construct a cohesive narrative during investigations.
• Deploy industrial-grade big data storage and processing solutions for data analysis.
• Prepare a proposal for adopting the most suitable tools and processes to enable a data-driven approach to criminal investigation.

Audience

If you're looking to make sense of the data you have access to, or wish to analyse unstructured data available online (such as from Twitter, LinkedIn, and similar platforms), this course is designed for you.

It is primarily aimed at decision-makers and professionals who need to determine which data is worth collecting and which is worth analysing.

This course is not intended for those configuring the solution, although they will still gain valuable insights from the broader perspective presented.

Delivery Mode

Throughout the course, delegates will be introduced to practical examples using predominantly open-source technologies.

Short lectures will be followed by presentations and hands-on exercises led by participants.

Content and Software Used

All software used is updated with each course offering to ensure the latest available versions are covered.

The course covers the entire process—from acquiring, formatting, processing, and analysing data—to explaining how to automate decision-making through machine learning.

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 onsite) is aimed at intermediate-level developers who wish to build AI-powered applications using predictive analytics and generative models.

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

• Understand the fundamentals of predictive AI and generative models.
• Utilise AI-powered tools for predictive coding, forecasting, and automation.
• Implement LLMs (Large Language Models) and transformers for text and code generation.
• Apply time-series forecasting and AI-based recommendations.
• Develop and fine-tune AI models for real-world applications.
• Evaluate ethical considerations and best practices in AI deployment.

This instructor-led, live training in New Zealand (online or onsite) is aimed at beginner-level IT professionals who wish to grasp the fundamentals of Predictive AI.

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

• Understand the core concepts of Predictive AI and its applications.
• Collect, clean, and preprocess data for predictive analysis.
• Explore and visualise data to uncover insights.
• Build basic statistical models to make predictions.
• Evaluate the performance of predictive models.
• Apply Predictive AI concepts to real-world scenarios.

R is an open-source, free programming language designed for statistical computing, data analysis, and graphics. It is increasingly adopted by managers and data analysts across both corporate and academic sectors. R offers a wide array of packages tailored for data mining tasks.

Predictive analytics is the process of leveraging data analytics to forecast future outcomes. This approach combines data with data mining, statistical methods, and machine learning techniques to develop predictive models that anticipate future events.

In this instructor-led, live training, participants will learn how to use Matlab to construct predictive models and apply them to large sample data sets, enabling predictions about future events based on historical data.

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

• Develop predictive models to analyse patterns in historical and transactional data
• Apply predictive modelling to identify risks and uncover opportunities
• Build mathematical models that capture key trends
• Leverage data from devices and business systems to reduce waste, save time, or cut costs

Audience

• Developers
• Engineers
• Domain experts

Format of the course

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

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level data professionals who wish to apply machine learning techniques to data-driven business challenges, including sales forecasting and predictive modelling using neural networks.

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

• Understand the core concepts and types of machine learning.
• Apply key algorithms for classification, regression, clustering, and association analysis.
• Perform exploratory data analysis and data preparation using Python.
• Use neural networks for non-linear modelling tasks.
• Implement predictive analytics for business forecasting, including sales data.
• Evaluate and optimise model performance using visual and statistical techniques.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level DevOps professionals seeking to integrate predictive AI into their DevOps practices.

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

• Implement predictive analytics models to forecast and resolve challenges within the DevOps pipeline.
• Leverage AI-driven tools for improved monitoring and operational efficiency.
• Apply machine learning techniques to refine and enhance software delivery workflows.
• Design AI strategies that support proactive issue resolution and system optimisation.
• Navigate the ethical considerations associated with using AI in DevOps environments.

R is an open-source, free programming language for statistical computing, data analysis, and graphics. It is increasingly used by managers and data analysts within corporations and academia. R offers a wide range of packages for data mining.

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.

This instructor-led, live training (available online or on-site) is designed for beginner to intermediate-level analysts, researchers, and professionals who wish to leverage SPSS for data preparation, statistical analysis, and predictive modelling.