Predictive Analytics Training in Gaia

This instructor-led, live training in Gaia (online or on-site) is designed for intermediate-level developers who wish to build AI-powered applications using predictive analytics and generative models.

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

• Comprehend the fundamentals of predictive AI and generative models.
• Utilize AI-driven tools for predictive coding, forecasting, and automation.
• Implement Large Language Models (LLMs) and transformers for text and code generation.
• Apply time-series forecasting and AI-based recommendation systems.
• Develop and fine-tune AI models for practical, real-world use cases.
• Assess ethical considerations and adhere to best practices in AI deployment.

This instructor-led, live training in Gaia (online or onsite) is aimed at intermediate-level data professionals who wish to apply machine learning techniques to data-driven business problems, including sales forecasting and predictive modeling 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 nonlinear modeling tasks.
• Implement predictive analytics for business forecasting, including sales data.
• Evaluate and optimize model performance using visual and statistical techniques.

This instructor-led, live training in Gaia (offered online or onsite) is designed for beginner-level IT professionals who want to master the fundamentals of Predictive AI.

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

• Understand the core concepts of Predictive AI and their applications.
• Collect, clean, and preprocess data for predictive analysis.
• Explore and visualize 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.

This instructor-led, live training in Gaia (online or onsite) 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.
• Utilise AI-driven tools to improve monitoring and operations.
• Apply machine learning techniques to enhance software delivery workflows.
• Design AI strategies for proactive issue resolution and optimisation.
• Navigate the ethical considerations of using AI in DevOps.

In this instructor-led live training in Gaia, participants will learn the mindset required to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies to identify criminal activity and prevent crime. Case studies from law enforcement organizations worldwide will be examined to gain insights into their adoption approaches, challenges, and results.

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

• Integrate Big Data technology with traditional data gathering processes to construct a narrative during an investigation.
• Implement industrial 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.

• Understand the importance of ethics in AI development and deployment.

• Learn to identify and mitigate ethical risks associated with AI.

• Gain insights into regulatory and compliance requirements.

• Develop strategies for fostering an ethical AI culture within their organisations.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

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