Predictive Analytics Training in Varna

This instructor-led live training in Varna (online or onsite) targets intermediate-level developers who wish to build AI-powered applications using predictive analytics and generative models.

Upon completing this training, participants will be able to:

• Understand the fundamentals of predictive AI and generative models.
• Utilize 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 Varna (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:

• Grasp the fundamental concepts and categories of machine learning.
• Utilize essential algorithms for classification, regression, clustering, and association analysis.
• Conduct exploratory data analysis and prepare data using Python.
• Leverage neural networks for nonlinear modeling tasks.
• Deploy predictive analytics for business forecasting, including sales data.
• Assess and enhance model performance through visual and statistical techniques.

This instructor-led, live training in Varna (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 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 Varna (online or onsite) is aimed at intermediate-level DevOps professionals who wish 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 solve challenges in the DevOps pipeline.
• Utilize AI-driven tools for enhanced monitoring and operations.
• Apply machine learning techniques to improve software delivery workflows.
• Design AI strategies for proactive issue resolution and optimization.
• Navigate the ethical considerations of using AI in DevOps.

In this instructor-led live training in Varna, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results.

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

• Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation.
• Implement industrial big data storage and processing solutions for data analysis.
• Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation.

Target Audience

This course is designed for professionals who aim to derive meaningful insights from existing data resources or analyze unstructured information available online, such as content from Twitter, LinkedIn, and similar platforms.

It primarily caters to decision-makers and stakeholders responsible for determining which data sets are valuable for collection and analysis.

While not specifically intended for technical personnel configuring the solution, they will still gain valuable high-level perspectives on the subject matter.

Delivery Mode

Participants will be introduced to practical examples utilizing predominantly open-source technologies throughout the course.

The training structure combines concise lectures with interactive presentations and hands-on exercises led by the delegates.

Content and Software Used

All software employed is updated for every course iteration to ensure the use of the most current versions available.

The curriculum encompasses the entire data lifecycle—from acquisition and formatting to processing and analysis—while demonstrating how to automate decision-making processes through machine learning techniques.

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

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

• Import datasets into DataRobot to analyze, assess, and perform quality checks on data.
• Develop and train models to pinpoint critical variables and achieve prediction goals.
• Interpret model outputs to generate actionable insights that support business decision-making.
• Monitor and oversee models to ensure optimal prediction performance is maintained.

R is a free, open-source programming language designed for statistical computing, data analysis, and graphical representation. It is increasingly utilized by data analysts and managers across both corporate and academic environments. R offers a comprehensive ecosystem of packages tailored for data mining.

Predictive analytics involves leveraging data analysis to forecast future outcomes. This discipline combines data mining, statistics, and machine learning techniques to construct predictive models capable of anticipating upcoming events.

During this instructor-led live training, participants will discover how to utilize Matlab to construct predictive models and apply them to large-scale datasets, enabling predictions about future scenarios based on historical information.

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

• Develop predictive models to uncover patterns within historical and transactional data.
• Employ predictive modeling to pinpoint potential risks and opportunities.
• Construct mathematical models that encapsulate significant trends.
• Leverage data from devices and business systems to minimize waste, conserve time, or reduce expenses.

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• A combination of lectures, discussions, exercises, and intensive hands-on practice.

R is a free, open-source programming language designed for statistical computing, data analysis, and data visualization. It is increasingly utilized by corporate managers and data analysts across both industry and academia. R offers a comprehensive ecosystem of packages tailored for data mining and analytical tasks.

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

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

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

• Install and configure RapidMiner
• Prepare and visualize data using RapidMiner
• Validate machine learning models
• Combine data and build predictive models
• Implement predictive analytics within a business process
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Course Format

• Combination of lectures, discussions, exercises, and extensive hands-on practice

Note

• To request customized training for this course, please contact us to make arrangements.

This live training, led by an instructor and offered online or on-site, is designed for beginner to intermediate-level analysts, researchers, and professionals who want to use SPSS for data preparation, statistical analysis, and predictive modeling.