Predictive Analytics Training in Hsinchu

This instructor-led, live training in Hsinchu (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.
• 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 Hsinchu (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 Hsinchu (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 Hsinchu (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 Hsinchu, 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.

Audience

If you are looking to interpret the data at your disposal or analyze unstructured data available on platforms like Twitter, LinkedIn, and others, this course is designed for you.

The primary target audience includes decision-makers and individuals who need to determine which data is valuable to collect and analyze.

While not specifically aimed at those configuring the solution, they will still gain a broad understanding of the big picture.

Delivery Mode

During the course, participants will be presented with working examples of predominantly open-source technologies.

The sessions will consist of short lectures followed by presentations and simple exercises for the attendees.

Content and Software Used

All software used in the course is updated each time it is delivered, ensuring that the latest versions are utilized.

The curriculum covers the entire process from data acquisition, formatting, processing, and analysis, to explaining how to automate decision-making processes with machine learning.

This instructor-led, live training in Hsinchu (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 in DataRobot to analyze, assess, and quality check data.
• Build and train models to identify important variables and meet prediction targets.
• Interpret models to create valuable insights that are useful in making business decisions.
• Monitor and manage models to maintain an optimized prediction performance.

R is an open-source programming language that is freely available for statistical computing, data analysis, and graphical visualization. It is increasingly being adopted by managers and data analysts both in corporate settings and academic institutions. R offers a vast array of packages specifically designed for data mining.

Predictive analytics involves using data analysis to forecast future outcomes. This method combines data with data mining, statistical techniques, and machine learning to develop predictive models that can anticipate future events.

In this instructor-led, live training, participants will learn how to use Matlab to construct predictive models and apply them to extensive datasets to predict future scenarios based on the available data.

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

• Create predictive models to identify patterns in historical and transactional data
• Leverage predictive modeling to recognize potential risks and opportunities
• Develop mathematical models that capture significant trends
• Utilize data from devices and business systems to enhance efficiency, save time, or reduce costs

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Part lecture, part discussion, with exercises and extensive hands-on practice

R is an open-source programming language that is widely used for statistical computing, data analysis, and graphical representation. It is increasingly favored by managers and data analysts in both corporate settings and academic institutions. R offers a vast array of packages designed for data mining, making it a versatile tool for various analytical tasks.

RapidMiner is an open-source data science software platform designed for quick application prototyping and development. It offers 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 visualize data using RapidMiner
• Validate machine learning models
• Integrate data and create predictive models
• Implement predictive analytics within a business process
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course

• Part lecture, part discussion, exercises, and extensive hands-on practice

Note

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

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level analysts, researchers, and professionals who wish to use SPSS for data preparation, statistical analysis, and predictive modeling.