Designing a Professional Corporate AI Project

This five-day training program provides a comprehensive approach to designing and implementing Data Science and AI projects in any company.

It addresses AI ethics, focusing on bias, transparency, and regulatory compliance, and explores business strategies that utilize Porter's principles to balance AI innovation with effective decision-making. Participants will learn about various Data Science applications, including Machine Learning, Deep Learning, Robotic Process Automation, and Generative AI.

The program emphasizes practical implementation alongside theoretical concepts, teaching attendees to leverage AI for predictive analytics, manage requests via WhatsApp, automate workflows, and create performance monitoring flowcharts.

Workshop Overview

Learning Outcomes

• Understand AI business ethics and strategies and apply Data Science, ML, and DL in AI project development.

• Apply statistical analysis and predictive modeling for AI-driven decision-making.

• Implement Machine Learning & Deep Learning solutions for business intelligence and automation.

• Pipelining NLPs to Machine Learning

• Explore Robotic Process Automation (RPA) to streamline repetitive business processes.

• Integrate ethical and regulatory frameworks into AI project development.

• Develop a complete AI project roadmap from strategy to deployment.

Detailed Course Schedule

• Day 1:

  • Introduction to AI

  • AI Ethics and Business Strategies

  • Porter's rules of thumb

• Day 2:

  • CRISP-MD overview

  • Transforming a business into efficient data sets

  • Business automation with RPA

• Day 3:

  • Data Visualization, Statistical KPIs, and Data Analysis dashboards

• Day 4:

  • Predictive Machine Learning (Supervised) and Trends

  • Explorative Machine Learning (Unsupervised)

• Day 5:

  • Quality Control with Statistical Measurements and Deep Learning

  • Generative AI for managerial automation

  • Role of Generative AI Tools in the automation process

• Data Science or AI?

• AI ethics and governance considerations.

• Porter’s strategic rules for balancing AI adoption in competitive markets.

• CRISP-DM framework for AI project management.

• Translating a project into meaningful data.

• Data Visualization and statistical profiling tools.

• Predictive analytics with:

  • Machine Learning: Regressions, Decision Trees, SVMs, PCA, ...

  • Deep Learning: FFNN, CNN, RNN, LSTM, ...

  • NLP: Text classification, LLMs, ...

• Automating workflows, document processing, and customer service tasks with RPAs.

• Controlling production with Control Charts and CNN algorithms.

• AI-based inventory management, stock forecasting, and supply chain automation with Generative AI solutions.

• Role of LLMs (ChatGPT, DeepSeek) in the automation process.

What will it be about?