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.
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 anhttps://websites.godaddy.com/managers-cornerd 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.
The "Complete Data Science & AI Dictionary" workshop offers a comprehensive overview of foundational and advanced AI concepts. This immersive program blends theoretical insights with practical applications, providing a structured journey statistics, data visualization, data analysis, machine learning, deep learning, generative AI, NLPs, data management, big data technologies, IoT, and analytical tools.
Ideal for managers, analysts, and professionals, the workshop equips participants with the knowledge to navigate AI's evolving landscape and apply these technologies effectively across industries.
Designed to demystify AI solutions, it ensures participants gain a solid understanding of Data Science and AI components, enabling them to confidently engage in professional discussions without getting lost in technical jargon.
Day 1:
- The complete roadmap towards AI
- Data design, visualization, and statistical KPIs
Day 2:
- Data Analysis with statistical tests
- Data Management & SQL
Day 3:
- Supervised Machine Learning vs. Trend Analysis
- IoT and Big Data technology essentials
Day 4:
- Unsupervised Machine Learning
- Python vs. Proprietary software
Day 5:
- Deep Learning and Generative Deep Learning
- Generative AI Tools
This five-day workshop provides participants with essential knowledge and skills for automating business processes using Microsoft Power Automate and enhancing productivity with Microsoft Copilot.
The "Complete RPA, Power Automate & Copilot Dictionary" workshop offers a practical introduction to Robotic Process Automation (RPA) and No-Code/Low-Code tools. It combines theory with hands-on applications, covering process automation and workflow optimization.
Designed for business leaders, IT professionals, analysts, and process managers, the workshop empowers participants to automate tasks, eliminate repetitive work, and drive efficiency using Microsoft’s automation tools. It ensures a clear, non-technical understanding of automation implementation across industries.
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With the rapid advancements in technology, the foundation of AI-driven decision-making, particularly through "Supervised" Machine Learning (ML) models and mainly "Neural Networks", has become increasingly accessible to practitioners. Thanks to improved automation tools, mastering these key predictive models is now more achievable.
This workshop provides an in-depth look at "supervised" ML and its one-step improvement, Neural Networks, which play a vital role in enhancing predictive accuracy across various industries. Participants will engage with multiple PYTHON comparative solutions by simply learning how to prompt with ChatGPT. By the end, attendees will have the skills to utilize these powerful algorithms effectively, positioning themselves as proficient practitioners.
Day 1:
- The complete ML and DL encyclopedia
Day 2:
- Linear and Logistic Regressions
- Discriminant analysis
Day 3:
- Decision Trees / Naive Bayes / Support Vector Machines
Day 4:
- Feed Forward Neural Networks
- Sequential Predictive Nets
Day 5:
- Case study workshop
- Compare Machine Learning and Deep Learning approaches.
- Understand data requirements and scalability differences.
- Explore ML models like decision trees and SVMs.
- Learn how DL models like CNNs and RNNs extract complex patterns.
- Apply the right validation techniques: cross-validation vs. train/test splits.
- Discover regularization methods like dropout and early stopping.
- Master analytics: feature engineering vs. automatic representation learning.
- Choose the best approach for your data and business needs.
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