AI Apps with Supervised Machine Learning

Due to rapid technological advancements, the foundation of AI solutions for decision-making, "Supervised" ML models, is now more accessible to practitioners. Mastering the most critical predictive models has become more accessible, especially with the improvement of automation tools.

This workshop provides a comprehensive overview of "supervised" Machine Learning algorithms and their role in improving predictions across various industries. To ensure practical application, it also explores models using different technologies (SAS, Alteryx, STATISTICA, PYTHON, etc.), enabling participants to become professional practitioners and expert consultants by evaluating and selecting the most suitable solution with the right technical package.

Workshop Overview

Learning Outcomes

• Explore the rise of AI with IoT and technology capacities.

• Understand the true meaning of Machine Learning (ML).

• Connect Data Analysis to Machine Learning.

• Make it all with f(X) = y.

• Differentiate between Regression and Classification models.

• Validate models with p-value and Accuracy metrics.

• Improve predictions by testing different ML models.

• Fine-tune models with stepwise methods.

• Compare models using accuracy measures.

• Understand the utility of all cross-validation techniques.

• Design dashboards for comparative models.

• Overview Ensemble Models.

Detailed Course Schedule

• Day 1:

  • Introduction to Machine Learning

  • Multiple Linear Regression

• Day 2:

  • Simple & Multiple Logistic Regression

  • Models Evaluation Indicators

• Day 3:

  • Linear and Quadratic Discriminant Analysis

• Day 4:

  • Decision Trees

  • Random Forest Trees

  • Naive Bayes

• Day 5:

  • Support Vector Machines

  • K Nearest Neighbors

• Colored PPT documents / Videos.

• The multiple-way model generation.

• All-in-one predictive model solution.

• Quality model indicators.

• Team Competition for Best Model Finding.

• Complete case studies from A to Z.

• Proprietary tools for data visualization.

What will it be about?