AI's Machine and Deep Learning Predictive Models

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. At the end, attendees will have the skills to utilize these powerful algorithms effectively, positioning themselves as proficient practitioners.

Workshop Overview

Learning Outcomes

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

• Understand the difference between Statistical Data Analysis, Machine Learning, and Deep Learning models.

• Differentiate between Regression and Classification models.

• Avoid the high-performance illusion of predictive models.

• Validate and evaluate ML and DL models.

• Selecting between ML and DL models.

• Optimize models with hyperparameter fine-tuning.

• Compare ML and DL models using accurate measures.

• Design dashboards for comparative models.

• Prompting with ChatGPT.

Detailed Course Schedule

• 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.

What will it be about?