Introduction to Machine Learning Corporate Training

Develop Machine Learning Judgment Through Models, Features, and Honest Validation

Edstellar introduces machine learning through business problem framing and comparative modelling. Participants explore regression, classification, and clustering, then assemble feature pipelines and evaluate results with suitable validation. The final solution connects model choice to the original question, with feedback on imbalance, feature preparation, tuning, and the evidence supporting performance claims.

1. Step 01

   Consult

   We assess Python basics, confidence working with data, and expectations about prediction or segmentation to identify the support needed for a first machine learning project.

2. Step 02

   Plan

   We frame ML tasks before comparing regression, classification, and clustering, then connect feature pipelines, validation, and tuning in a complete introductory solution.

3. Step 03

   Align

   We align machine learning practitioner expertise with analysts, engineers, and technical managers, confirming Python and scikit-learn readiness and accessible datasets for guided modelling.

4. Step 04

   Customize

   We choose sample regression, classification, and clustering cases relevant to the cohort's work, retaining imbalance and feature-preparation challenges so model comparisons require explicit reasoning.

5. Step 05

   Execute

   Participants compare models, handle imbalanced classes, interpret clusters, and build feature pipelines, receiving coaching on validation design and what the reported metrics actually demonstrate.

6. Step 06

   Evaluate

   We assess the final solution for suitable task framing, justified model and feature choices, sound validation, and performance conclusions that remain within the available evidence.