Machine Learning
Train your team to build and evaluate ML models
About Machine Learning Training
A complete, end-to-end machine learning training built for team autonomy. In three days, participants learn how to prepare data, choose the right learning approach, train models, and evaluate results properly. The goal is not to turn everyone into a software engineer; it’s to build solid understanding so teams can apply machine learning methods correctly, interpret outcomes, and make good decisions about model usage.
The training prioritizes conceptual mastery and practical interpretation. Pre-written code and guided examples are used to illustrate algorithms so participants focus on the “why” and “how to evaluate” rather than losing time on implementation details.
The eaQbe Methodology: A Progressive Learning Curve
We don’t believe in training that overwhelms participants with theory. We believe in structured immersion. Our “3-Stage Autonomy Arc” ensures concepts aren’t just understood ; they’re truly mastered.
1) Foundations & Data Readiness
Participants learn how data quality, distributions, leakage, and preprocessing decisions directly influence model behavior. They build strong reflexes around feature engineering, encoding, scaling, missing values, and splitting strategies.
2) Learning Methods & Model Training
Participants learn the core families of supervised and unsupervised learning, when to use them, and what their assumptions imply. They practice training and comparing models using the right evaluation logic for each task.
3) Evaluation, Interpretation & Model Choice
Participants master the foundations of evaluation and interpretation: selecting metrics, reading errors, understanding overfitting, using validation correctly, and making informed trade-offs between performance, stability, and simplicity.
Continuous knowledge validation
Each module intentionally reuses and builds on the previous one. Concepts are applied repeatedly in new exercises so understanding becomes durable, not superficial.
Learning outcomes
- A clear, shared foundation of ML concepts and vocabulary
- Strong habits in data preparation and feature engineering
- The ability to train and compare supervised and unsupervised models
- Confidence in evaluation: choosing metrics, reading results, and spotting common pitfalls
- Better decision-making on when ML is appropriate and how to use results responsibly
Practical details
- Format: 3-day interactive workshop
- Group size: 3–6 participants
- Prerequisites: none
- Follow-up: evaluation + practical exercises to anchor post-training
From Data Preparation to Model Evaluation (3 days)
Module 1 - Data Preparation & Feature Engineering
Understanding data properties, cleaning strategies, feature creation, preprocessing choices, and what can break model validity (leakage, target contamination, biased splits).
Module 2 - Training & Evaluation Fundamentals
Train/validation logic, generalization, overfitting vs underfitting, bias/variance intuition, baseline thinking, and metric selection principles.
Module 3 - Supervised Learning: Parametric Methods
Core intuition and usage of models like linear/logistic regression and neural networks (what they learn, what they assume, what to watch in evaluation).
Module 4 - Supervised Learning: Non-Parametric Methods
Decision trees, random forests, gradient boosting (e.g., XGBoost-style), and KNN—how they behave, how to tune sensibly, and how to compare models fairly.
Module 5 - Unsupervised Learning Essentials
Clustering and pattern discovery fundamentals (use cases, limits, and how to interpret results), with practical evaluation habits.
Final outcome (end of day 3)
Participants can prepare data correctly, train the main model families, evaluate results with the right metrics, and interpret outputs with confidence.
