HIGAET Machine Learning
Learn applied regression, classification, and model evaluation to train, tune, and compare machine learning models through HIGAET Practical Training projects.
Duration
12 weeks · 5-7 hours/week
Level
Intermediate
Delivery
Hybrid
Status
Open for enrollment
Why this technology matters.
Machine learning is the practice of training models on data to predict, classify, and rank, then tuning and comparing them so the best one actually holds up. It matters now because tabular, text, and time-based data sit at the center of pricing, risk, and personalization decisions.
Practitioners use applied regression, classification, preprocessing, and feature pipelines to solve prediction tasks, with validation strategies that measure true generalization. It does not solve everything: models do not fix leaked or biased training data, no algorithm rescues a poorly framed target, and strong offline metrics do not guarantee business value.
By the end you will be able to build supervised models for regression, classification, and ranking, preprocessing and feature pipelines for tabular, text, and time-based data, and validation and tuning comparisons judged by precision, recall, calibration, and business-aligned metrics.
Why this course exists
The gap is between a tutorial model that scores on a sample and a tuned model that generalizes to new data and a real decision. This course teaches the arc from data framing to preprocessing to training to honest evaluation: preventing leakage, comparing candidates fairly, and aligning metrics with the business outcome.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and core libraries
- Basic statistics and linear algebra
- Familiarity with tabular datasets
Technologies & tools
Skills you'll gain
A 12 weeks arc, module by module.
- Module 01
Module 01 — Foundations: Machine Learning Concepts, Problem Types, and Evaluation Thinking
- Module 02
Module 02 — Core: Data Preparation, Feature Engineering, and Baseline Modeling
- Module 03
Module 03 — Core: Regression, Classification, and Probability Calibration
- Module 04
Module 04 — Core: Tree Models, Ensembles, and Model Comparison Methods
- Module 05
Module 05 — Engineering: Cross-Validation, Tuning, and Experiment Organization
- Module 06
Module 06 — Engineering: Unsupervised Methods, Embeddings, and Feature Extraction
- Module 07
Module 07 — Advanced: Time Series, Imbalanced Data, and Error Analysis
- Module 08
Module 08 — Production: Model Packaging, Batch Scoring, and Responsible ML Review
- Module 09
Module 09 — Capstone: End-to-End Machine Learning Model with Evaluation Report
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Build supervised learning models for regression, classification, and ranking tasks
- Design validation strategies that prevent leakage and measure true generalization
- Develop preprocessing and feature pipelines for tabular, text, and time-based data
- Evaluate models using precision, recall, calibration, and business-aligned metrics
- Automate training and tuning workflows with tracked experiments and reproducible code
- Optimize algorithms through regularization, ensembles, and hyperparameter search
- Integrate trained models into simple services and batch scoring workflows
- Architect a complete modeling project with baselines, comparisons, and deployment notes
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Regression and ranking model
- Project 02
Classification model comparison
- Project 03
Feature preprocessing pipeline
- Project 04
Validation and tuning study
- Capstone
Tuned ML model with evaluation report
Speak the language first.
- Supervised Learning
- Training models on labeled examples to predict outcomes for new inputs.
- Regression
- Predicting continuous values such as prices or demand from input features.
- Classification
- Assigning inputs to categories such as spam or not spam.
- Validation Strategy
- How data is split for training and testing so results reflect true generalization.
- Data Leakage Prevention
- Practices that keep test information out of training so evaluation stays honest.
- Preprocessing Pipelines
- Ordered steps for imputation, scaling, and encoding that are fitted on training data only.
- Hyperparameter Tuning
- Searching settings like tree depth or regularization strength to improve validation performance.
- Precision and Recall
- Metrics that measure false alarms versus missed cases in classification tasks.
- Model Calibration
- Checking that predicted probabilities match real-world outcome rates.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Validation scores look great but test performance collapses
Audit for leakage by checking split order and time boundaries, then rebuild splits so no test information leaks into training.
Model overfits small tabular datasets
Add cross-validation, simplify the model, tune regularization, and compare learning curves before adding features.
Preprocessing leaks statistics from test data
Fit imputers, scalers, and encoders on training folds only, then apply the fitted steps to validation and test sets.
Precision-recall tradeoff misaligned with business need
Revisit the decision threshold against business costs, then select metrics and thresholds with stakeholder review.
Text or time features degrade model performance
Isolate the new features with ablations, check encoding and date-based splits, and keep only features that improve held-out metrics.
Tuning results vary wildly between runs
Fix random seeds, use repeated cross-validation, and compare configurations by mean and spread rather than a single run.
Before you move on, you should be able to
- Build supervised models for regression, classification, and ranking tasks
- Design validation strategies that prevent leakage and measure generalization
- Develop preprocessing and feature pipelines for tabular, text, and time data
- Evaluate models with precision, recall, calibration, and business-aligned metrics
- Tune and compare models with documented evidence
- Explain model tradeoffs and limits to stakeholders
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
Continue in Data & Machine Learning.
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Ready to start HIGAET Machine Learning?
A 12 weeks course — Data & Machine Learning.