Machine Learning in Practice
The practitioner course: supervised and unsupervised learning, feature engineering, choosing and training the right model, and evaluating it rigorously through a complete practical project.
4 modules · 12 lessons
01
Core ML Concepts
- Supervised vs. unsupervised learning
- Choosing the right approach for a problem
- Overfitting, underfitting and generalization
02
Feature Engineering
- Turning raw data into useful features
- Handling missing and imbalanced data
- Feature selection basics
03
Model Selection and Training
- Comparing model families with scikit-learn
- Training and hyperparameter basics
- Cross-validation
04
Evaluation and Real Projects
- Evaluation metrics that matter
- Diagnosing a struggling model
- A complete practical ML project
Before and after this course.
Before you begin
- Completion of Python for AI & Data or equivalent experience
- Basic familiarity with core ML concepts is helpful
- Comfort working with data in Python
What this actually builds
- Build and evaluate ML models on real datasets
- Diagnose and improve an underperforming model
- A complete ML project to show for it
Certificate of Completion
Earn recognition upon course completion
Receive a certificate upon meeting the program's completion criteria. Add it to LinkedIn or your resume as a record of what you built.
Certificate of Completion
Machine Learning in Practice
- Shareable on LinkedIn and social media
- Verifiable by employers and institutions
- Includes course completion details and skills
Reflects real work
Tied to modules and projects you actually complete
Completion-based
Issued only when the completion criteria are met
Shareable
Add it to LinkedIn or your resume