Machine Learning with Python
The core ML toolkit — regression, classification, trees, ensembles and model evaluation, practised on real datasets every week.
About this course
A rigorous, hands-on pass through classical machine learning. Every algorithm is taught three ways: the intuition, the scikit-learn implementation, and a real dataset where it wins or fails. You learn to frame problems, engineer features, tune models honestly, and read metrics the way employers expect. The capstone is a complete ML project from raw data to a defended result.
Who it's for: Learners comfortable with basic Python (our Python for AI course or equivalent) ready to become ML practitioners.
What you'll learn
Syllabus
Foundations
Weeks 1–3- The ML workflow
- Linear and logistic regression
- Loss, gradient descent, overfitting
Core algorithms
Weeks 4–6- Decision trees and random forests
- Gradient boosting (XGBoost)
- k-NN, SVMs and clustering
Real-world practice
Weeks 7–9- Feature engineering
- Imbalanced data and leakage
- Pipelines and cross-validation
Capstone
Weeks 10–12- A full project on a real dataset
- Model selection and tuning
- Presenting results to non-experts
Tools you'll use
Certificate
Finish the course and its capstone project to earn a FuturAIse Academy Certificate of Completion — verifiable online, with the projects to back it up.