Back to Catalog
Machine Learning: The Engineering Approach
Master the algorithms that power modern AI. From Regression to Random Forests, learn how to build, evaluate, and deploy predictive models using Scikit-Learn.
10 Weeks Medium
What you'll build
Master Scikit-Learn Pipeline
Understand Supervised vs Unsupervised Learning
Evaluate models with Precision/Recall/F1
Deploy models with FastAPI
Handle Real-world Dirty Data
Your Progress
Track your journey through this course.
0% Completed0/13 Modules
Your progress is stored locally on this device. Export it to continue on another device.
Curriculum
1. The Scikit-Learn API
2. Supervised: Regression
3. Unsupervised: Clustering
4. Metrics that Matter
5. Deployment with FastAPI
6. Gradient Boosting
7. Feature Engineering
8. AutoML (Optuna)
9. Explainable AI (XAI)
10. Support Vector Machines
11. Naive Bayes (Spam Filtering)
12. Dimensionality Reduction (t-SNE/UMAP)
13. Deployment with Docker
Frequently Asked Questions
Is this Deep Learning?
No. This is Classical ML. You cannot use a Bazooka (Deep Learning) for a fly (Excel data). You need this foundation first.
Math required?
Algebra and basic probability. We explain the intuition (Geometric) rather than the proofs.