Description
Data Science: Supervised Machine Learning in Python
Go beyond theory and build **real, deployable machine learning models** in Python. This course covers the most widely used supervised algorithms—K-Nearest Neighbors, Naive Bayes, Decision Trees, and Perceptrons—with hands-on projects and a full web service deployment using Flask.
Projects You’ll Build
- A spam email classifier using Naive Bayes
- A handwritten digit recognizer with KNN
- A decision tree for medical diagnosis
- A ML web API that serves predictions over HTTP
Algorithms Covered
- K-Nearest Neighbors (KNN)—simple, powerful, instance-based learning
- Naive Bayes—probabilistic classification for text and categorical data
- Decision Trees—interpretable models for complex decisions
- Perceptrons—the foundation of neural networks
Why This Stands Out
- No black boxes—you’ll implement algorithms from scratch using NumPy
- Real-world focus—learn feature scaling, cross-validation, and overfitting
- Deployment-ready—build a REST API so your model can be used by apps
Who Is This For?
- Python developers entering data science
- Students needing practical ML portfolio projects
- Data analysts moving beyond Excel and dashboards
- Self-taught coders filling critical ML knowledge gaps
Your First Step into Applied ML
This course gives you the **confidence to say “I built and deployed a real ML model”**—not just ran a tutorial.
Ready to go from learner to builder? Enroll now.
