Course · Intermediate
Introduction to Supervised Machine Learning in Python
In this course, you'll learn how to build a supervised machine learning model in Python, as well as how to train and improve it for better performance and accuracy.
- Intermediate friendly
- 7 hrs
- 4 lessons
- 1 project
- Premium
Course overview
Develop a supervised machine learning workflow for classification by training, evaluating, and tuning models with scikit-learn on real-world datasets.
What's inside
4 lessons · 1 project
- 01 68 minThe Machine Learning Workflow
<p>Learn what supervised machine learning is, and establish a machine learning workflow to build and train a classifier.</p>
- 02 123 minIntroduction to K-Nearest Neighbors
Learn about the K-Nearest Neighbor algorithm and implement it from scratch.
- 03 64 minEvaluating Model Performance
Implement the K-Nearest Neighbor algorithm using Scikit-learn and learn about evaluating the model's performance.
- 04 80 minHyperparameter Optimization
<p>Learn how to improve a model's performance by selecting relevant features and to tune hyperparameters by utilizing the grid search technique.</p>
- 05 75 minGuided Project: Predicting Heart Disease Project
For this project, we'll take on the role of a data scientist at a healthcare solutions company to build a model that predicts a patient's risk of developing heart disease based on their medical data.
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