Course · Intermediate
Optimizing Machine Learning Models in Python
In this course, you'll learn the most common methods and techniques that will enable you to optimize your machine learning models for better efficiency.
- Intermediate friendly
- 4 hrs
- 5 lessons
- 1 project
- Premium
Course overview
Improve machine learning model performance by applying optimization techniques such as cross-validation, regularization, and feature engineering in Python.
What's inside
5 lessons · 1 project
- 01 53 minFeature Engineering
<p>In this lesson, you will explore feature engineering methods to manipulate data, as well as how and when to use them.</p>
- 02 39 minModel Selection
<p><span style="color: rgb(0,0,0);font-size: medium;">In this lesson, you will learn different techniques for model selection, as well as how to choose the best model among multiple candidates. </span></p>
- 03 26 minCross-Validation
<p><span style="color: rgb(0,0,0);font-size: medium;font-family: -apple-system, "system-ui", "Segoe UI", Roboto, Oxygen, Ubuntu, Cantarell, "Fira Sans", "Droid Sans", "Helvetica Neue", sans-serif;">In this lesson, you will learn how to use k-fold cross-validation to assess model performance and see how test error can vary.</span></p>
- 04 31 minRegularization
<p><span style="color: rgb(0,0,0);font-size: medium;">In this lesson, we will learn about the concept of regularization and how to use it in linear models.</span></p>
- 05 37 minGoing Beyond Linear Models
<p><span style="color: rgb(0,0,0);font-size: medium;">In this lesson, you will learn how to create non-linear models that can capture more complex feature-outcome relationships. You will learn how to create polynomial models as well as splines using scikit-learn.</span></p>
- 06 11 minGuided Project: Optimizing Model Prediction Project
For this project, we'll step into the role of data scientists to predict forest fire damage using optimized machine learning models in Python.
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