Overview
Join over 9 million learners and start Hyperparameter Tuning in Python today!
Building powerful machine learning models depends heavily on the set of hyperparameters used. But with increasingly complex models with lots of options, how do you efficiently find the best settings for your particular problem? In this Hyperparameter Tuning in Python course at Data Camp, you will get practical experience in using some common methodologies for automated hyperparameter tuning in Python using Scikit Learn. These include Grid Search, Random Search & advanced optimization methodologies including Bayesian & Genetic algorithms . You will use a dataset predicting credit card defaults as you build skills to dramatically increase the efficiency and effectiveness of your machine learning model building.
Programme Structure
Chapters include:
- Hyperparameters and Parameters
- Random Search
- Grid search
- Informed Search
Key information
Duration
- Part-time
- 1 days
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Disciplines
Software Engineering View 554 other Short Courses in Software Engineering in United StatesAcademic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Prerequisites
- Intermediate Python
- Supervised Learning with scikit-learn
Tuition Fee
To always see correct tuition fees
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International
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 1 days. -
National
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 1 days.
Basic Access: Free; Premium (for individuals): $12.42 per month billed annually; Teams: $25 per month billed annually; Enterprise: Contact sales for pricing
Funding
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