Ensemble Methods in Python, Short Course | Part time online | Data Camp | United States
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Short Online

Ensemble Methods in Python

1 days
Duration
Free
Free
Unknown
Tuition fee
Anytime
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Apply date
Anytime
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Start date

About

In this Ensemble Methods in Python course at Data Camp, you’ll learn all about these advanced ensemble techniques, such as bagging, boosting, and stacking.

Overview

Context of the Ensemble Methods in Python course at Data Camp

Continue your machine learning journey by diving into the wonderful world of ensemble learning methods!

These are an exciting class of machine learning techniques that combine multiple individual algorithms to boost performance and solve complex problems at scale across different industries.

 Ensemble techniques regularly win online machine learning competitions as well! You’ll apply them to real-world datasets using cutting edge Python machine learning libraries such as scikit-learn, XGBoost, CatBoost, and mlxtend.

Programme Structure

Chapters

  • Combining Multiple Models
  • Bagging
  • Boosting
  • Stacking

Key information

Duration

  • Part-time
    • 1 days

Start dates & application deadlines

You can apply for and start this programme anytime.

Language

English

Delivered

Online

Academic 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

  • Linear Classifiers in Python
  • Machine Learning with Tree-Based Models in Python

Tuition Fee

To always see correct tuition fees
  • International

    Free
    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 1 days.
  • National

    Free
    Tuition Fee
    Based 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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