Basics of Hyperparameter Tuning

This course will allow learners to understand the concept of hyperparameter tuning for both regression and classification models that can help in improving the overall model performance, along with their hands-on application in real-world business cases.

  • icon-videos 2 Videos
  • icon-data 6 Data Cases
  • icon-reading 2 Readings
  • icon-quiz 4 Quizzes
Basics of Hyperparameter Tuning
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    Difficulty: Advanced

    Prior knowledge or professional experience is strongly recommended.

  • Asset 1
    Duration: Approximately 12 hours

Course Overview

  • Linear and Logistic regression are one of the most widely used algorithms in the realm of machine learning and data science.
  • Models can have a number of hyperparameters which can significantly affect the performance based on how optimally the values are chosen.
  • Learn all the steps required to optimize these algorithms for performance improvement through hyperparameter tuning.
  • Gain practical experience by apply the understanding in 6 real-world based business scenarios.

What’s included

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Lifetime Access

Access this project for life once completed

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Flexible Scheduling

Start learning online immediately, at your own pace

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Desktop Only

We recommend completing this project on a desktop

Skills You Will Learn

Machine Learning

Model Building

Exploratory Data Analysis

Hyperparameter Tuning

Model Building

Model Evaluation and Selection

Hyperparameter Tuning

Hyperparameters for Linear Regression

Hyperparameters for Logistic Regression

Associated Learning Tracks

Syllabus

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