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Hyperparameter Tuning in Python

4.7+
12 reviews
Intermediate

Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

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Course Description

As a data or machine learning scientist, 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? The answer is hyperparameter tuning!

Hyperparameters vs. parameters

Gain practical experience using various methodologies for automated hyperparameter tuning in Python with Scikit-Learn.

Learn the difference between hyperparameters and parameters and best practices for setting and analyzing hyperparameter values. This foundation will prepare you to understand the significance of hyperparameters in machine learning models.

Grid search

Master several hyperparameter tuning techniques, starting with Grid Search. Using credit card default data, you will practice conducting Grid Search to exhaustively search for the best hyperparameter combinations and interpret the results.

You will be introduced to Random Search, and learn about its advantages over Grid Search, such as efficiency in large parameter spaces.​

Informed search

In the final part of the course, you will explore advanced optimization methods, such as Bayesian and Genetic algorithms.

These informed search techniques are demonstrated through practical examples, allowing you to compare and contrast them with uninformed search methods. By the end, you will have a comprehensive understanding of how to optimize hyperparameters effectively to improve model performance​.
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In the following Tracks

Machine Learning Scientist in Python

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Supervised Machine Learning in Python

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  1. 1

    Hyperparameters and Parameters

    Free

    In this introductory chapter you will learn the difference between hyperparameters and parameters. You will practice extracting and analyzing parameters, setting hyperparameter values for several popular machine learning algorithms. Along the way you will learn some best practice tips & tricks for choosing which hyperparameters to tune and what values to set & build learning curves to analyze your hyperparameter choices.

    Play Chapter Now
    Introduction & 'Parameters'
    50 xp
    Parameters in Logistic Regression
    50 xp
    Extracting a Logistic Regression parameter
    100 xp
    Extracting a Random Forest parameter
    100 xp
    Introducing Hyperparameters
    50 xp
    Hyperparameters in Random Forests
    50 xp
    Exploring Random Forest Hyperparameters
    100 xp
    Hyperparameters of KNN
    100 xp
    Setting & Analyzing Hyperparameter Values
    50 xp
    Automating Hyperparameter Choice
    100 xp
    Building Learning Curves
    100 xp
  2. 2

    Grid search

    This chapter introduces you to a popular automated hyperparameter tuning methodology called Grid Search. You will learn what it is, how it works and practice undertaking a Grid Search using Scikit Learn. You will then learn how to analyze the output of a Grid Search & gain practical experience doing this.

    Play Chapter Now
  3. 3

    Random Search

    In this chapter you will be introduced to another popular automated hyperparameter tuning methodology called Random Search. You will learn what it is, how it works and importantly how it differs from grid search. You will learn some advantages and disadvantages of this method and when to choose this method compared to Grid Search. You will practice undertaking a Random Search with Scikit Learn as well as visualizing & interpreting the output.

    Play Chapter Now
  4. 4

    Informed Search

    In this final chapter you will be given a taste of more advanced hyperparameter tuning methodologies known as ''informed search''. This includes a methodology known as Coarse To Fine as well as Bayesian & Genetic hyperparameter tuning algorithms. You will learn how informed search differs from uninformed search and gain practical skills with each of the mentioned methodologies, comparing and contrasting them as you go.

    Play Chapter Now
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In the following Tracks

Machine Learning Scientist in Python

Go To Track

Supervised Machine Learning in Python

Go To Track

datasets

Credit Card Defaults

collaborators

Collaborator's avatar
Hadrien Lacroix
Collaborator's avatar
Chester Ismay
Alex Scriven HeadshotAlex Scriven

Senior Data Scientist @ Atlassian

Alex is a Senior Data Scientist working for Atlassian in Sydney, Australia and has previous experience in government, agency and startup. He also holds lecturing and research positions at the University of Technology Sydney and the University of New South Wales. He has built and delivered several Masters-level courses in machine learning & deep learning whilst researching on applications of machine learning & data science in industry. From a heavily commercial background, Alex greatly enjoys bridging the gap between cutting-edge technology and business applications.
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Don’t just take our word for it

*4.7
from 12 reviews
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  • Clark B.
    about 2 months

    Very informative

  • Alfredo V.
    6 months

    Recomendado, explica clara y precisa.

  • Louis B.
    9 months

    This was one of the best courses in the Machine Learning Scientist track up to this point. Very practical. Left me wanting more on this topic.

  • Ayoub M.
    11 months

    everything was good .. I like this platform so far

  • tinh n.
    12 months

    The course is very useful. I like it.

"Very informative"

Clark B.

"Recomendado, explica clara y precisa."

Alfredo V.

"This was one of the best courses in the Machine Learning Scientist track up to this point. Very practical. Left me wanting more on this topic."

Louis B.

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