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Hyperparameter Tuning in Python

Gain experience using techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

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4 Hours13 Videos44 Exercises
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Course Description

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 course 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.
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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.

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    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.

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  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.

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  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.

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In the following tracks

Machine Learning Scientist with PythonSupervised Machine Learning in Python

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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