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.

Introduction & 'Parameters'

Parameters in Logistic Regression

Extracting a Logistic Regression parameter

Extracting a Random Forest parameter

Introducing Hyperparameters

Hyperparameters in Random Forests

Exploring Random Forest Hyperparameters

Hyperparameters of KNN

Setting & Analyzing Hyperparameter Values

Automating Hyperparameter Choice

Building Learning Curves

Hyperparameters and Parameters

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.

Introducing Grid Search

Build Grid Search functions

Iteratively tune multiple hyperparameters

How Many Models?

Grid Search with Scikit Learn

GridSearchCV inputs

GridSearchCV with Scikit Learn

Understanding a grid search output

Using the best outputs

Exploring the grid search results

Analyzing the best results

Using the best results

Grid 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.

Introducing Random Search

Randomly Sample Hyperparameters

Randomly Search with Random Forest

Visualizing a Random Search

Random Search in Scikit Learn

RandomSearchCV inputs

The RandomizedSearchCV Object

RandomSearchCV in Scikit Learn

Comparing Grid and Random Search

Comparing Random & Grid Search

Grid and Random Search Side by Side

Random 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.

Informed Search: Coarse to Fine

Visualizing Coarse to Fine

Coarse to Fine Iterations

Informed Search: Bayesian Statistics

Bayes Rule in Python

Bayesian Hyperparameter tuning with Hyperopt

Informed Search: Genetic Algorithms

Genetic Hyperparameter Tuning with TPOT

Analysing TPOT's stability

Congratulations!

Informed Search

Credit Card Defaults

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.

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! <br><br><h2>Hyperparameters vs. parameters</h2>
Gain practical experience using various methodologies for automated hyperparameter tuning in Python with Scikit-Learn. <br><br>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.<br><br>
<h2>Grid search</h2>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.<br><br>You will be introduced to Random Search, and learn about its advantages over Grid Search, such as efficiency in large parameter spaces.​ <br><br><h2>Informed search</h2>In the final part of the course, you will explore advanced optimization methods, such as Bayesian and Genetic algorithms. <br><br>
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​.

Supervised Learning with scikit-learn

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

Hyperparameter Tuning in Python

Científico de Aprendizaje Automático en Python

Aprendizaje automático supervisado en Python

Data Scientist

GridSearchCV Object

Steps in a Grid Search

GridSearchCV Object Inputs

GridSearchCV 'estimator'

GridSearchCV 'param_grid'

GridSearchCV 'cv'

GridSearchCV 'scoring'

GridSearchCV 'refit'

GridSearchCV 'n_jobs'

GridSearchCV 'return_train_score'

Building a GridSearchCV object

Building a GridSearchCv Object

Using a GridSearchCV Object

Let's practice!

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Grid search - Grid Search with Scikit Learn

Grid Search with Scikit Learn

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