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ScikitLearn Labs: Random Forest Multioutput, Hyperparameter Optimization, Hashing Features, RBMs & ML Pipelines

Welcome to the Scikit-learn Skill Tree, your definitive guide to mastering Python's most versatile machine learning library. This path is meticulously crafted for aspiring data scientists and ML enthusiasts, offering a structured, hands-on journey through core algorithms, model selection, and evaluation. Forget passive learning; our non-video courses and practical exercises in a dedicated data science playground ensure you gain real-world experience. Let's explore some pivotal labs that will accelerate your expertise and transform your understanding of machine learning.

Plot Random Forest Regression Multioutput

Plot Random Forest Regression Multioutput

Difficulty: Beginner | Time: 35 minutes

This lab demonstrates the use of the multi-output meta estimator to perform multi-output regression. A random forest regressor is used, which supports multi-output regression natively, so the results can be compared. The purpose of this lab is to show how to use the MultiOutputRegressor in scikit-learn to perform multi-output regression, and compare the results to a standard random forest regressor.

Practice on LabEx → | Tutorial →

Hyperparameter Optimization: Randomized Search vs Grid Search

Hyperparameter Optimization: Randomized Search vs Grid Search

Difficulty: Beginner | Time: 20 minutes

In machine learning, hyperparameters are parameters that are not learned from data, but rather set prior to training. Selecting appropriate hyperparameters is crucial to achieving high accuracy in machine learning models. Two common methods for hyperparameter optimization are randomized search and grid search. In this lab, we will compare these two methods for optimizing hyperparameters of a linear Support Vector Machine (SVM) with Stochastic Gradient Descent (SGD) training.

Practice on LabEx → | Tutorial →

Hashing Feature Transformation

Hashing Feature Transformation

Difficulty: Beginner | Time: 40 minutes

This lab demonstrates how to use hashing feature transformation using totally random trees to map data to a high-dimensional, sparse representation that may be useful for classification. The mapping is entirely unsupervised and very efficient.

Practice on LabEx → | Tutorial →

Digit Classification with RBM Features

Digit Classification with RBM Features

Difficulty: Beginner | Time: 25 minutes

This lab focuses on the use of Bernoulli Restricted Boltzmann Machine (RBM) for classification of handwritten digits. The RBM feature extractor is combined with a logistic regression classifier to predict the digits. The dataset used is a greyscale image data where pixel values can be interpreted as degrees of blackness on a white background.

Practice on LabEx → | Tutorial →

Constructing Scikit-Learn Pipelines

Constructing Scikit-Learn Pipelines

Difficulty: Beginner | Time: 25 minutes

This lab is a step-by-step guide on how to construct and display pipelines in Scikit-Learn.

Practice on LabEx → | Tutorial →

Scikit-learn is more than just a library; it's a gateway to practical machine learning. These hands-on labs are meticulously designed to demystify complex concepts and equip you with the skills to build robust, efficient ML solutions. Dive in, experiment, and transform your theoretical understanding into tangible expertise. The journey to becoming a proficient data scientist starts here, with every line of code you write and every model you refine.

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Build Apps with Google AI Studio 🧱

This track will guide you through Google AI Studio's new "Build apps with Gemini" feature, where you can turn a simple text prompt into a fully functional, deployed web application in minutes.

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Google AI Education track image

Work through these 3 parts to earn the exclusive Google AI Studio Builder badge!

This track will guide you through Google AI Studio's new "Build apps with Gemini" feature, where you can turn a simple text prompt into a fully functional, deployed web application in minutes.

Read more →

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