
Gain insights into Scikit-Learn's datasets, feature engineering, linear/logistic regression, and unsupervised learning. Delve into k-means clustering and neural networks to enhance your machine learning expertise.

mk1.tar.gz

data_preprocessing

dataset

decision_tree_classification

feature_extraction

feature_selection

gradient_boost

kmeans

knn_classification

sk_lr_classification

sk_lr_regression

sk_metrics

sk_missing_value

sk_naive_bayes_classification

sk_nn

sk_parameter_search

sk_pca

sk_pipeline

sk_rf

sk_tsne

sk_svm_classification

jupyter_job

python_updated

sk_naive_bayes_classification-copy

Scikit-Learn is a powerful library that provides a handful of supervised and unsupervised learning algorithms. If you’re serious about having a career in machine learning, then scikit-learn is a must know.

In this course, you will start by learning the various built-in datasets that scikit-learn offers, such as iris and mnist. You will then learn about feature engineering and more specifically, feature selection, feature extraction, and dimension reduction.

In the latter half of the course, you will dive into linear and logistic regression where you’ll work through a few challenges to test your understanding. Lastly, you will focus on unsupervised learning and deep learning where you’ll get into k-means clustering and neural networks.

By the end of this course, you will have a great new skill to add to your resume, and you’ll be ready to start working on your own projects that will utilize scikit-learn.

Hands-on Machine Learning with Scikit-Learn

# Challenge - GBDT parameter finetune

In this challenge, you need to do the following tasks:

- Load the breast cancer dataset by `load_breast_cancer`.
- Split the dataset into two parts, the test set accounts for 20%. Use `42` as the random seed.
- Create a GBDT object. This is a classification task.
- Use the Grid Search to finetune the parameters of the `GBDT`. To narrow the search space, in this challenge, you only need to finetune the `learning_rate`, `n_estimators`, and `min_samples_leaf`.
- Finally, return the best `F1-score`.

In this challenge, you need to use GBDT to perform classification. Meanwhile, you should use Grid Search to fine-tune the parameters.

Challenge - GBDT Parameter Fine-Tune

Preliminaries

Working with Datasets

Feature Engineering

General Concepts

Linear Regression

Logistic Regression

Support Vector Machine

Tree Model and Ensemble Method

Unsupervised Learning

Deep Learning

Others

What's Next

Challenge - GBDT Parameter Fine-Tune

Challenge - GBDT parameter finetune