
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 - Classification of non-linear data

In this lesson, you need to perform a classification task. Unlike the tasks we completed before, this dataset is non-linear. This means you can't only use logistic regression. `SVM` is a good choice. (If you already learned the `GBDR`, it's also a good choice.)

- Load the dataset from the file `nonlinear.csv`.
- Split the data into two parts, the test accounts for 20%. You can use `42` as the random seed
- Build an `SVM` model and fit it.
- Return the `F1-score` metric.

>**Note**:
>You can load `nonlinear.csv` as this file is already on the platform. This dataset has three columns, **col1**, **col2**, and **target**. The **target** is the label.
>
>If you are already familiar with `pandas` library, it's easy for you to load this dataset. If not, you can click the `Hint` to get the code of loading data.

# Challenge - Classification of non-linear data

In this lesson, you need to perform a classification task. Unlike the tasks we completed before, this dataset is non-linear. This means you can't only use logistic regression. `SVM` is a good choice. (If you already learned the `GBDR`, it's also a good choice.)

- Load the dataset from the file `nonlinear.csv`.
- Split the data into two parts, the test accounts for 20%. You can use `42` as the random seed
- Build an `SVM` model and fit it.
- Return the `F1-score` metric.

>**Note**:
>You can load `nonlinear.csv` as this file is already on the platform. This dataset has three columns, **col1**, **col2**, and **target**. The **target** is the label.
>
>If you are already familiar with `pandas` library, it's easy for you to load this dataset. If not, you can click the `Hint` to get the code of loading data.

In this lesson, you need to classify the non-linear dataset.

Challenge - Classification of non-linear data

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 - Classification of non-linear data

Challenge - Classification of non-linear data