
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

In the last [lesson](https://www.educative.io/courses/hands-on-machine-learning-with-scikit-learn/load-built-in-dataset), we showed how to load the built-in dataset. 

In addition to those built-in datasets, `scikit-learn` also provides some functions that could generate data that follows some distributions.

# Generate classification dataset

As we have already mentioned above, **scikit-learn** provides some functions to build artificial datasets. As you can see from the code below, `make_classification` generates a random n-class classification dataset. 

>**Notice**: The default number of the class is `2`, you can change it from the parameter `n_classes`.

In this example below, the data has two class labels with two features. Meanwhile, the function returns two variables, the first one, **X**, is an `n_samples` * `n_features` 2D array, which is the samples just like the `data` field in the `iris` data. The second one, **y**, is the target vector.

```python
X,y = datasets.make_classification(n_features=2,
                                   n_redundant=0, n_informative=1,n_clusters_per_class=1)
print("The data X shape is {}".format(X.shape))
print("The data y shape is {}".format(y.shape))
```

Since this is a two-class classification data with two features, we can draw them in a 2D chart and assign different colors to each class.

You can try to change the parameters in the code widget below and re-run it to see the difference between those outputs.

In this lesson, let's see how to generate data based on a certain distribution.

Generate synthetic dataset

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

Generate synthetic dataset

Generate classification dataset