
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

**Clustering** is an unsupervised Machine Learning model that groups similar items in some groups based on some kind of metric. Clustering is a common way to explore your data when your data is unlabeled. **Clustering** itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their understanding of what constitutes a cluster and how to efficiently find them.

**Clustering** is a group of algorithms that focuses on grouping similar items. Among them, the most famous algorithm is `k-means`. In this lesson, we will focus on this algorithm, and do some extended discussion.

# What is `k-means`

**Clustering** is a big topic. `k-means` is one of those simple cases trying to separate data points to `k` groups and minimize a metric known as the inertia or within-cluster sum-of-squares. One of the features of this algorithm is that you need to specify the number of clusters, which is `k`.

This algorithm groups a set of `N` samples into `k` disjoint clusters, `C`. Each group is described by the mean $\mu_{j}$ of the data points in the cluster. The $\mu_{j}$ is referred to as the cluster centroids.

The goal of this algorithm is to minimize the objective function (inertia or within-cluster sum-of-squares) like below:

$$\sum_{i=0}^{n} \min _{\mu_{j} \in C}\left(\left\|x_{i}-\mu_{j}\right\|^{2}\right)$$

`K-means` is equivalent to the expectation-maximization algorithm with a small, all-equal, diagonal covariance matrix. Here are the standard two steps for resolving cluster. You may see the E-step and M-step in other places.

Given an initial set of k means $m_{1}^{(1)}...m_{k}^{(1)}$. $S_{j}$ stands for `k` groups, each data point is assigned to one of them, and there is only one.

- Assign each observation to the cluster with the nearest mean, in most cases, it's least squared Euclidean distance. Where each $x_{p}$ (data point) is assigned to one $S^{t}$.
$$S_{i}^{(t)}=\left\{x_{p}:\left\|x_{p}-m_{i}^{(t)}\right\|^{2} \leq\left\|x_{p}-m_{j}^{(t)}\right\|^{2} \forall j, 1 \leq j \leq k\right\}$$

- Recalculate means (cluster centroids) for data points assigned to each cluster.
$$m_{i}^{(t+1)}=\frac{1}{\left|S_{i}^{(t)}\right|} \sum_{x_{j} \in S_{i}^{(t)}} x_{j}$$

The algorithm has converged when the assignments no longer change. 

That's the end of the theory, now let's code!

In this lesson, we introduce a model that is used to group items based on some metrics.

K-means Clustering

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

K-means Clustering

What is `k-means`

K-means Clustering

What is k-means

What is `k-means`