
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

# What is `t-SNE`

**Manifold learning** is an approach to non-linear dimensionality reduction. Algorithms for this task are based on the idea that the dimensionality of many data sets is only artificially high.

We are not talking about the topic of `manifold learning`. The purpose of talking about **manifold learning** is that we would use this technique to do some visualization on our dataset. In the real world, many datasets are very high dimensional data. Such high dimensional data can't be visualized in a 2D or 3D space. You may think that we can use the dimensionality reduction to process the data, such as `PCA`, to 2-dimension, and plot it. `Manifold Learning` can be thought of as an attempt to generalize linear frameworks like PCA to be sensitive to nonlinear structure in data. There are many methods in **manifold learning**, such as `Isomap`, `MDS`, `LLE`, and `t-SNE`. `t-SNE` is one of the most commonly used methods, and you may have already seen it in many papers.

t-distributed stochastic neighbor embedding (`t-SNE`) is one of a family of stochastic neighbor embedding methods. The algorithm computes the probability that pairs of data points in the high-dimensional space are related and then chooses low-dimensional embeddings that produce a similar distribution. In this lesson, we focus on the `t-SNE`.

In this lesson, we show you how to visualize High-dimensional data by t-SNE.

t-SNE

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

t-SNE

What is `t-SNE`

t-SNE

What is t-SNE

What is `t-SNE`