You'll be introduced to the concept of dimensionality reduction and will learn when an why this is important. You'll learn the difference between feature selection and feature extraction and will apply both techniques for data exploration.  The chapter ends with a lesson on t-SNE, a powerful feature extraction technique that will allow you to visualize a high-dimensional dataset.

Introduction

Finding the number of dimensions in a dataset

Removing features without variance

Feature selection vs. feature extraction

Visually detecting redundant features

Advantage of feature selection

t-SNE visualization of high-dimensional data

t-SNE intuition

Fitting t-SNE to the ANSUR data

t-SNE visualisation of dimensionality

Exploring High Dimensional Data

In this first out of two chapters on feature selection, you'll learn about the curse of dimensionality and how dimensionality reduction can help you overcome it. You'll be introduced to a number of techniques to detect and remove features that bring little added value to the dataset. Either because they have little variance, too many missing values, or because they are strongly correlated to other features.

The curse of dimensionality

Train - test split

Fitting and testing the model

Accuracy after dimensionality reduction

Features with missing values or little variance

Finding a good variance threshold

Features with low variance

Removing features with many missing values

Pairwise correlation

Correlation intuition

Inspecting the correlation matrix

Visualizing the correlation matrix

Removing highly correlated features

Filtering out highly correlated features

Nuclear energy and pool drownings

Feature Selection I - Selecting for Feature Information

In this second chapter on feature selection, you'll learn how to let models help you find the most important features in a dataset for predicting a particular target feature. In the final lesson of this chapter, you'll combine the advice of multiple, different, models to decide on which features are worth keeping.

Selecting features for model performance

Building a diabetes classifier

Manual Recursive Feature Elimination

Automatic Recursive Feature Elimination

Tree-based feature selection

Building a random forest model

Random forest for feature selection

Recursive Feature Elimination with random forests

Regularized linear regression

Creating a LASSO regressor

Lasso model results

Adjusting the regularization strength

Combining feature selectors

Creating a LassoCV regressor

Ensemble models for extra votes

Combining 3 feature selectors

Feature Selection II - Selecting for Model Accuracy

This chapter is a deep-dive on the most frequently used dimensionality reduction algorithm, Principal Component Analysis (PCA). You'll build intuition on how and why this algorithm is so powerful and will apply it both for data exploration and data pre-processing in a modeling pipeline.  You'll end with a cool image compression use case.

Feature extraction

Manual feature extraction I

Manual feature extraction II

Principal component intuition

Principal component analysis

Calculating Principal Components

PCA on a larger dataset

PCA explained variance

PCA applications

Understanding the components

PCA for feature exploration

PCA in a model pipeline

Principal Component selection

Selecting the proportion of variance to keep

Choosing the number of components

PCA for image compression

Congratulations!

Feature Extraction

ANSUR Female

ANSUR Male

Diabetes

Grocery store sales

Boston Public Schools

Pokemon

High-dimensional datasets can be overwhelming and leave you not knowing where to start. Typically, you’d visually explore a new dataset first, but when you have too many dimensions the classical approaches will seem insufficient. Fortunately, there are visualization techniques designed specifically for high dimensional data and you’ll be introduced to these in this course. After exploring the data, you’ll often find that many features hold little information because they don’t show any variance or because they are duplicates of other features. You’ll learn how to detect these features and drop them from the dataset so that you can focus on the informative ones. In a next step, you might want to build a model on these features, and it may turn out that some don’t have any effect on the thing you’re trying to predict. You’ll learn how to detect and drop these irrelevant features too, in order to reduce dimensionality and thus complexity. Finally, you’ll learn how feature extraction techniques can reduce dimensionality for you through the calculation of uncorrelated principal components.

Supervised Learning with scikit-learn

Learn to reduce dimensionality in Python by detecting these features and dropping them from the dataset so that you can focus on the informative ones.

Dimensionality Reduction in Python

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

Machine Learning Scientist in Python

Likely to Recommend

Filtering out highly correlated features

“Dimensionality Reduction in Python”

Exercise instructions

Hands-on interactive exercise

Dimensionality Reduction in Python

Chapter 1: Exploring High Dimensional Data

Chapter 2: Feature Selection I - Selecting for Feature Information

Chapter 3: Feature Selection II - Selecting for Model Accuracy

Chapter 4: Feature Extraction

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