Jump-Start Scikit-Learn

https://machinelearningmastery.com/jump-start-scikit-learn/

Do you want to get started or make the most of the scikit-­learn library without getting bogged down with the mathematics and theory of the algorithms?

In this guide you will discover 35 standalone machine learning recipes that you can copy-paste into your project.

This simple lightweight reference to the scikit-­learn library will give you the confidence you need to get started and make progress with machine learning in Python.

The recipes in this guide cover data handling, supervised learning algorithm, regularization, ensemble methods and advanced topics like feature selection, cross validation and parameter tuning.

If you want to get up and running with scikit-­learn fast, this guide is for you!

Below is an overview of the Jump-Start Scikit-Learn guide that highlights the recipes provided in each section.

Introduction
Why is scikit-learn
Where did scikit-learn come from
What is scikit-learn
What are the features of the library
Who is using scikit-learn
Further reading
Handling data
Example datasets
Load data from CSV
Data normalization
Data standardization
Imputing missing values
Supervised learning
Linear Regression
Logistic Regression
Linear Discriminant Analysis
Quadratic Discriminant Analysis
Perceptron
Naive Bayes
k-Nearest Neighbors
Classification and Regression Trees
Support Vector Machines
Regularization
Ridge Regression
Least Absolute Shrinkage and Selection Operator (LASSO)
Last Angle Regression (LARS)
LASSO LARS
ElasticNet
Ensemble methods
Random Forest
Extra Trees
Adaptive Boosting
Gradient Boosting
Advanced
Recursive Feature Elimination
Feature Importance
Cross Validation
Grid Search Parameter Tuning
Random Search Parameter Tuning
Summary