Learn what machine learning is and get a high-level view of it with Python.

Machine Learning with Python Libraries

Machine learning is a type of artificial intelligence used with multiple Python libraries. In this module, we’ll learn to code basic machine learning models, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. This module will teach you how to import datasets into Python using pandas. We’ll also learn about tree-based learning algorithms to predict numeric and categorical outputs. If you need to revise the concepts, we have summarized key concepts of machine learning, and the overviews of specific algorithms are dispersed throughout the module.

A Practical Guide to Machine Learning with Python

## Introduction to decision trees
#key# DT: Decision Trees #key# creates a decision structure to interpret patterns by splitting data into groups using variables that best split the data into homogenous or numerically relevant groups based on entropy (a measure of variance in the data among different classes). The primary appeal of decision trees is that they can be displayed graphically as a tree-like graph, and they’re easy to explain to non-experts.

Unlike an actual tree, the decision tree is displayed upside down with the “leaves” located at the bottom, or foot, of the tree. Each branch represents the outcome of a decision or variable, and each leaf node represents a class label such as “Go to the beach” or “Stay in.” Decision rules are subsequently marked by the path from the tree’s root to a terminal leaf node.

# Introduction to decision trees
#key# DT: Decision Trees #key# creates a decision structure to interpret patterns by splitting data into groups using variables that best split the data into homogenous or numerically relevant groups based on entropy (a measure of variance in the data among different classes). The primary appeal of decision trees is that they can be displayed graphically as a tree-like graph, and they’re easy to explain to non-experts.

Unlike an actual tree, the decision tree is displayed upside down with the “leaves” located at the bottom, or foot, of the tree. Each branch represents the outcome of a decision or variable, and each leaf node represents a class label such as “Go to the beach” or “Stay in.” Decision rules are subsequently marked by the path from the tree’s root to a terminal leaf node.

This lesson will go over decision trees in more detail and provide the steps involved in their implementation.

Before We Begin

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Unsupervised Learning

Deep Learning

Conclusion

Appendix

1- Decision Trees

Introduction to decision trees