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

## 1) Import libraries
While decision trees can solve regression or classification problems, we will use the classification version of the algorithm as we will be predicting a discrete variable. Using the `DecisionTreeClassifier` algorithm from `Scikit-learn`, we will attempt to predict the dependent variable `Clicked on Ad` (0 or 1). The performance of the model will be evaluated using a classification report and a confusion matrix.




# 1) Import libraries
While decision trees can solve regression or classification problems, we will use the classification version of the algorithm as we will be predicting a discrete variable. Using the `DecisionTreeClassifier` algorithm from `Scikit-learn`, we will attempt to predict the dependent variable `Clicked on Ad` (0 or 1). The performance of the model will be evaluated using a classification report and a confusion matrix.




We will start to get familiarized with the implementation steps (1-4) of decision trees.

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

DT Implementation Steps: 1 to 4

1) Import libraries