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

## Overview of advertising data

This dataset contains fabricated information about the features of users responding to online advertisements, including their gender, age, location, daily time spent online, and whether they clicked on the target advertisement.

> **Note:** It has ten features with no missing values. It’s available on [Kaggle](https://www.kaggle.com/fayomi/advertising/data).


## Overview of Melbourne housing market data

This dataset contains data on house, unit, and townhouse prices in Melbourne, Australia. This dataset is composed of data scraped from publicly available real estate listings posted weekly on the domain. The full dataset contains twenty-one variables, including `address`, `suburb`, `land size`, `number of rooms`, `price`, `longitude`, `latitude`, `postcode`, etc.

> **Note:** This dataset has twenty-one features with some missing values. It’s available on [Kaggle](https://www.kaggle.com/anthonypino/melbourne-housing-market/#Melbourne_housing_FULL.csv).

## Overview of Berlin Airbnb data

Airbnb has exploded in popularity following its humble beginnings in 2008. Today, Berlin is one of the biggest markets for alternative accommodation in Europe, with over 22,552 Airbnb listings recorded in November 2018. The dataset contains detailed data including location, price, and reviews.

> **Note:** It has sixteen features with some missing values. It’s available on [Kaggle](https://www.kaggle.com/brittabettendorf/berlin-airbnb-data/version/2).

## Overview of Kickstarter data

Kickstarter is the world’s largest crowd-funding platform for creative projects. This dataset was created using data extracted from the Kickstarter website.

> **Note:** It has thirty-five features with some missing values. It’s available on [Kaggle](https://www.kaggle.com/tayoaki/kickstarter-dataset).
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# Overview of advertising data

This dataset contains fabricated information about the features of users responding to online advertisements, including their gender, age, location, daily time spent online, and whether they clicked on the target advertisement.

> **Note:** It has ten features with no missing values. It’s available on [Kaggle](https://www.kaggle.com/fayomi/advertising/data).


# Overview of Melbourne housing market data

This dataset contains data on house, unit, and townhouse prices in Melbourne, Australia. This dataset is composed of data scraped from publicly available real estate listings posted weekly on the domain. The full dataset contains twenty-one variables, including `address`, `suburb`, `land size`, `number of rooms`, `price`, `longitude`, `latitude`, `postcode`, etc.

> **Note:** This dataset has twenty-one features with some missing values. It’s available on [Kaggle](https://www.kaggle.com/anthonypino/melbourne-housing-market/#Melbourne_housing_FULL.csv).

# Overview of Berlin Airbnb data

Airbnb has exploded in popularity following its humble beginnings in 2008. Today, Berlin is one of the biggest markets for alternative accommodation in Europe, with over 22,552 Airbnb listings recorded in November 2018. The dataset contains detailed data including location, price, and reviews.

> **Note:** It has sixteen features with some missing values. It’s available on [Kaggle](https://www.kaggle.com/brittabettendorf/berlin-airbnb-data/version/2).

# Overview of Kickstarter data

Kickstarter is the world’s largest crowd-funding platform for creative projects. This dataset was created using data extracted from the Kickstarter website.

> **Note:** It has thirty-five features with some missing values. It’s available on [Kaggle](https://www.kaggle.com/tayoaki/kickstarter-dataset).
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We will go over an introduction to the datasets used in the entire course.


Introduction to Datasets

We will go over an introduction to the datasets used in the entire course.

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

Introduction to Datasets

Overview of advertising data