Explore practical coding of basic machine learning models using Python. Gain insights into algorithms like linear regression, logistic regression, SVM, KNN, and decision trees.

Machine Learning with Python A Practical Beginners’ Guide by Oliver Theobald-01.png

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Mlxtend

This course teaches you how to code basic machine learning models. The content is designed for beginners with general knowledge of machine learning, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. If you need a refresher, we have summarized key concepts from machine learning, and there are overviews of specific algorithms dispersed throughout the course.

A Practical Guide to Machine Learning with Python

## A quick review of exploratory data analysis (EDA) 

As a key part of data inspection, #key# EDA: Exploratory Data Analysis #key#  involves summarizing the salient characteristics of your dataset in preparation for further processing and analysis. 

EDA includes understanding the shape and distribution of the data, scanning for missing values, learning which features are most relevant based on correlation, and familiarizing yourself with the overall contents of the dataset. Gathering this intel helps inform algorithm selection and highlight parts of the data that require cleaning to prepare for further processing.

You can leverage a range of simple techniques to summarize data using Pandas, with additional options to visualize the data using Seaborn and Matplotlib.

Let’s begin by importing Pandas, Seaborn, and Matplotlib inline using the following code in Jupyter Notebook.

```python
import pandas as pd
import seaborn as sns
%matplotlib inline
```
> **Note:** Using the inline feature of Matplotlib, you can display plots directly below the applicable code cell within Jupyter Notebook or other frontends.


## How you can import the dataset

You can import datasets from various sources, including internal and external files as well as random self-generated datasets called **blobs**.

The following sample dataset is an external dataset downloaded from Kaggle called the Berlin Airbnb dataset. This data was scraped from [Airbnb](https://www.airbnb.com/) and contains detailed accommodation listings available in Berlin including location, price, and reviews.

# A quick review of exploratory data analysis (EDA) 

As a key part of data inspection, #key# EDA: Exploratory Data Analysis #key#  involves summarizing the salient characteristics of your dataset in preparation for further processing and analysis. 

EDA includes understanding the shape and distribution of the data, scanning for missing values, learning which features are most relevant based on correlation, and familiarizing yourself with the overall contents of the dataset. Gathering this intel helps inform algorithm selection and highlight parts of the data that require cleaning to prepare for further processing.

You can leverage a range of simple techniques to summarize data using Pandas, with additional options to visualize the data using Seaborn and Matplotlib.

Let’s begin by importing Pandas, Seaborn, and Matplotlib inline using the following code in Jupyter Notebook.

```python
import pandas as pd
import seaborn as sns
%matplotlib inline
```
> **Note:** Using the inline feature of Matplotlib, you can display plots directly below the applicable code cell within Jupyter Notebook or other frontends.


# How you can import the dataset

You can import datasets from various sources, including internal and external files as well as random self-generated datasets called **blobs**.

The following sample dataset is an external dataset downloaded from Kaggle called the Berlin Airbnb dataset. This data was scraped from [Airbnb](https://www.airbnb.com/) and contains detailed accommodation listings available in Berlin including location, price, and reviews.

This lesson teaches the steps to import a CSV file into Python using Pandas.

Import Dataset

Introduction to Course

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Customer Segmentation with K-Means Clustering

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Cardiovascular Disease Risk Prediction with Random Forest

Conclusion

Appendix

Import Dataset

A quick review of exploratory data analysis (EDA)