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

## Quick overview 
Preparing data for further processing generally starts by removing variables that aren’t compatible with the chosen algorithm or variables that are deemed less relevant to your target output. Determining which variables to remove from the dataset is generally done using exploratory data analysis and domain knowledge.

Speaking of exploratory data analysis, it is often helpful to start by checking the data type of your variables (i.e., string, Boolean, integer, etc.) and the correlation between variables. Domain knowledge, meanwhile, is useful for spotting duplicate variables, such as country and country code, and eliminating less relevant variables like latitude and longitude.

> **Note:** In Python, variables can be removed from the dataframe using the `del` function alongside the variable name of the dataframe and the title of the column you wish to remove. The column title should be nested inside quotation marks and square brackets, as shown here:

```python
del df['latitude']
del df['longitude']
```
> **Note:** this code example, in addition to other changes made inside your notebook, won’t affect or alter the source file of the dataset. You can even restore variables removed from the development environment by deleting the code's relevant line(s). In fact, it’s common to reverse the removal of features when testing the model using different combinations of variables.

---

# Quick overview 
Preparing data for further processing generally starts by removing variables that aren’t compatible with the chosen algorithm or variables that are deemed less relevant to your target output. Determining which variables to remove from the dataset is generally done using exploratory data analysis and domain knowledge.

Speaking of exploratory data analysis, it is often helpful to start by checking the data type of your variables (i.e., string, Boolean, integer, etc.) and the correlation between variables. Domain knowledge, meanwhile, is useful for spotting duplicate variables, such as country and country code, and eliminating less relevant variables like latitude and longitude.

> **Note:** In Python, variables can be removed from the dataframe using the `del` function alongside the variable name of the dataframe and the title of the column you wish to remove. The column title should be nested inside quotation marks and square brackets, as shown here:

```python
del df['latitude']
del df['longitude']
```
> **Note:** this code example, in addition to other changes made inside your notebook, won’t affect or alter the source file of the dataset. You can even restore variables removed from the development environment by deleting the code's relevant line(s). In fact, it’s common to reverse the removal of features when testing the model using different combinations of variables.

---

This lesson will introduce you to ways of removing redundant or unhelpful data variables.

Data Scrubbing Operation: Removing Variables

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

Data Scrubbing Operation: Removing Variables

Quick overview