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

## Preview the data frame

You can now use the Pandas `head()` command to preview the data frame in Jupyter Notebook. The `head()` function must come after the variable name of the data frame, which is df.

```Python
df.head()
```


# Preview the data frame

You can now use the Pandas `head()` command to preview the data frame in Jupyter Notebook. The `head()` function must come after the variable name of the data frame, which is df.

```Python
df.head()
```


An introduction to the most common and important data frame functions.

Data Frame Functions

Introduction to Course

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

Conclusion

Appendix

Data Frame Functions

Preview the data frame