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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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

# Jupyter notebook
**Jupyter notebook** is a popular choice for practitioners and online courses alike. It combines live code, explanatory notes, and visualizations into one convenient workspace and runs from any web browser.

Jupyter notebook can be installed using the **Anaconda Distribution** or Python’s package manager, **pip**. As an experienced Python user, you may wish to install the Jupyter Notebook via pip. There are instructions on the Jupyter Notebook [website](http://jupyter.org/install.html) outlining this option.

# Beginners way of environment setup

## Step 1

The Anaconda Distribution option offers an easy click-and-drag [setup](https://www.anaconda.com/distribution/). This installation option will direct you to the Anaconda website. From there, you can select your preferred installation for Windows, macOS, or Linux. Again, you can find instructions available on the Anaconda website based on your operating system.

## Step 2

After installing Anaconda on your machine, you’ll have access to several data science applications, including Rstudio, Jupyter Notebook, and Graphviz for data visualization through the Anaconda application. Next, you need to select Jupyter Notebook by clicking on “Launch” inside the Jupyter Notebook tab.


Explore steps for installing the Jupyter Notebook.

Development Environment

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

Development Environment

Jupyter notebook