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

## Quick overview
In data science, it’s common to have a mismatch or compatibility issue between the data and the algorithm. While the variable's contents might be relevant, the algorithm might not read the data in its default form. Text-based categorical values, for example, can’t be parsed and mathematically modeled using general clustering and regression algorithms.

One quick remedy is to re-express categorical variables as a numeric categorizer. This can be performed using a common technique called one-hot encoding which converts categorical variables into binary form, represented as “1” or “0” or “True” or “False.”

# Quick overview
In data science, it’s common to have a mismatch or compatibility issue between the data and the algorithm. While the variable's contents might be relevant, the algorithm might not read the data in its default form. Text-based categorical values, for example, can’t be parsed and mathematically modeled using general clustering and regression algorithms.

One quick remedy is to re-express categorical variables as a numeric categorizer. This can be performed using a common technique called one-hot encoding which converts categorical variables into binary form, represented as “1” or “0” or “True” or “False.”

We will become familiar with the techniques of converting data to OHE (One-Hot Encoding).

Data Scrubbing Operation: One-Hot Encoding

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: One-Hot Encoding

Quick overview