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

The distance between the boundary line and the nearest data point


Support vector machines can be used for:

Support vector machines are more effective at classifying multiple discrete outcomes than logistic regression. True or False?

Support vector machines are more effective at classifying multiple discrete outcomes than logistic regression. True or False?

What is the name of the classification version of support vector machines in Scikit-learn?

What is the name of the classification version of support vector machines in Scikit-learn?

Ignoring data points that cross over into the margin


No leeway for data points that cross over into the margin


What is a feature of a soft margin?

In general, a small gamma produces:

Quiz

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

Quiz