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

Which tree-based technique can be easily visualized?

Which tree-based technique can be easily visualized?

Which tree-based technique is prone to overfitting?

Which tree-based technique is prone to overfitting?

Gradient boosting is an example of a sequential processing algorithm. True or False?

Gradient boosting is an example of a sequential processing algorithm. True or False?

Which is not an accurate description of random forests?

Which is not an accurate description of random forests?

What is generally a good number of initial estimators (trees) for a gradient boosting classifier? 

What is generally a good number of initial estimators (trees) for a gradient boosting classifier?

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