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 code argument can we use to replicate the same random spit of the same data?

Which code argument can we use to replicate the same random spit of the same data?

The following code is an example of:
X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.7)


The following code is an example of:
X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.7)

Which argument should we use to set the training data to 60% of the original dataset as part of split validation?

Which argument should we use to set the training data to 60% of the original dataset as part of split validation?

Which library should we use to perform split validation?

Which library should we use to perform split validation?

The training set is usually smaller than the test set to ensure higher prediction accuracy. True or False?

The training set is usually smaller than the test set to ensure higher prediction accuracy. True or False?

Quiz

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

Quiz