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

Using Scikit-learn, the default number of variables using the KNeighborsClassifier is:

Using Scikit-learn, the default number of variables using the KNeighborsClassifier is:

Which of the following evaluation techniques can we use to evaluate the accuracy of a k-nearest neighbors model?

Which of the following evaluation techniques can we use to evaluate the accuracy of a k-nearest neighbors model?

Which metric can we use to compare the accuracy of different k-NN models?

Which metric can we use to compare the accuracy of different k-NN models?

Which algorithm is sometimes used to simplify and prepare a dataset for k-NN analysis?

Which algorithm is sometimes used to simplify and prepare a dataset for k-NN analysis?

k-nearest neighbors is both a classification and a regression technique. True or False?

k-nearest neighbors is both a classification and a regression technique. True or False?

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