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

## 9) Evaluate
The next step of the model design process is to evaluate the results. The evaluation method will depend on the scope of your model. Specifically, this depends on whether it is a classification or regression model. In classification, the common evaluation methods are the confusion matrix, classification report, and accuracy score.
<div align = "center">

$Accuracy = \frac{Number Predicted Correctly}{Number of Cases}$

</div>





# 9) Evaluate
The next step of the model design process is to evaluate the results. The evaluation method will depend on the scope of your model. Specifically, this depends on whether it is a classification or regression model. In classification, the common evaluation methods are the confusion matrix, classification report, and accuracy score.
<div align = "center">

$Accuracy = \frac{Number Predicted Correctly}{Number of Cases}$

</div>





This lesson will finish going over the implementation steps (9-10) of the model design.

MD Implementation Steps: 9 and 10

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

MD Implementation Steps: 9 and 10

9) Evaluate