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


## 1) Import libraries
This model is built using the `KNeighborsClassifier` from `Scikit-learn`. You’ll also be relying on `StandardScaler` to standardize the data, as you did earlier with principal component analysis.





# 1) Import libraries
This model is built using the `KNeighborsClassifier` from `Scikit-learn`. You’ll also be relying on `StandardScaler` to standardize the data, as you did earlier with principal component analysis.




This lesson will familiarize you with the implementation steps (1-5) of the k-nearest neighbor method.

k-NN Implementation Steps: 1 to 5

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

k-NN Implementation Steps: 1 to 5

1) Import libraries