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

# 8) Predict 
Let’s now run the model to find the value of an individual property by creating a new variable (`new_house`) using the following input features:

#8. Predict 
new_house = [
	2, #Rooms
	2.5, #Distance
	1, #Bathroom
	1, #Car
]

new_house_predict = model.predict([new_house])
print(new_house_predict)

The predicted value of this house is AUD $981,746.347. The actual value of this house, according to the dataset, is AUD $1,480,000.

# 9) Evaluate
Using mean absolute error from Scikit-learn, you can next compare the difference between the model’s expected price predictions for the test set and the actual price.


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

LR Implementation Steps: 8 and 9

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

LR Implementation Steps: 8 and 9

8) Predict

9) Evaluate