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

We need to build a prediction model to predict the gender of five different species of penguins based on their weight, height, and color. How many output variables will our model have?

We need to build a prediction model to predict the gender of five different species of penguins based on their weight, height, and color. How many output variables will our model have?

Logistic regression can be used interchangeably with linear regression to predict the same output variable. True or false?

Logistic regression can be used interchangeably with linear regression to predict the same output variable. True or false?

How many false positives did the model produce based on the results of the confusion matrix?
[[1655  171]
 [211   1625]]


How many false positives did the model produce based on the results of the confusion matrix?
[[1655 171]
[211 1625]]

Which function can we use to apply our trained model to new data to produce a prediction?

Which function can we use to apply our trained model to new data to produce a prediction?

Predicting a high number of discrete categories 


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