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

Removing discrete variables using the delete method

Reducing the dataset to a fewer number of variables using a descending dimension algorithm  

Which technique is not an example of data scrubbing?

Which technique is not an example of data scrubbing?

Which code example will delete the age variable from a dataframe saved as a variable called df.

Which code example will delete the age variable from a dataframe saved as a variable called df.

Which line of code can we use to inspect missing values in a dataframe (df)?

Which line of code can we use to inspect missing values in a dataframe (df)?

Which code command can we use to perform one-hot encoding using Pandas?

Which code command can we use to perform one-hot encoding using Pandas?

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