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

Which method can we use to view the bottom n rows of a Panda dataframe?

Which method can we use to view the bottom n rows of a Panda dataframe?

Which method can we use to view the top n rows of a Panda dataframe?

Which method can we use to view the top n rows of a Panda dataframe?

Which method can we use to generate a summary of the dataframe’s mean, standard deviation, and IQR values?

Which method can we use to generate a summary of the dataframe’s mean, standard deviation, and IQR values?

Which technique is not an example of exploratory data visualization?

Which technique is not an example of exploratory data visualization?

Which code command can we use to preview a Pandas dataframe (df) inside a notebook?

Which code command can we use to preview a Pandas dataframe (df) inside a notebook?

Quiz

Introduction to Course

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Customer Segmentation with K-Means Clustering

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Cardiovascular Disease Risk Prediction with Random Forest

Conclusion

Appendix

Quiz