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 library is better for managing a mix of data types (numeric and non-numerical data)?

Which library is better for managing a mix of data types (numeric and non-numerical data)?

Which library should we use for common shallow algorithms for use with Python?

Which library should we use for common shallow algorithms for use with Python?

Which Python library can be used for visualizing data relationships?

Which Python library can be used for visualizing data relationships?

Which of the following is a dataframe?

Tensorflow is generally used for deep learning.

Tensorflow is generally used for deep learning.

Machine Learning Libraries

Machine Learning Libraries

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