PG545 - Advanced Python Programming: Level 3

Course Description

Continue to expand your Python coding knowledge and learn to perform statistical data analysis and visualization to better understand relationships in your data. Work with the scikit-learn package to implement machine learning and predicative data analysis. Discover new patterns in your data using preprocessing, classification, regression, and clustering. Learn how to create learning data sets and testing data sets for machine learning, how to determine if over-learning has occurred and what to do about it, and how to keep your AI up to date with the changing conditions of the real world.

Throughout the class you will learn the following topics: 

• Import data, use a machine learning algorithm (estimator) to fit the data, and the estimator to predict the results of future data. 

• Use a decision tree to classify your data and make predictions. 

• Learn about overfitting and what to do to prevent overfitting. 

• Learn how to make the most of your limited data by using random selection and folding. 

• Use the support vector classification (SVC). 

• Use regression for linear and polynomial functions, and learn when to use regression. 

• Do unsupervised learning with K-means. 

• Learn multiple methods to determine how well the estimator predicted the results. 

• Do pre-processing (scalers) and principal component analysis. 

• Create a pipeline to get the pre-processing and the estimator in order. 

• Use the pipeline to experiment with the best hyper-parameters. 

Applies Towards the Following Certificates

• Python Data Analysis Certificate : Core Courses