Introduction to Machine Learning Using Python
About This Course
The course is an introduction to classical Machine Learning technique using Python and Scikit-Learn. It introduces learners to basic machine learning steps from data preparation to evaluation of machine learning models. Learners will learn and build two classical machine learning models namely Linear Regression and Logistic Regression for continuous and categorical data respectively. Learners will learn how to process data using R-squared Pandas library in Python as well as to visualize those data using Seaborn and Matplotlib. At the end, they will learn some metrics to evaluate their machine learning models.
What You'll Learn
* Create a Panda’s DataFrame and selecting data from DataFrame
* Use library to read Comma-separated values (CSV) or EXCEL file
* Split data randomly into training set and testing set
* Give example of linear regression and classification
* Evaluate linear regression model using r and mean-squared-error
* Plot linear regression
* Train logistic regression model
* Calculate confusion matrix, precision, and recall
Entry Requirements
Participants should possess a basic understanding of the Python programming language and should have gone through the Fundamentals in Python (Basic) course.