Programme Outline
Learning Objectives and Structure
By the end of this course, participants should be able to:
- Create scatter plot and statistical plots like box plot, histogram, and bar plot
- 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
Course Outline – Day 1
- Introduction of course
- Introduce students to the course outline and pre-requisite knowledge including Python programming and some other mathematics knowledge such as linear algebra.
- Introduction to Machine Learning, Numpy and Pandas Library
- Review some basic classical machine learning tasks, particularly linear regression and logistic regression. Introducing students to Pandas library and Numpy Library. Introduction to matrix and vector.
- Working with Data
- Introducing Data Frame and Series and various ways of extracting datas from a data frame. Introduce students to reading data from CSV or Excel file. Introducing basic operations with Data Frames. Category of datasets. Matrix properties.
- Visualizating Data
- Introducing students to matplotlib and seaborn package. Creating some statistical plots to get insight of data.
Course Outline – Day 2
- Linear Regression Class in Scikit-Learn
- Introducing Linear Regression and using Scikit-learn library for linear regression computation.
- Metrics
- Computing Mean Square Error and Correlation Coefficient.
- Logistic Regression Class in Scikit-Learn
- Introducing Classification using Scikit-learn library for logistic regression computation.
- Metrics
- Computing Confusion Matrix, precision and recall.
Assessment
- Problem Set
- Quizzes
- Group Project