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 as Comma-separated values (CSV) or EXCEL file
- Split data randomly into training set and testing set
- Normalize data using min-max normalization
- Give example of linear regression and classification
- Write objective function of linear regression
- Implement Gradient Descent algorithm for optimisation
- Train linear regression model using gradient descent
- Transform data for higher order features
- Evaluate linear regression model using R-squared and mean-squared-error
- Evaluate and choose learning rate
- Plot cost function over iteration time
- Plot linear regression
- Write objective function of logistic regression
- Use logistic regression to calculate probabilities of binary classification
- Train logistic regression model
- Split data into training, validation, and testing set
- Visualize non-linear decision boundary
- Classify multi-class problems using one-vs-all technique
- Calculate confusion matrix, precision, and recall
Course Outline – Day 1
- Modeling Continuous Data, Review of Mathematical Notations
- Introducing the inside Linear regression model. Review of basic matrix operations and notation. Matrix multiplication..
- Linear Regression Cost Function and Gradient Descent
- Computing cost function and gradient descent optimization.
- Classification and Logistic Function
- Introducing the inside of logistic function for classification algorithm.
- Hypothesis and Cost Function Computation for Logistic Regression
- Computing cost function and probability for logistic regression.
Course Outline – Day 2
- One-vs-all Technique for Multi-class Classification
- Applying One-vs-all technique for multi-class classification.
- Review and Recap
- Recap and review previous lessons and what to expect moving forward.
Course Outline – Day 3
- Project Consultation
- Project Presentation
Assessment
- Problem Set
- Quizzes
- Group Project