These are the exercise files used for Solving Problems with Machine Learning course.
The course outline can be found in
https://www.tertiarycourses.com.sg/solving-problems-with-machine-learning.html https://www.tertiarycourses.com.my/solving-problems-with-machine-learning-malaysia.html
Module 1 - Introduction to Machine Learning
- What is Machine Learning
- Machine Learning in Real life
- Types of Machine Learning
- Key ML Models
- Installing Weka
- Load Dataset to Weka
- Build Your First Classifier
Module 2 - Classification
- What is Classification?
- K-Nearest Neighbours (KNN)
- Support Vector Machine (SVM)
- Naive Bayes
- Decision Tree (DT)
Module 3 - Regression
- What is Regression?
- Linear Regression
- Support Vector Regression
- K-Nearest Neighbour Regression
Module 4 - Ensemble Methods
- What is Ensemble Methods?
- Bagging
- Random Forest
- Stacking
Module 5 - Clustering
- What is Clustering?
- K-Means Clustering
- Hierarchical Clustering
Module 6 - Neural Network
- What is Neural Network?
- Multilayer Perceptron Classifier
Module 7 - Problem Solving through Machine Learning
- Problem Definition
- Data Conceptualization
- Data Gathering
- Feature Engineering
- Algorithm Spot Check
- Fine Tuning Model
- Pitfalls