Solving Problems with Machine Learning

by Tertiary Infotech Pte. Ltd

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