What this is
I have 2 machine learning reports in my Github, so I put both of them here. These were my assignments for my Machine Learning class in 2020.
1. YouTube Video Classifier
This project's goal is to predict what category a YouTube video belongs to based on its description. I used 5 different classifier models (K-Nearest Neighbor, Logistic Regression, Decision Tree, Random Forest and Support Vector Machine) and compared them together to see which one has the best accuracy.
2. Waste Classifier
In this project, I aimed to classify different type of waste (organic/recyclable) from the dataset. Unlike the previous project, this project uses images as input, so my approach is a bit different from the previous one. I still start with a simpler approach with K-Nearest Neighbor (KNN), simply flattening the images into arrays to use as inputs, but I also used Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) and compared the results.
Compared to the first project, I also have trained the models with different variables (such as different number of neighbors for KNN), and show how the results vary.
Why it's made
I have been interested in Machine Learning for a long while before taking the ML class, but I never was really able to learnt it. My team leaders from both of my previous internships (Fablab Hanoi and Vinple) have asked me to learnt Machine Learning before, but I never got far in either time. Hence, when I saw that there was a free elective course about Machine Learning, I registered for it as fast as I could.
Even though Machine Learning was not related to my work interest and I never intended to go deeper with it, I still had a lot of fun learning about the topic and making the projects.