These are interm projects/assignments solved by me with ML algorithms using Python.
Project1: Perceptron Learning (Path Finding).
Overview: Traditionally when we are faced with the task of determining whether a given maze has a path or not we resort to various search or path finding algorithms which are able to answer this question with absolute certainty. In this project, however, we will take a different approach to solve this problem by building a classifier that determines (to a certain degree) whether a given maze has a path or not.
Overview: Apprenticeship learning or behavioural cloning is the process by which an agent learns how to act by observing how other agents are acting in a given environment. In this project you are going to use this approach to train an agent that learns how to navigate optimally in a non-deterministic environment by observing the behaviour of expert learners.
Overview: One of the keys that determine how well a machine learning algorithm can learn is the quality of data and the amount of useful information it contains. In this project we will explore various data preprocessing techniques that will be useful in building good machine learning models.
Project4: Image Recognition With NN
Overview: In this project you are going to use neural networks to perform some basic image recognition tasks. The image dataset we have provided you contains images (in jpeg format) of dimensions 120x128, with the training set consisting of 315 images and the test set consisting of 90 images.
Project5: Image Recognition With CNN
Overview: In this project you are going to use convolutional neural networks to perform some basic image recognition tasks. The image dataset we have provided you contains images (in jpeg format) of dimensions 120x128, with the training set consisting of 315 images, a validation set consisting of 45 images and a test set consisting of 45 images.
Project6: Eye State Prediction
Overview: Aimed to detect the eye state of humans extracted from EEG dataset using Machine Learning techniques such as (SVM, NN, Decision Trees, and Random Forests). Implemented Machine Learning algorithms for prediction using Sikit-Learn and Pandas in Python.