Welcome to the Python-Data-Science repository! This collection offers a variety of hands-on labs and tutorials for mastering data preparation and machine learning techniques using Python. Dive into the world of data science with practical exercises and real-world applications!
Explore the essentials of handling loan datasets.
Learn strategies to impute missing data values effectively.
Create insightful boxplots to understand data distribution.
Identify and handle outliers in your datasets.
Master the art of splitting datasets for training and testing.
Learn the fundamentals of decision trees and ensemble methods.
Implement and understand linear regression models.
Explore logistic regression techniques for classification tasks.
Dive into neural networks and fully connected layers.
Discover the principles behind K-Means clustering algorithms.
Analyze market basket data using association rule mining.
Optimize models with grid search techniques.
Apply your knowledge with an exercise on logistic regression.
Advanced clustering techniques with K-Means.
Explore DBSCAN clustering methods.
Deep dive into market basket analysis with updated techniques.
Build advanced recommender systems using collaborative filtering and SVD.
Implement deep learning techniques for recommendation systems.
Enhance your model tuning skills with grid and random search methods.
Implement CNNs for image classification on the CIFAR-10 dataset.
Predict NYSE stock prices using Long Short-Term Memory networks.
A concise version of stock price prediction with LSTM.
Teerapong Panboonyuen (also known as Kao Panboonyuen)
Ph.D. in Computer Engineering
Chulalongkorn University
Contact: panboonyuen.kao@gmail.com
If you use or refer to this work, please cite it as follows:
@phdthesis{panboonyuen2019semantic,
title = {Semantic segmentation on remotely sensed images using deep convolutional encoder-decoder neural network},
author = {Teerapong Panboonyuen},
year = {2019},
school = {Chulalongkorn University},
type = {Ph.D. thesis},
doi = {10.58837/CHULA.THE.2019.158},
address = {Faculty of Engineering},
note = {Doctor of Philosophy}
}
This project is licensed under the MIT License. See the LICENSE file for details.