Machine Learning Tutorials - Example Projects - AI

I am sharing my 28 Machine Learning, Deep Learning (Artificial Intelligence - AI) projects with their data, software and outputs on Kaggle for educational purposes as open source. It appeals to people who want to work in this field, have 0 Machine Learning knowledge, have Intermediate Machine Learning knowledge, specialize in this field (Attracts to all levels). The deep learning projects in it are for advanced level, so I recommend you to start your studies from the Machine Learning section. You can check your own outputs along with the outputs in it. I am happy to share 28 educational projects with the whole world through Kaggle. Knowledge is free and better when shared!

Algorithms used in it:

1) Nearest Neighbor
2) Naive Bayes
3) Decision Trees
4) Linear Regression
5) Support Vector Machines (SVM)
6) Neural Networks
7) K-means clustering

Kind regards, Emirhan BULUT

You can use the links below for communication. If you have any questions or comments, feel free to let me know!

LinkedIn: https://www.linkedin.com/in/artificialintelligencebulut/ Email: emirhan@novosteer.com

Emirhan BULUT. (2022). Machine Learning Tutorials - Example Projects - AI [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/4361310

Dataset

This dataset was created by LeviL1

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Dataset

This dataset was created by vamsi kamatham

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Dataset

This dataset was created by Summa One

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Dataset

This dataset was created by Krishna Swapnika

Released under Apache 2.0

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Emotion Prediction with Quantum5 Neural Network AI Machine Learning - By Emirhan BULUT

V1

I have created an artificial intelligence software that can make an emotion prediction based on the text you have written using the Semi Supervised Learning method and the RC algorithm. I used very simple codes and it was a software that focused on solving the problem. I aim to create the 2nd version of the software using RNN (Recurrent Neural Network). I hope I was able to create an example for you to use in your thesis and projects.

V2

I decided to apply a technique that I had developed in the emotion dataset that I had used Semi-Supervised learning in Machine Learning methods before. This technique is produced according to Quantum5 laws. I developed a smart artificial intelligence software that can predict emotion with Quantum5 neuronal networks. I share this software with all humanity as open source on Kaggle. It is my first open source project in NLP system with Quantum technology. Developing the NLP system with Quantum technology is very exciting!

Happy learning!

Emirhan BULUT

Head of AI and AI Inventor

Emirhan BULUT. (2022). Emotion Prediction with Quantum5 Neural Network AI [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DS/2129637

The coding language used:

Python 3.9.8

Libraries Used:

Keras

Tensorflow

NumPy

Pandas

Scikit-learn (SKLEARN)

https://raw.githubusercontent.com/emirhanai/Emotion-Prediction-with-Semi-Supervised-Learning-of-Machine-Learning-Software-with-RC-Algorithm---By/main/Quantum%205.png" alt="Emotion Prediction with Quantum5 Neural Network on AI - Emirhan BULUT">

https://raw.githubusercontent.com/emirhanai/Emotion-Prediction-with-Semi-Supervised-Learning-of-Machine-Learning-Software-with-RC-Algorithm---By/main/Emotion%20Prediction%20with%20Semi%20Supervised%20Learning%20of%20Machine%20Learning%20Software%20with%20RC%20Algorithm%20-%20By%20Emirhan%20BULUT.png" alt="Emotion Prediction with Semi Supervised Learning of Machine Learning Software with RC Algorithm - Emirhan BULUT">

Developer Information:

Name-Surname: Emirhan BULUT

Contact (Email) : emirhan@isap.solutions

LinkedIn : https://www.linkedin.com/in/artificialintelligencebulut/

Kaggle: https://www.kaggle.com/emirhanai

Official Website: https://www.emirhanbulut.com.tr

DESCRIPTION

Information about popular open source projects related to machine learning.

SUMMARY

The goal of this dataset is to better undertand how open source machine learning projects evolve. Data collection date: early May 2018. Source: GitHub user interface and API. Contains original research.

Presentation

Columns

name - name of the project. alignment - either corporate, academia or indie. Corporate projects are being developed by professional engineers, typically have a dedicated development team and trying to solve specific problems. Academical projects usually mention publications, they help to research. Independent projects are often a hobby. company - name of the company if the alignment is corporate. forecast - expected middle-term evolution of the project. 1 means positive, 0 means negative (stagnation) and -1 means factual death. year - when the project was created. Defaults to the GitHub repository creation date but can be earlier - this is a subject of manual adjustments. code of conduct - whether the project has a code of conduct. contributing - whether the project has a contributions guide. stars - number of stargazers on GitHub. issues - number of issues on GitHub, either open or closed. contributors - number of contributors as reported by GitHub. core - estimation of the core team aka "bus factor". team - number of people which commit to a project regularly. commits - number of commits in the project. team / all - ratio of the number of commits by the dedicated development team to the overall number of contributions. Indicates roughly which part of the project is own by the internal developers. link - URL of the project. language - API language. multi means several languages. implementation - the language which was mainly used for implementing the project. license - license of the project.

Dataset

This dataset was created by SUJIT SHIBAPRASAD MAITY

Released under MIT

Contents

Dataset

This dataset was created by Yuqi Zhao

Contents

To be written later in details.

Discussion group: https://t.me/sberlogasci Draft slides: link. Webinars: https://t.me/sberlogabig/370 , https://t.me/sberlogabig/368 ....

Briefly

The project is devoted to interaction between machine learning methods and finite group/graph theory (tasks like: estimate diameter, find short path, etc). Partly motivated by the Kaggle challenge "Santa23" : https://www.kaggle.com/competitions/santa-2023 but much more broad.

Main goals:

1. Develop machine learning methods to find short paths on Cayley graphs of arbitrary finite groups. (It is direct generalization of the Kaggle Santa23 challenge).
2. Machine learning methods to estimate diameter of the Caley graphs for finite groups. And also growth.
3. Develop mathematical based benchmarks for graph embedding packages - taking into account: distortion of graph metric under embedding, symmetry preservation, distance distribution preservations, comparing with example gallery, ...

Other goals include:

Study random walks on Cayley graphs, machine learning methods for Lovász conjecture on existence of Hamiltonian paths on Cayley graphs , ........

Basic approaches:

To be written later

In this project, we delve into the world of machine learning and explore the hottest topics and key insights in the field. We analyze a dataset of research papers on machine learning, using natural language processing techniques and unsupervised learning algorithms such as LDA to extract meaningful topics and insights. We also visualize the results using tools like word clouds and bar charts, and provide commentary on the latest trends and emerging areas of research in machine learning. Whether you're a seasoned data scientist or a beginner in the field, this project will give you a fascinating glimpse into the cutting-edge developments and breakthroughs in machine learning.

Dataset

This dataset was created by Sainath Reddy S

Contents

Dataset

This dataset was created by Avinash Shan Monteiro

Released under Data files © Original Authors

Contents

I created this dataset as part of a data analysis project and concluded that it might be relevant for others who are interested in examining in analyzing content on YouTube. This dataset is a collection of over 6000 videos having the columns:

• Channel: video's channel
• Title: video title
• PublishedDate: date the video was uploaded
• Likes: likes count for the video
• Views: views count for the video

• Comments: comments count for the video

  Through the YouTube API and using Python, I collect data about some of these popular channels' videos that provide educational content about Machine Learning and Data Science in order to extract insights about which topics had been popular within the last couple of years. Featured in the dataset are the following creators:

• Krish Naik

• Nicholas Renotte

• Sentdex

• DeepLearningAI

• Artificial Intelligence — All in One

• Siraj Raval

• Jeremy Howard

• Applied AI Course

• Daniel Bourke

• Jeff Heaton

• DeepLearning.TV

• Arxiv Insights

These channels are features in multiple top AI channels to subscribe to lists and have seen a big growth in the last couple of years on YouTube. They all have a creation date since or before 2018.

Dataset

This dataset was created by Emre Dumbo

Contents

Credit to the original author: The dataset was originally published here

Weather prediction dataset

A dataset for teaching machine learning and deep learning

Hands-on teaching of modern machine learning and deep learning techniques heavily relies on the use of well-suited datasets. The "weather prediction dataset" is a novel tabular dataset that was specifically created for teaching machine learning and deep learning to an academic audience. The dataset contains intuitively accessible weather observations from 18 locations in Europe. It was designed to be suitable for a large variety of different training goals, many of which are not easily giving way to unrealistically high prediction accuracy. Teachers or instructors thus can chose the difficulty of the training goals and thereby match it with the respective learner audience or lesson objective. The compact size and complexity of the dataset make it possible to quickly train common machine learning and deep learning models on a standard laptop so that they can be used in live hands-on sessions.

The dataset can be found in the `\dataset` folder and be downloaded from zenodo: https://doi.org/10.5281/zenodo.4980359

References

If you make use of this dataset, in particular if this is in form of an academic contribution, then please cite the following two references:

• Klein Tank, A.M.G. and Coauthors, 2002. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. of Climatol., 22, 1441-1453. Data and metadata available at http://www.ecad.eu
• Florian Huber, Dafne van Kuppevelt, Peter Steinbach, Colin Sauze, Yang Liu, Berend Weel, "Will the sun shine? – An accessible dataset for teaching machine learning and deep learning", DOI TO BE ADDED!

Map of the locations of the 18 weather stations from which data was collected

Data scraped from Mangakalot I had originally decided to create this dataset for use in a recommendation system for manga titles. Other datasets that I had found were either missing information that I wanted to use to build this system or contained too small a sample size to build what I deemed a useful product. This is also my first attempt at web scraping (I'm also fairly new to python and data science) so I suppose I wanted to do a simple project at first to learn the basics. I hope it proves useful to someone.

Dataset

This dataset was created by aarvee

Released under CC BY-SA 3.0

Contents

Dataset

This dataset was created by MrSimple

Contents

Indian Sign Language images along with corresponding XML files that can be used for Artificial Intelligence, Machine Learning and deep learning projects such as hand gesture to speech converter.