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

Context

This dataset is comprised of several others and is collected specially for the Vowpal Wabbit tutorial, Kernel. The tutorial covers (both theoretically and in practice) two reasons of Vowpal Wabbit's exceptional training speed, namely, online learning and hashing trick. We'll try it out with the Spooky Author Identification dataset as well as with news, letters, movie reviews datasets and gigabytes of StackOverflow questions.

Content

The included datasets are:

• SOCR weights and heights, link (CC-BY licensed)
• UCI bikes sharing demand, link (no license)
• the 20 newsgroups text dataset, link (BSD licensed)
• pickled VW-versions of IMDB movie reviews (no license)
• a sample from the 10mln StackOverflow dataset (preprocessed and shared under the cc-by-sa 3.0 license)

Dataset

This dataset was created by Rahel Weldegebriel

Released under MIT

Contents

Dataset

This dataset was created by Lucas Henrique Mateo

Released under Apache 2.0

Contents

Dataset

This dataset was created by Rahul Nakka

Contents

Dataset for my (German) Python Data Science Tutorial on YouTube.

Playlist: https://www.youtube.com/playlist?list=PLW4WJMmOF9juA1Ebs1vNwTBuF7ck6YCT7

My version of: 'Bike Share Daily Data' (https://www.kaggle.com/contactprad/bike-share-daily-data)

Data used in this competition: https://www.kaggle.com/c/bike-sharing-demand

Use of this dataset in publications must be cited to the following publication:

[1] Fanaee-T, Hadi, and Gama, Joao, "Event labeling combining ensemble detectors and background knowledge", Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg, doi:10.1007/s13748-013-0040-3.

@article{ year={2013}, issn={2192-6352}, journal={Progress in Artificial Intelligence}, doi={10.1007/s13748-013-0040-3}, title={Event labeling combining ensemble detectors and background knowledge}, url={http://dx.doi.org/10.1007/s13748-013-0040-3}, publisher={Springer Berlin Heidelberg}, keywords={Event labeling; Event detection; Ensemble learning; Background knowledge}, author={Fanaee-T, Hadi and Gama, Joao}, pages={1-15} }

Dataset

This dataset was created by Lisette

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by TinaSoni

Released under Data files © Original Authors

Contents

• The dataset contains input data (e.g., conference articles) that can be used when playing around with fine-tuning of embeddings for RAG applications with LlamaIndex (other use cases are possible as well of course). The dataset furthermore contains synthetic queries created using the input data and (fine-tuned) embedding models trained using the synthetic queries.
• The form 10-K files in this dataset are used in tutorials from LlamaIndex (e.g., Fine-tuning an Adapter, Embedding fine-tuning)
• Data is used in this public notebook: https://www.kaggle.com/code/hiarsl/fine-tuning-embeddings-with-llamaindex

Dataset

This dataset was created by Naimul Hasan Shadesh

Released under Apache 2.0

Contents

This is a sample Dataset used for the Tutorial Notebook Titled:

Mistakes To Avoid In Data Science⚠️| Python🐍

Follow the Notebook here: https://www.kaggle.com/code/aryashah2k/mistakes-to-avoid-in-data-science-python

This dataset was collected by a edtech startup. The startup is into teaching entrepreneurial life-skills in animated-gamified format through its video series to kids between the age group of 6-14 years. Through its learning management system the company tracks the progress made by all of its subscribers on the platform. Company records platform content usage activity data and tries to follow up with parents if there is any inactiveness on the platform by their child. Here's more information about the dataset

Dataset Information:

• Child Name: Name of the subscriber kid
• Email Address: Email address created by parent
• Contact: Contact details of the parent
• follow up: Responses received by the company employee after progress follow-up over the phone.
• response: segregating the follow-up responses in to categories
• Introduction: Tutorial 1
• Activity:- Know your personality, a fun way:Tutorial 2
• A Simple Quiz on the previous Video: Quiz on the Tutorial 2
• Lets see what ‘Product’ is…:Tutorial 3
• A Simple Quiz on the previous Video:Quiz on the Tutorial 3
• Product that represents me: Tutorial 4
• Let's see what 'Service' means: Tutorial 5
• A Simple Quiz on the previous Video:Quiz on the Tutorial 5
• Instruction for 'Product & Service' worksheet:Tutorial 6
• Activity:- Product and Service Worksheet: Exercise on Tutorial 6
• Instructions for Product Word Association:Tutorial 7
• Activity:- Product Word Association:Exercise on Tutorial 7
• Life without products??.... Impossible !:Tutorial 8
• What Is a Need?:Tutorial 9
• A Simple Quiz on the previous Video:Quiz on the Tutorial 9
• Summary of Session 1: Summarizing all the learnings from the Tutorials 1-9
• Your Feedback on Session 1: Feedback page

There is some missing data as well. I hope it would be good dataset for beginners practicing their NLP skills.

Image by Steven Weirather from Pixabay

Note: This dataset is partially synthetic meaning names, email and contact details mentioned are not of the actual customers. Kindly use it for educational and research purposes.

Dataset

This dataset was created by skyhwchoi

Contents

Dataset

This dataset was created by Seol

Contents

Dataset

This dataset was created by BCanOzen

Released under MIT

Contents

Dataset

This dataset was created by Alvin Na

Contents

Dataset

This dataset was created by Dauren Zhaksylykov

Contents

Dataset

This dataset was created by Lorentz

Released under MIT

Contents

Dataset

This dataset was created by Şükrü Yusuf Kaya

Released under MIT

Contents

Context

This dataset is used to practice Pandas for beginners

Content

This dataset is presented with some errors which is needed to be fixed. You can use this dataset to practice: Cleaning NaN values with basic Pandas techniques.

Acknowledgements

I have this dataset from w3school