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 Naimul Hasan Shadesh

Released under Apache 2.0

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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

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 Lisette

Released under CC0: Public Domain

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Dataset

This dataset was created by Nelson InCube

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Dataset

This dataset was created by Minkoo Yoon

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Welcome! Are you completely new to programming? If not then we presume you will be looking for information about why and how to get started with Python. Fortunately an experienced programmer in any programming language (whatever it may be) can pick up Python very quickly. It's also easy for beginners to use and learn, so jump in! Python is a powerful general-purpose programming language. It is used in web development, data science, creating software prototypes, and so on. Fortunately for beginners, Python has simple easy-to-use syntax. This makes Python an excellent language to learn to program for beginners.

Our Python tutorial will guide you to learn Python one step at a time.

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} }

This dataset was created to be the base of the data.world SQL tutorial exercises. Data was genererated using Synthea, a synthetic patient generator that models the medical history of synthetic patients. Their mission is to output high-quality synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from cost, privacy, and security restrictions, enabling research with Health IT data that is otherwise legally or practically unavailable. De-identified real data still presents a challenge in the medical field because there are peopel who excel at re-identification of these data. For that reason the average medical center, etc. will not share their patient data. Most governmental data is at the hospital level. NHANES data is an exception.

You can read Synthea's first academic paper here.

Original Data

Acknowlegement

Foto von Rubaitul Azad auf Unsplash

The Learning Resources Database is a catalog of interactive tutorials, videos, online classes, finding aids, and other instructional resources on National Library of Medicine (NLM) products and services. Resources may be available for immediate use via a browser or downloadable for use in course management systems

Dataset Description

It contains 520 rows and 13 variables as listed below - - Resource ID : Alphanumeric identifier - Resource Name : Title of the resource - Resource URL : Link of the resource - Description : Brief explanation on the reource - Archived : Flagged as False for all data points - Format : Format of the resource ex. HTML, PDF, MP4 video , MS Word, Powerpoint etc. - Type : Type of the resource ex Webinar, document, tutorial, slides etc. - Runtime : Runtime of the resource - Subject Areas : Topic covered in reource - Authoring Organization : Name of the Authoring Organization - Intended Audiences : Profile of the intended audience - Record Modified : Timestamp info on record last modification - Resource Revised : Timestamp info on resource last modified

Dataset

This dataset was created by Athira Pisharody

Contents

This is the dataset that goes along with the Deep Learning basics with Python, TensorFlow and Keras p.2 Tutorial provided by Sentdex. Link here: https://www.youtube.com/watch?v=j-3vuBynnOE&list=PLQVvvaa0QuDfhTox0AjmQ6tvTgMBZBEXN&index=2

Original data source: https://www.youtube.com/redirect?redir_token=wz7434m56UWH1Vs4X1Je76XEoE58MTUzOTA3NDQwMUAxNTM4OTg4MDAx&q=https%3A%2F%2Fwww.microsoft.com%2Fen-us%2Fdownload%2Fconfirmation.aspx%3Fid%3D54765&v=j-3vuBynnOE&event=video_description

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 Athena Liu

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Dataset

This dataset was created by Carlos Souza

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Context

Datacamp.com is one of the most popular websites to learn Data Science from. It has courses & tutorials in both R & Python and has courses for different verticals & industries.

Content

The data was scraped from datacamp keeping in mind the need to find the list of tutorials that datacamp offers across various topics.

Acknowledgements

This dataset was based on datacamp's tutorial to scrap web pages using rvest

Inspiration

If you are looking to find tutorials in a particular topic, you can find from this dataset rather than scrolling through the pages all day long. Hope this is useful for everyone.

Dataset

This dataset was created by Lucas Henrique Mateo

Released under Apache 2.0

Contents

Context

On the Pytorch website, you'll find a bunch of tutorials. This Dataset is the one used for the dataloading one.

Content

It contains some faces images and a CSV file with their respective landmarks points.

Acknowledgements

Thanks for pytorch to provide comprehensive material to get a grasp about their framework. https://pytorch.org/tutorials/beginner/data_loading_tutorial.html

Inspiration

Play with pytorch or check how much simpler it is to accomplish this kind of task with a different framework.

Dataset

This dataset was created by Joe Fitzgerald

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