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Tuesday, September 1, 2026
Sunday, August 30, 2026
How to Download and Export YouTube Watch History in Youtube

How to Download and Export YouTube Watch History [and More]
When watching YouTube videos, your watch history is, by default, stored in your Google account (unless otherwise excluded by you via Settings). In addition, you can download and export your YouTube activities to an offline HTML or JSON file format.
You will only have watch history if you are logged in using your Google account (Gmail) while watching YouTube videos.
Table Of ContentsHow to export YouTube Watch History?
How to export YouTube Watch History?
Here are the two (2) different ways you can use to export your YouTube watch history.
1. Export via Google Takeout
Google Takeout is a feature from Google that allows users to export their information when it comes to using Google’s services, such as YouTube, Chrome web browser, Google Maps, etc.
For YouTube, you can have the option to export all or selected YouTube data, such as comments, likes, subscriptions, and many more.
To download your YouTube data using Google Takeout, follow the steps below.
- Ensure you are logged into your YouTube account and go to the Google Takeout page.
- Scroll down the list and select <YouTube and YouTube Music>

There are some optional export configurations you can make here.

- You can choose your export output as HTML or JSON format under the <Mutiple formats> setting.

- You can exclude most YouTube data, such as subscriptions, playlists, etc. and include only the history. This can be found in the <All YouTube data included> setting.
- Click <Next step> and choose <Send downlink link via email>.
- Click <Create Report>. You will receive the download link in your Gmail inbox (check your Spam) and on the actual Google Takeout webpage.

You have now successfully downloaded your YouTube watch history (and any other Google data you have selected) offline in HTML or JSON format.
2. Export via YouTube History Page
YouTube provides a single webpage called “History Page” that displays the list of your watched history. You can export this page.
To access it, follow the steps below.

Go to the YouTube homepage and ensure you are logged in to your Google account.
On the left pane, click on <History> to display your list of previously watched videos.
Now click on <Manage all history> on the right pane.

You will now see the <Youtube History> page.
To export the list of watched videos, you can right-click anywhere on the page and choose;
- Select <Print> and save as PDF document format or
- Save as Webpage Complete as HTML webpage format.
Monday, August 24, 2026
pc build 2026
Intel® Core™ Ultra 5 225 ₱11,900.00
Kingston Fury Beast DDR5
5200MHz (16gb-11000)
Asus(H810PrimeMK-6100)
Friday, August 21, 2026
Data Structures and Algorithms Specialization
https://www.coursera.org/specializations/data-structures-algorithms#courses
Specialization - 6 course series
Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.
Applied Learning Project
The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco 1000 times faster than the shortest path algorithms you learn in the standard Algorithms 101 course! Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.
Algorithmic Toolbox
Course 1, 41 hours
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