What games are most similar to Professor Teaches Photoshop Creative Cloud?

The most similar games to Professor Teaches Photoshop Creative Cloud include Theory Test UK 2016/17 - Driving Test Success, English Teaching Assistant VR, Driving Theory Test UK 2017/18 - Driving Test Success, and more. These recommendations are based on shared tags, gameplay mechanics, and player behavior patterns.

What genre is Professor Teaches Photoshop Creative Cloud?

Professor Teaches Photoshop Creative Cloud is a Software Training game with popular tags including Software Training, Education.

Best software training games like Professor Teaches Photoshop Creative Cloud?

If you're looking for more software training games like Professor Teaches Photoshop Creative Cloud, try Theory Test UK 2016/17 - Driving Test Success, English Teaching Assistant VR, Driving Theory Test UK 2017/18 - Driving Test Success. These share the Software Training tag and similar gameplay elements.

How does Nodal find games like Professor Teaches Photoshop Creative Cloud?

Nodal uses a hybrid recommendation system that combines tag similarity analysis with collaborative filtering from player behavior data. We analyze which games are frequently played by the same players and share similar tags to Professor Teaches Photoshop Creative Cloud. You can tune the blend between content-based and community-based recommendations.

Games like Professor Teaches Photoshop Creative Cloud but more education?

To find games emphasizing education, use Nodal's tag filters to prioritize Education alongside Software Training. You can also explore the education tag page directly.

Games Like Professor Teaches Photoshop Creative Cloud

60+ similar titles based on shared tags and player behavior. Themes include Software Training, Education, VR, Psychological Horror.

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Professor Teaches Photoshop Creative Cloud header art

Professor Teaches Photoshop Creative Cloud is a fast and easy way to learn Adobe Photoshop CC, the premier image design tool used by artists, animators, design agencies, and web developers and designers. Professor Teaches Photoshop CC provides complete tutorials, guides, and interactive exercises…

Reviews: Negative — 15% — of 13 reviews

Released: 2015-10-06

Developer: Individual Software

Genres: Software Training

Platforms: Windows

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Professor Teaches Photoshop Creative Cloud

Oct 6, 2015

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PsychLabVR

May 4, 2017

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Aug 25, 2020

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#10

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Professor Teaches Photoshop Creative Cloud

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Release DateOct 6, 2015

Current playersN/A

Review Score70.9% – Negative

Total reviews13

DeveloperIndividual Software

PublisherIndividual Software

PlatformsWindows

Websiteprofessorteaches.com/course_info/photoshop_cc.shtml

How to Use This Page

Find games similar to your favorites using a hybrid recommendation engine, and optionally tune the results to match your taste. This page includes multiple recommendation models, including collaborative filtering (player behavior) and tag-based similarity to surface games you might love.

What do the different models mean?

•Content: Finds similar games only using Steam tags. Disregards all player behavior data. Note: this model's accuracy depends on how well the tags reflect the game, which can be less reliable for newer or smaller titles.
•Community: Finds games using only player behavior ("people who play X also play Y"). Disregards all tags.
•Community Variants (Mode I–VII): Alternate community engines with different behaviors. Users often prefer different modes, so try a couple and keep the one that best matches your taste.
•Blend: A customizable mix of any two models for more balanced or personalized recommendations.

What is the graph showing?

•It's a "map of Steam tags." The graph uses tag similarity to project games into a 2D space, revealing clusters of games that share similar themes and mechanics.
•Position = tag similarity (compressed). We're squeezing 400+ tags (and their combinations) into 2D, so some detail is inevitably lost. Overall, local neighborhoods should still be quite accurate.
•Size = popularity. Bigger nodes generally mean more widely played games.
•Color = whatever you pick. Use the color dropdown to switch between cluster views (and other color modes).
•Important: The left-hand ranking shows the true recommendation order. It's computed using the selected model's full similarity space, not the 2D map.

How does re-ranking work?

The re-ranking sliders let you personalize the list without changing the underlying model.

•All sliders at 0 = default ranking for the current model.
•As you add weights, we re-rank the results to emphasize what you care about (e.g., more niche, more recent, higher rated, etc.).

What does the purple score bar mean?

•The white vertical line shows the game's score under the model's original ranking.
•The purple bar shows the game's score after your tuning is applied.

Frequently Asked Questions

Why is the #1 game score ~95% in every model?

Because each model produces scores on its own internal scale (different ranges, different distributions). To make the UI readable, we normalize scores so they're best interpreted as relative rankings, not whether one model's raw score is "bigger" than another's.

Why are two games close on the graph but far apart in the list?

The graph is a 2D tag-only map (compressed). The list is computed using the chosen model in its full similarity space. So the map is great for intuition and discovery, but the ranked list is the accurate ordering.

What's the difference between Community variants (Mode I–VII)?

Each mode is a different recommendation engine trained on Steam community co-play patterns. No tags are used here. The main difference is how similarity is measured, which changes whether results lean toward bigger, widely played games or tighter, more aligned neighbors. For example, starting from Hades, one mode may favor large games that many Hades players also own, while another will favor smaller roguelites that are more directly aligned, even if the shared audience is smaller. Try a couple of modes and stick with the one that matches what you mean by "similar."

Why did you make this?

Because people love great games, and most recommenders are either a black box or too generic. I wanted something high-quality, visual, and customizable, built from a stats / machine learning mindset.

Quick Tips

•Hover games in the list to highlight them on the graph
•Don't sweat the absolute % too much, focus on relative order
•Click around the graph to discover clusters you didn't know you liked