style: consistent case for User-Agent and JavaScript

Author: honzajavorek

sources/academy/glossary/tools/user_agent_switcher.md

@@ -15,14 +15,14 @@ slug: /tools/user-agent-switcher
 
 ![User-Agent Switcher groups](./images/user-agent-switcher-groups.png)
 
-Clicking on a group will display a list of possible user-agents to set.
+Clicking on a group will display a list of possible User-Agents to set.
 
 ![Default available Internet Explorer agents](./images/user-agent-switcher-agents.png)
 
 After setting the **User-Agent**, the page will be refreshed.
 
 ## Configuration
 
-The extension configuration page allows you to edit the **User-Agent** list in case you want to add a specific user-agent that isn't already provided. You can find some other options, but most likely you will never need to modify those.
+The extension configuration page allows you to edit the **User-Agent** list in case you want to add a specific User-Agent that isn't already provided. You can find some other options, but most likely you will never need to modify those.
 
 ![User-Agent Switcher configuration page](./images/user-agent-switcher-config.png)

sources/academy/webscraping/anti_scraping/index.md

@@ -117,7 +117,7 @@ This is the most straightforward and standard protection, which is mainly implem
 
 ### Header checking
 
-This type of bot identification is based on the given fact that humans are accessing web pages through browsers, which have specific [header](../../glossary/concepts/http_headers.md) sets which they send along with every request. The most commonly known header that helps to detect bots is the `user-agent` header, which holds a value that identifies which browser is being used, and what version it's running. Though `user-agent` is the most commonly used header for the **Header checking** method, other headers are sometimes used as well. The evaluation is often also run based on the header consistency, and includes a known combination of browser headers.
+This type of bot identification is based on the given fact that humans are accessing web pages through browsers, which have specific [header](../../glossary/concepts/http_headers.md) sets which they send along with every request. The most commonly known header that helps to detect bots is the `User-Agent` header, which holds a value that identifies which browser is being used, and what version it's running. Though `User-Agent` is the most commonly used header for the **Header checking** method, other headers are sometimes used as well. The evaluation is often also run based on the header consistency, and includes a known combination of browser headers.
 
 ### URL analysis
 
@@ -131,7 +131,7 @@ One of the best ways of avoiding the possible breaking of your scraper due to we
 
 ### IP session consistency
 
-This technique is commonly used to entirely block the bot from accessing the website altogether. It works on the principle that every entity that accesses the site gets a token. This token is then saved together with the IP address and HTTP request information such as user-agent and other specific headers. If the entity makes another request, but without the session token, the IP address is added on the greylist.
+This technique is commonly used to entirely block the bot from accessing the website altogether. It works on the principle that every entity that accesses the site gets a token. This token is then saved together with the IP address and HTTP request information such as User-Agent and other specific headers. If the entity makes another request, but without the session token, the IP address is added on the greylist.
 
 ### Interval analysis
 

sources/academy/webscraping/anti_scraping/techniques/browser_challenges.md

@@ -11,15 +11,15 @@ slug: /anti-scraping/techniques/browser-challenges
 
 ## Browser challenges
 
-Browser challenges are a type of security measure that relies on browser fingerprints. These challenges typically involve a javascript script that collects both static and dynamic browser fingerprints. Static fingerprints include attributes such as user-agent, video card, and number of CPU cores available. Dynamic fingerprints, on the other hand, might involve rendering fonts or objects in the canvas (known as a [canvas fingerprint](./fingerprinting.md#with-canvases)), or playing audio in the [AudioContext](./fingerprinting.md#from-audiocontext). We were covering the details in the previous [fingerprinting](./fingerprinting.md) lesson.
+Browser challenges are a type of security measure that relies on browser fingerprints. These challenges typically involve a JavaScript program that collects both static and dynamic browser fingerprints. Static fingerprints include attributes such as User-Agent, video card, and number of CPU cores available. Dynamic fingerprints, on the other hand, might involve rendering fonts or objects in the canvas (known as a [canvas fingerprint](./fingerprinting.md#with-canvases)), or playing audio in the [AudioContext](./fingerprinting.md#from-audiocontext). We were covering the details in the previous [fingerprinting](./fingerprinting.md) lesson.
 
 While some browser challenges are relatively straightforward - for example, just loading an image and checking if it renders correctly - others can be much more complex. One well-known example of a complex browser challenge is Cloudflare's browser screen check. In this challenge, Cloudflare visually inspects the browser screen and blocks the first request if any inconsistencies are found. This approach provides an extra layer of protection against automated attacks.
 
 Many online protections incorporate browser challenges into their security measures, but the specific techniques used can vary.
 
 ## Cloudflare browser challenge
 
-One of the most well-known browser challenges is the one used by Cloudflare. Cloudflare has a massive dataset of legitimate canvas fingerprints and user-agent pairs, which they use in conjunction with machine learning algorithms to detect any device property spoofing. This might include spoofed user-agents, operating systems, or GPUs.
+One of the most well-known browser challenges is the one used by Cloudflare. Cloudflare has a massive dataset of legitimate canvas fingerprints and User-Agent pairs, which they use in conjunction with machine learning algorithms to detect any device property spoofing. This might include spoofed User-Agent headers, operating systems, or GPUs.
 
 ![Cloudflare browser check](https://images.ctfassets.net/slt3lc6tev37/55EYMR81XJCIG5uxLjQQOx/252a98adf90fa0ff2f70437cc5c0a3af/under-attack-mode_enabled.gif)