Translation needed for existing languages

[github-actions]

@sumn2u has made changes on the original english version of the book (PR #251)

Visual Diff of Changes:

diff --git a/en/SUMMARY.md b/en/SUMMARY.md
index a3c47a9..87df426 100644
--- a/en/SUMMARY.md
+++ b/en/SUMMARY.md
@@ -141,6 +141,9 @@
   - [Velocity JS](animation-resources/velocityjs.md)
   - [React Spring](animation-resources/react-spring.md)
   - [Framer Motion](animation-resources/framer-motion.md)
+- [Complexity](complexity/README.md)
+  - [Space Complexity](complexity/space-complexity.md)
+  - [Time Complexity](complexity/time-complexity.md)
 - [JavaScript Behind the Scenes](behind-scenes/README.md)
   - [Call Stack](behind-scenes/call-stack.md)
   - [Engine](behind-scenes/engine.md)
diff --git a/en/complexity/README.md b/en/complexity/README.md
index e46b9b8..468a478 100644
--- a/en/complexity/README.md
+++ b/en/complexity/README.md
@@ -1,11 +1,11 @@
 ---
 layout: editorial
-chapter: 8
-pageNumber: 53
+chapter: 27
+pageNumber: 258
 description: Analyzing an algorithm's efiiciency is most important part of writing an optimal program. This chapter deep dives into analayzing the efficiency by understanding the space and time complexity.
 ---
 
-# Chapter 8
+# Chapter 27
 # Complexity
 
 From previous chapter you should have understood how to create an algorithm. In this chapter, we will explore the intricacies of algorithmic complexity in JavaScript.
diff --git a/en/complexity/time-complexity.md b/en/complexity/time-complexity.md
index 13c4c07..0d8cbe2 100644
--- a/en/complexity/time-complexity.md
+++ b/en/complexity/time-complexity.md
@@ -51,6 +51,6 @@ The code takes n steps to run. Instead of using just plain n for time complexity
 Some common types of Big O notations are as follows:
 
 - O(log n) - logarithmic time complexity
-- O($n^2$) - quadratic time complexity
+- O(n^2) - quadratic time complexity
 - O(n) - linear time complexity
 - O(1) - constant time complexity