Update bloom-filter.md

Author: LiorKogan

content/develop/data-types/probabilistic/bloom-filter.md

@@ -19,7 +19,7 @@ weight: 10
 
 A Bloom filter is a probabilistic data structure in Redis Stack that enables you to check if an element is present in a set using a very small memory space of a fixed size.
 
-Instead of storing all of the elements in the set, Bloom Filters store only the elements' hashed representation, thus sacrificing some precision. The trade-off is that Bloom Filters are very space-efficient and fast.
+Instead of storing all of the elements in the set, Bloom Filters store only the items' hashed representation, thus sacrificing some precision. The trade-off is that Bloom Filters are very space-efficient and fast.
 
 A Bloom filter can guarantee the absence of an element from a set, but it can only give an estimation about its presence. So when it responds that an element is not present in a set (a negative answer), you can be sure that indeed is the case. But one out of every N positive answers will be wrong. Even though it looks unusual at a first glance, this kind of uncertainty still has its place in computer science. There are many cases out there where a negative answer will prevent more costly operations, for example checking if a username has been taken, if a credit card has been reported as stolen, if a user has already seen an ad and much more.
 
@@ -142,7 +142,7 @@ Just as a comparison, when using a Redis set for membership testing the memory n
 memory_with_sets = capacity*(192b + value)
 ```
 
-For a set of IP addresses, for example, we would have around 40 bytes (320 bits) per element, which is considerably higher than the 19.170 bits per item we need for a Bloom filter with 0.01% precision.
+For a set of IP addresses, for example, we would have around 40 bytes (320 bits) per element - considerably higher than the 19.170 bits per item we need for a Bloom filter with a 0.01% probability of false positives.
 
 
 ## Bloom vs. Cuckoo filters