Merge pull request #216 from JamBove/patch-1

README.md links in new line

README.md

@@ -37,90 +37,107 @@ A dynamic array is not the same thing as a dynamically allocated array, which is
 
 ### Loops
 Your best tools as a programmer: control flow. Looping is the quintenscence of your work, not duplicating X times your code but factorising it and leaving a small footprint.
->In computer science, control flow (or flow of control) is the order in which individual statements, instructions or function calls of an imperative program are executed or evaluated. The emphasis on explicit control flow distinguishes an imperative programming language from a declarative programming language. 
+>In computer science, control flow (or flow of control) is the order in which individual statements, instructions or function calls of an imperative program are executed or evaluated. The emphasis on explicit control flow distinguishes an imperative programming language from a declarative programming language.  
+
 [_Wikipedia_](https://en.wikipedia.org/wiki/Control_flow)
 
 
 
 ### Search
 #### Linear search
 >In computer science, a linear search or sequential search is a method for finding an element within a list. It sequentially checks each element of the list until a match is found or the whole list has been searched.[1] A linear search runs in at worst linear time and makes at most n comparisons, where n is the length of the list. If each element is equally likely to be searched, then linear search has an average case of n/2 comparisons, but the average case can be affected if the search probabilities for each element vary. Linear search is rarely practical because other search algorithms and schemes, such as the binary search algorithm and hash tables, allow significantly faster searching for all but short lists.[2]
+  
 [_Wikipedia_](https://en.wikipedia.org/wiki/Linear_search)
 
 #### Binary search
 >In computer science, binary search, also known as half-interval search,[1] logarithmic search,[2] or binary chop,[3] is a search algorithm that finds the position of a target value within a sorted array.[4][5] Binary search compares the target value to the middle element of the array. If they are not equal, the half in which the target cannot lie is eliminated and the search continues on the remaining half, again taking the middle element to compare to the target value, and repeating this until the target value is found. If the search ends with the remaining half being empty, the target is not in the array.
+  
 [_Wikipedia_](https://en.wikipedia.org/wiki/Binary_search_algorithm)
 
 #### Breadth First Search
 >Breadth First Traversal (or Search) for a graph is similar to Breadth First Traversal of a tree. The only catch here is, unlike trees, graphs may contain cycles, so we may come to the same node again. To avoid processing a node more than once, we use a boolean visited array. For simplicity, it is assumed that all vertices are reachable from the starting vertex.
+  
 [_geeksforgeeks_](https://www.geeksforgeeks.org/breadth-first-search-or-bfs-for-a-graph/)
 
 #### Depth First Search
->Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph) and explores as far as possible along each branch before backtracking.
-[_wikipedia_](https://en.wikipedia.org/wiki/Depth-first_search)
+>Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph) and explores as far as possible along each branch before backtracking.  
+
+[_Wikipedia_](https://en.wikipedia.org/wiki/Depth-first_search)
 
 #### Ternary Search
->A ternary search algorithm is a technique in computer science for finding the minimum or maximum of a unimodal function. A ternary search determines either that the minimum or maximum cannot be in the first third of the domain or that it cannot be in the last third of the domain, then repeats on the remaining two thirds. A ternary search is an example of a divide and conquer algorithm.
-[_Wikipedia](https://en.wikipedia.org/wiki/Ternary_search)
+>A ternary search algorithm is a technique in computer science for finding the minimum or maximum of a unimodal function. A ternary search determines either that the minimum or maximum cannot be in the first third of the domain or that it cannot be in the last third of the domain, then repeats on the remaining two thirds. A ternary search is an example of a divide and conquer algorithm.  
+
+[_Wikipedia_](https://en.wikipedia.org/wiki/Ternary_search)
 
 
 ### Sorting
 #### Heap Sort
->In computer science, heapsort is a comparison-based sorting algorithm. Heapsort can be thought of as an improved selection sort: like that algorithm, it divides its input into a sorted and an unsorted region, and it iteratively shrinks the unsorted region by extracting the largest element and moving that to the sorted region. The improvement consists of the use of a heap data structure rather than a linear-time search to find the maximum.
+>In computer science, heapsort is a comparison-based sorting algorithm. Heapsort can be thought of as an improved selection sort: like that algorithm, it divides its input into a sorted and an unsorted region, and it iteratively shrinks the unsorted region by extracting the largest element and moving that to the sorted region. The improvement consists of the use of a heap data structure rather than a linear-time search to find the maximum.  
 
 [_Wikipedia_](https://en.wikipedia.org/wiki/Heapsort)
 
 #### Merge Sort
 >In computer science, merge sort (also commonly spelled mergesort) is an efficient, general-purpose, comparison-based sorting algorithm. Most implementations produce a stable sort, which means that the order of equal elements is the same in the input and output. Merge sort is a divide and conquer algorithm that was invented by John von Neumann in 1945.[2] A detailed description and analysis of bottom-up mergesort appeared in a report by Goldstine and von Neumann as early as 1948.
+  
 [_Wikipedia_](https://en.wikipedia.org/wiki/Merge_sort)
 
 #### Bubble Sort
 >Bubble sort, sometimes referred to as sinking sort, is a simple sorting algorithm that repeatedly steps through the list, compares adjacent elements and swaps them if they are in the wrong order. The pass through the list is repeated until the list is sorted. The algorithm, which is a comparison sort, is named for the way smaller or larger elements "bubble" to the top of the list. Although the algorithm is simple, it is too slow and impractical for most problems even when compared to insertion sort.[2] Bubble sort can be practical if the input is in mostly sorted order with some out-of-order elements nearly in position.
+  
 [_Wikipedia_](https://en.wikipedia.org/wiki/Bubble_sort)
 
 #### QuickSort
 >Quicksort (sometimes called partition-exchange sort) is an efficient sorting algorithm, serving as a systematic method for placing the elements of a random access file or an array in order. Quicksort is a comparison sort, meaning that it can sort items of any type for which a "less-than" relation (formally, a total order) is defined. 
+  
 [_Wikipedia_](https://en.wikipedia.org/wiki/Quicksort)
 
 #### Topological Sorting
->In computer science, a topological sort or topological ordering of a directed graph is a linear ordering of its vertices such that for every directed edge uv from vertex u to vertex v, u comes before v in the ordering. For instance, the vertices of the graph may represent tasks to be performed, and the edges may represent constraints that one task must be performed before another; in this application, a topological ordering is just a valid sequence for the tasks. A topological ordering is possible if and only if the graph has no directed cycles, that is, if it is a directed acyclic graph (DAG). Any DAG has at least one topological ordering, and algorithms are known for constructing a topological ordering of any DAG in linear time.
+>In computer science, a topological sort or topological ordering of a directed graph is a linear ordering of its vertices such that for every directed edge uv from vertex u to vertex v, u comes before v in the ordering. For instance, the vertices of the graph may represent tasks to be performed, and the edges may represent constraints that one task must be performed before another; in this application, a topological ordering is just a valid sequence for the tasks. A topological ordering is possible if and only if the graph has no directed cycles, that is, if it is a directed acyclic graph (DAG). Any DAG has at least one topological ordering, and algorithms are known for constructing a topological ordering of any DAG in linear time.  
+
 [_Wikipedia_](https://en.wikipedia.org/wiki/Topological_sorting)
 
 #### Segment Tree
->In computer science, a segment tree also known as a statistic tree is a tree data structure used for storing information about intervals, or segments. It allows querying which of the stored segments contain a given point. It is, in principle, a static structure; that is, it's a structure that cannot be modified once it's built. A similar data structure is the interval tree.
+>In computer science, a segment tree also known as a statistic tree is a tree data structure used for storing information about intervals, or segments. It allows querying which of the stored segments contain a given point. It is, in principle, a static structure; that is, it's a structure that cannot be modified once it's built. A similar data structure is the interval tree.  
+
 [_Wikipedia_](https://en.wikipedia.org/wiki/Segment_tree)
 
 #### Stack
 > In computer science, a stack is an abstract data type that serves as a collection of elements, with two principal operations:
 >* **push**, which adds an element to the collection, and
->* **pop**, which removes the most recently added element that was not yet removed.
+>* **pop**, which removes the most recently added element that was not yet removed.  
+
 [_Wikipedia_](https://en.wikipedia.org/wiki/Stack_(abstract_data_type))
 
 #### Queue
->In computer science, a queue is a collection in which the entities in the collection are kept in order and the principal (or only) operations on the collection are the addition of entities to the rear terminal position, known as enqueue, and removal of entities from the front terminal position, known as dequeue. This makes the queue a First-In-First-Out (FIFO) data structure. In a FIFO data structure, the first element added to the queue will be the first one to be removed.
+>In computer science, a queue is a collection in which the entities in the collection are kept in order and the principal (or only) operations on the collection are the addition of entities to the rear terminal position, known as enqueue, and removal of entities from the front terminal position, known as dequeue. This makes the queue a First-In-First-Out (FIFO) data structure. In a FIFO data structure, the first element added to the queue will be the first one to be removed.  
+
 [_Wikipedia_](https://en.wikipedia.org/wiki/Queue_(abstract_data_type))
 
 #### Dijkstra's algorithm
->Dijkstra's algorithm (or Dijkstra's Shortest Path First algorithm, SPF algorithm) is an algorithm for finding the shortest paths between nodes in a graph, which may represent, for example, road networks. The algorithm exists in many variants. Dijkstra's original algorithm found the shortest path between two given nodes, but a more common variant fixes a single node as the "source" node and finds shortest paths from the source to all other nodes in the graph, producing a shortest-path tree.
+>Dijkstra's algorithm (or Dijkstra's Shortest Path First algorithm, SPF algorithm) is an algorithm for finding the shortest paths between nodes in a graph, which may represent, for example, road networks. The algorithm exists in many variants. Dijkstra's original algorithm found the shortest path between two given nodes, but a more common variant fixes a single node as the "source" node and finds shortest paths from the source to all other nodes in the graph, producing a shortest-path tree.  
+
 [_Wikipedia_](https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm)
 
 #### OOP
 >Object-oriented programming (OOP) is a programming paradigm based on the concept of "objects", which can contain data, in the form of fields (often known as attributes or properties), and code, in the form of procedures (often known as methods). A feature of objects is an object's procedures that can access and often modify the data fields of the object with which they are associated (objects have a notion of "this" or "self"). In OOP, computer programs are designed by making them out of objects that interact with one another. There are 4 major principles:
 >* **Encapsulation** is the mechanism of hiding of data implementation by restricting access to public methods. Instance variables are kept private and accessor methods are made public to achieve this.
 >* **Data Abstraction** means a concept or an Idea which is not associated with any particular instance. Using abstract class/Interface we express the intent of the class rather than the actual implementation. In a way, one class should not know the inner details of another in order to use it, just knowing the interfaces should be good enough.
 >* **Inheritance** expresses “is-a” and/or “has-a” relationship between two objects. Using Inheritance, In derived classes we can reuse the code of existing super classes.
->* **Polymorphism** means one name many forms. It is further of two types — static and dynamic. Static polymorphism is achieved using method overloading and dynamic polymorphism using method overriding. It is closely related to inheritance. We can write a code that works on the superclass, and it will work with any subclass type as well.
+>* **Polymorphism** means one name many forms. It is further of two types — static and dynamic. Static polymorphism is achieved using method overloading and dynamic polymorphism using method overriding. It is closely related to inheritance. We can write a code that works on the superclass, and it will work with any subclass type as well.  
+
 [_Wikipedia_](https://en.wikipedia.org/wiki/Object-oriented_programming), [_Medium_](https://medium.com/@cancerian0684/what-are-four-basic-principles-of-object-oriented-programming-645af8b43727)
 
 ### Dynamic Programming
 >Dynamic programming is both a mathematical optimization method and a computer programming method. The method was developed by **Richard Bellman** in the 1950s and has found applications in numerous fields, from aerospace engineering to economics. In both contexts it refers to simplifying a complicated problem by breaking it down into simpler sub-problems in a recursive manner. While some decision problems cannot be taken apart this way, decisions that span several points in time do often break apart recursively. Likewise, in computer science, if a problem can be solved optimally by breaking it into sub-problems and then recursively finding the optimal solutions to the sub-problems, then it is said to have optimal substructure.
-If sub-problems can be nested recursively inside larger problems, so that dynamic programming methods are applicable, then there is a relation between the value of the larger problem and the values of the sub-problems.[1] In the optimization literature this relationship is called the **Bellman equation**.
+If sub-problems can be nested recursively inside larger problems, so that dynamic programming methods are applicable, then there is a relation between the value of the larger problem and the values of the sub-problems.[1] In the optimization literature this relationship is called the **Bellman equation**.  
+
 [_Wikipedia_](https://en.wikipedia.org/wiki/Dynamic_programming)
 
 ### Machine Learning
 >Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.[1][2]:2 Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.
 
->Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning.[3][4] In its application across business problems, machine learning is also referred to as predictive analytics.
+>Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning.[3][4] In its application across business problems, machine learning is also referred to as predictive analytics.  
+
 [_Wikipedia_](https://en.wikipedia.org/wiki/Machine_learning)
 
 ### Networking
@@ -130,7 +147,8 @@ If sub-problems can be nested recursively inside larger problems, so that dynami
 
 >The level of skill required to operate a network directly correlates to the complexity of a given network. For example, a large enterprise may have thousands of nodes and rigorous security requirements, such as end-to-end encryption, requiring specialized network administrators to oversee the network.
 
->At the other end of the spectrum, a layperson may set up and perform basic troubleshooting for a home Wi-Fi network with a short instruction manual. Both examples constitute computer networking.
+>At the other end of the spectrum, a layperson may set up and perform basic troubleshooting for a home Wi-Fi network with a short instruction manual. Both examples constitute computer networking.  
+
 [_Wikipedia_](https://en.wikipedia.org/wiki/Computer_network)
 
 ### Socket Programming