We are all aware of how algorithms are vital to Google's existence. The complex algorithms that Google uses to produce results is truly formidable. Algorithms form the basis of any computing program to achieve the desired result. Take the first step towards learning interesting concepts about Algorithms at TechGig with advantageous online tutorials. Algorithms are everywhere! It's just not possible to write any significant software code without in-depth knowledge about algorithms. At TechGig, we introduce you to some ideas related to the analysis of algorithms, and then help you put these into practice with a few examples illustrating the practical steps.
Dig into the depths of algorithms
Practice does make a man perfect and at TechGig, we give you the perfect opportunity to grasp the various concepts of algorithm and put them to practice.
We cover a variety of topics that includes Dynamic Programming, Greedy Algorithms, Bit Algorithms and a wide range of sorting techniques etc. Also, after seeking knowledge from the tutorials you will be able to solve a greater variety of problems. Once you have mastered the sample tutorial problem highlighted under algorithm practice problems, you can further test your skills by solving various coding problems and eventually move on to multiple choice problems, after having gained sufficient expertise.
Comprehensive details along with practice samples allows you to optimally use the information available on TechGig to your advantage. Become a pro at creating complex algorithms with these simple yet easy to understand lessons that guide you step by step from beginner level to an advance stage.
Besides, search engines, algorithms are used in a wide range of fields such as medicine, psychology, and transportation and the list doesn't end here! If you have been eager to learn, however, your professional commitments do not allow you to enroll for a course, then TechGig is the platform you may have been looking for. Simple and practical study guide developed by industry experts helps you to learn myriad facets of algorithms and practice them as and when you have grasped the concepts and at your convenience.
So, get on the fast track path of learning, practicing and becoming a pro at algorithms.
In this TechGig Practice section on implementation of algorithms, you will get to solve many interesting problems, but before you do that we wanted to share some of the best practices of writing a good algorithm:
The algorithm you write needs to clearly define the input and output
It should outline each step in an unambiguous and clear manner
It should solve the problem in the minimum number of steps
It should be most effective in different ways to solve a problem
It should be written in such a way that it can be integrated in similar programming languages
It should cover all the possible causes
It should come to an end or exit
There exists so much data out there that without the help of algorithms it would be difficult to mine the structured and unstructured data and help organisations derive actionable insights from it.
A search algorithm helps in solving a search-related problem. It is used to fetch information stored inside a data structure either using discrete or continuous values. The following are the two types of searching algorithms:
Linear Search
A Linear Search is a simple search algorithm where elements or values in a given array can be searched by passing the array from the start till the desired value or the element is found. A Linear Search has a simple implementation and is applied where there are few elements in a list, e.g. in unsorted or unordered lists.
Binary Search
Binary Search is used only with sorted array or list and is useful when there are large number of elements in an array and they are sorted. It is therefore a prerequisite for the Binary Search to work correctly that the list or array should be sorted. The process of a Binary Search involves comparing the element that needs to be searched with the element in the middle of the list and/or array. The index of the middle element is then returned, upon a match.
A sorting algorithm is an algorithm that places the elements of a list in the desired order such as alphabetically or numerically. Sorting algorithms come in handy in situations where other algorithms such as search algorithms need to be optimized as the inputted data needs to be sorted.
Such an algorithm includes a series of instructions that receive input in the form of arrays and are used to perform specific operations on the array. Some of the common sorting algorithms include bubble sort, heap sort, quick sort, merge sort, insertion sort, and counting sort.
Greedy algorithms are intuitive algorithms that will always make the decision that it thinks will be right at that moment. These algorithms are used in machine learning, business intelligence, artificial intelligence, etc. Greedy algorithms will have key components like an objective function to assign a value to a solution and a selection function that will choose the best candidate to be added to the solution.
Graph Theory is the study of graphs that depicts relations between objects. It reveals different ways in which vertices can be connected to edges through lines. These graphs are then classified as per their complexity such as the number of edges allowed between any two vertices, or any other sets of rules in specific properties. Graph theory is highly used in the Air Traffic Management and the Maps.
Flood fill is an algorithm that determines the area that is connected to a given node in a multi-dimensional array. Two best examples of Flood fill are the bucket fill tool in MS paint or the popular Minesweeper game revolving around adjoined pieces. This algorithm works around three key parameters: the start node, replacement color, and target color.
String-searching algorithms or string-matching algorithms are an important class of string algorithms looking for a place where one or several strings, often called patterns are found within a larger string. KMP is an example of Pattern Searching/ String matching Algorithms.
Algorithmic game theory is an emerging area at the intersection of computer science and microeconomics. Motivated by the rise of the internet and electronic commerce, computer scientists have turned to models where problem inputs are held by distributed, selfish agents. This new perspective leads to a host of fascinating questions on the interplay between computation and incentives.
The key objective of game theory in algorithms is to understand design algorithms in different environments. The algorithmic game theory problems that you will find here on this TechGig Practice section will help you improve your skills in this area. In such game theory problems, the algorithm input is distributed among many participants or players who are responsible for the output.
Dynamic Programming is an optimization over plain recursion and a powerful technique to solve a given class of problems. The idea behind Dynamic Programming is very simple. It means that if you have solved a problem through a given input, then you need to save the result to avoid solving the same problem again in the future. What that means is that if ever you see a recursive solution with the same input calls repeated you can optimize it using Dynamic Programming.
Bit algorithms are used to perform operations at the bit-level or used to manipulate bits. These algorithms are set to be much faster and are used for improving the efficiency of a program. Bitwise operators can be used to perform bitwise operations that involve manipulation of one or more bits of a bit pattern.
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