Algorithms are essential in computer science, especially for Data Science and Machine Learning. Find out everything you need to know about them: definition, functioning, use cases, training…
The term “algorithm” derives from the name of the great Persian mathematician Al Khwarizmi, who lived around the year 820 and who introduced decimal numbering to the West (from India) and taught the elementary arithmetic rules related to it. Subsequently, the concept of algorithm was extended to more and more complex objects: texts, pictures, logical formulas and physical objects, among others.
Already essential in the field of computer programming, algorithms are becoming increasingly important in the age of Big Data and artificial intelligence. So what are they actually? If you are looking for a clear and complete definition, you are in the right place…
What is an algorithm?
An algorithm is essentially a step-by-step procedure. It is a set of rules to follow to accomplish a task or solve a problem.
Long before the emergence of computers, humans were already using algorithms. We can consider that cooking recipes, mathematical operations or even the instructions for assembling a piece of furniture are algorithms.
In the field of computer programming, algorithms are sets of rules telling the computer how to perform a task. In reality, a computer program is an algorithm that tells the computer what steps to perform and in what order to accomplish a specific task. They are written using a programming language.
What are the different types of algorithms?
There are a wide variety of algorithms, classified according to the concepts they use to accomplish a task. Here are the main categories:
Divide-and-conquer algorithms divide a problem into several subproblems of the same type. These smaller problems are solved, and their solutions are combined to solve the original problem.
Brute force algorithms test all possible solutions until the best one is found. A randomized algorithm uses a random number at least once during the calculation to find the solution to the problem.
A gluttonous algorithm finds the optimal solution at the local level, in order to find an optimal solution for the global problem. A recursive algorithm solves the simplest version of a problem and then solves larger and larger versions until the solution to the original problem is found.
A traceback algorithm divides the problem into sub-problems, which can be tried to solve one after the other. If the solution is not found, it is necessary to go back to the problem until a way is found to continue to advance.
Finally, a dynamic programming algorithm is used to decompose a complex problem into a collection of simpler sub-problems. All these sub-problems are solved once, and their solution is stored for future use. This avoids having to recompute their solutions.
What are sorting algorithms?
A sorting algorithm allows placing the elements of a list in a certain order. This can be, for example, a numerical or lexicographical order. This organization is often important as a first step to solve more complex problems.
There are many sorting algorithms, with their advantages and disadvantages. Here are some examples:
- Linear sorting algorithms find the smallest element of a list, sorts them, adds them to a new list and deletes them from the original list. This process is repeated until the original list is empty.
- Bubble sorting consists of comparing the first two elements of the list, and inverting them if the first is greater than the second. This process is repeated for each pair of adjacent elements in the list, and until the entire list is sorted.
- Finally, insertion sorting consists of comparing each element in the list with the previous elements until a smaller element is found. The two elements are reversed, and the process is repeated until the entire list is sorted.
How are algorithms used in computer science?
In computer science, algorithms are omnipresent. They are actually the backbone of computing, since an algorithm gives the computer a specific set of instructions. It is these instructions that allow the computer to perform its tasks.
Computer programs themselves are algorithms written in programming languages. Algorithms also play a key role in the operation of social networks, for example. They decide which publications are displayed or which advertisements are offered to the user.
On search engines, algorithms are used to optimize searches, predict what users will type and much more. Similarly, platforms like Netflix, YouTube, Amazon or Spotify rely on algorithms for their recommendation engines.
Why is it important to understand algorithms?
Beyond computer science, algorithmic thinking is crucial in many fields. It is the ability to define clear steps to solve a problem.
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In fact, we use this way of thinking every day and often without even realizing it. In the age of Data Science, Machine Learning and Artificial Intelligence, algorithms are more important than ever and are the fuel of the new industrial revolution…
What are the main Machine Learning algorithms?
Machine Learning algorithms are programs that can learn from data, and improve autonomously without human intervention by using previous experiences.
Among the learning tasks they are able to perform, these algorithms can, for example, learn the hidden structure of unlabeled data, or “instance-based” learning, which consists of producing a category label for a new instance by comparing it to training data stored in memory.
There are three main categories of Machine Learning algorithms: supervised, unsupervised, and semi-supervised. Each of these categories is based on a different learning method.
Supervised learning uses labeled training data to learn the mapping function that transforms the input variables or output variables. After this learning, the algorithm can generate outputs from new inputs.
Among the supervised learning algorithms, we can talk about classification and regression algorithms. Classification is used to predict the outcome of a given sample when the output variable is in the form of categories. The classification model analyzes the input data and attempts to predict labels to classify them.
Regression is used to predict the outcome of a sample when the output variable is in real-value form. From the input data, it will be, for example, to predict a volume, a size or a quantity. Examples of supervised learning algorithms include linear regression, logistic regression, naive Bayesian classification, and the K-nearest neighbor method.
The ensemble method is another type of supervised learning. It consists in combining the predictions of multiple individually weak Machine Learning models to produce a more accurate prediction on a new sample. Examples include decision tree forest techniques, or boosting with XGBoost.
Unsupervised learning models are used when there is only one input variable and no corresponding output variable. They use unlabeled training data to model the underlying structure of the data. Here are three examples of techniques:
- Association is used to discover the probability of concurrency of items in a collection. It is widely used for shopping cart analysis in retail, especially to discover which items are frequently purchased together.
- Clustering is used to group samples so that different items within the same cluster are more similar to each other than to items in another cluster.
- Finally, dimensionality reduction is used to reduce the number of variables within a data set while ensuring that important information is conveyed.
This can be achieved by using feature extraction or feature selection methods. Feature selection involves choosing a subset from the original variables, while extraction performs a transformation of the data to reduce the dimension. Examples of unsupervised algorithms include k-means and PCA.
Reinforcement learning is a third type of Machine Learning. It allows the agent to decide the best action to take based on its current state, by learning which behaviors maximize its rewards.
In general, reinforcement algorithms learn the optimal actions by trying and failing many times in a row. If we take the example of a video game in which the player must go to a specific location to earn points, the algorithm will start by moving randomly and then learn where it should go by trying to maximize its rewards.
How to learn to use algorithms?
Knowledge and mastery of algorithms are essential for working in the field of Computer Science, Data Science or Artificial Intelligence.
To acquire this expertise, you can turn to DataScientest’s training program. Our Data Scientist training will teach you how to handle algorithms, and will give you all the skills to become a Data Scientist.
In addition to algorithms, you will also learn how to manipulate databases and handle Big Data tools, Python programming, and various Machine Learningand Deep Learning techniques.
At the end of the course, you will receive a degree certified by the Sorbonne University and you will be ready to work as a Data Scientist. Among our alumni, 93% found a job immediately after their training.
All of our training courses adopt a Blended Learning approach combining face-to-face and distance learning, and can be done in BootCamp or Continuous training.