We often hear about Artificial Intelligence, but what is it used for, how does it work and how can we train for it? In this article we answer these questions and explain how Artificial Intelligence is trained at Jedha!
Information and communication technologies have been evolving at an astonishing rate in recent years. A profound transformation of the world is underway and is strongly impacting all key aspects of daily life. Whether it is health, communication, food, production or education, nothing is spared. Similarly, new disciplines are emerging with technological innovations such as artificial intelligence.
It is a booming industry that will undoubtedly continue to expand in the years to come. Given the possibilities offered by this discipline, it is important to prepare properly so as not to miss out on future opportunities. For this, training in this field is mandatory. Jedha develops the concept and explains the training to be done in artificial intelligence.
What is artificial intelligence?
It is a branch of computer science, the aim of which is to enable machines to perfectly simulate human intelligence.
In practical terms, it consists of creating and running programs on computers, to think and act like human beings.
Three elements are essential for its implementation: computer systems, data associated with a management system and advanced artificial intelligence algorithms. Indeed, for a machine to think like a person, it needs to have a large amount of information at its disposal and a high processing capacity.
Importance of artificial intelligence
The amount of data generated by machines and their users is enormous. This huge amount of information cannot be assimilated, processed and used by a human brain to make complex decisions. For this, it is essential to find a way to assign this task to computers.
This is where artificial intelligence comes in.
It aims to teach computers to process information like human beings. The aim here is to give these machines the ability to make complex and relevant decisions in total autonomy.
Known uses of artificial intelligence
The applications of Artificial Intelligence are becoming more and more numerous, without us necessarily realizing it! Any Internet user or smartphone user uses AI every day.
This raises real ethical questions about the use of artificial intelligence.
Link between artificial intelligence and data science
There is a relationship between these two concepts that is sometimes difficult to differentiate.
Data Science: definition
It is a discipline derived from artificial intelligence. It includes several subfields such as statistics, scientific methodologies and data analysis.
Its main role is to enable machines to detect a logic in the mass of information collected and to be able to put it into perspective. This is done through the development and execution of algorithms.
How does Data Science work?
In general, there are several steps to implementing data science. The first step is to collect the required data or set up a storage space to collect it. Next, time must be spent on two time-consuming but essential tasks: data discovery and data cleaning. This provides a solid foundation for the modelling to be implemented.
This step is the central part of the data science project. Through successive iterations, a combination of method and modelling algorithm will be defined. A proven model will finally be deployed.
Training in artificial intelligence
With Jedha, you can train for jobs in artificial intelligence, including that of AI developer! Artificial intelligence training courses are available face-to-face (in Lyon and Paris) or by distance learning. Several courses are available for this purpose.
Data Essentials Training
This is basic training for beginners in artificial intelligence.
Its aim is to give the prerequisites and to offer a good initiation to those who follow it. It is a 50-hour course that includes different modules:
- data visualisation,
- SQL database management,
- an introduction to the Python programming language,
- A/B testing,
- Machine Learning.
The Data Essentials course equips novices in this discipline to learn the business from the ground up. Students will learn how to manage data teams and understand the issues involved in their day-to-day activities. Ultimately, participants will be awarded an RNCP-registered training certificate .
Data Fullstack Training
Intended for intermediate level students, it provides all the necessary skills to manage a Data Science project in total autonomy.
It's a 450-hour training course to become a Data Analyst or Data Scientist. Data Science training focuses on :
- data analysis,
- training in Machine Learning,
- training in Deep Learning,
- management of Big Data infrastructures,
- web scraping and APIs,
- the deployment of Data applications on the Internet.
Participating in this training allows you to develop your project in artificial intelligence. It allows a total immersion in the world of Big Data and the whole process of creating a Data Science project. Once completed, a state-recognised "Data Science Designer-Developer" certificate is awarded to participants.
Data Lead Training
This advanced Data training course is reserved for experts in the field, and provides two distinct skills: Data Scientist and Data Engineer.
By following it, participants will become able to create their own data architecture and manage it.
It lasts 110 hours and covers several themes:
- Scala programming,
- Big Data infrastructure management with Spark Scala,
- SQL and NoSQL databases,
- Data streaming with Kinesis and Firehose,
- ETS with Airflow,
- Unit Testing...
To practice the skills taught during this training in artificial intelligence, 2 projects are added to the curriculum. Eventually, mastery of the Scala programming language will enable the participant to perform complex calculations and analyses on huge amounts of data. A "Data Science Designer-Developer" certificate also rewards this training. Machine Learning and Deep Learning are both sub-fields of AI, find out more here. real difference in Machine Learning and Deep Learning.
Machine Learning: definition and utility
This is a sub-field of artificial intelligence. Still called "machine learning", it consists in letting a machine find recurring patterns in a set of data. This can be images, words, numbers, statistics, etc.
This process allows computers tolearn how to perform a task autonomously, by observing a way of doing it repeatedly.
In this way, the machine becomes progressively more efficient at performing the specific task assigned to it. Once it is adept at detecting the patterns inherent in the data provided, it will also be efficient with the new information added.
In practical terms, a Machine Learning training allows several types of algorithms to be developed, with some being used more than others. They fall into two main variants depending on the data processed: labelled or unlabelled. In addition, there are other programs with special features.
Machine Learning Algorithms for Labelled Data
These are mainly the linear regression, logistic and decision tree algorithms. Their role is to establish links between the processed data.
The linear regression algorithm is used to predict the value of a dependent variable based on the value of an independent variable. In real life, the algorithm is used to predict the number of sales, statistics, the evolution in weight or height, the price of products in the future, etc. It comes in two versions: the simple version and the multiple version. The difference lies in the number of variables used for the prediction. In simple linear regression, only one basic element is considered, whereas in multiple linear modelling at least two are considered.
Logistic regression is used mainly when the dependent data are binary. Its main function is to predict the probability of an event occurring and to identify the relationship between the probabilities of specific outcomes. For example, the algorithm can be used to determine whether a political figure will be elected or not. The logistic regression algorithm also allows for the classification of information into specific categories to facilitate analysis. The categorisation will be done according to the steps of the ETL (Extract, Transform, Load) process.
The decision tree, on the other hand, is used to make recommendations. These will be made according to pre-established decision rules based on the data provided. For example, it can be used to suggest a boxer to bet on during a competition, based on his age and the number of fights he has won.
Algorithms for unlabelled data
To deal with unlabelled data, clustering algorithms should be used instead. This involves identifying groups with similar records and labelling them according to the grouping to which they belong. Several programs are used for this purpose: K-means, TwoStep, Kohonen.
As far as other Machine Learning algorithms are concerned, we find mainly association algorithms and neural networks. The former detect patterns and relationships between data, as well as association rules (if-then relationships). They share many similarities with the principles used in data mining.
Neural networks are algorithms structured as networks with several layers. The first layer is responsible for collecting information, while the last layer presents possible conclusions with probabilities. In between are the hidden layers, where the analysis of the inserted data is done and conclusions are drawn.
There are also deep neural networks which are widely used in Deep Learning. The intermediate layers are more numerous and this allows a deepening of the analyses provided by the higher layers. Indeed, the neurons at a lower level refine the result proposed by those at a higher level.
Deep Learning: definition and fields of intervention
This is an advanced version of Machine Learning and is currently the most widely used.
It uses deep neural networks to detect the patterns present and especially the more subtle ones.
This is what makes it more powerful than Machine Learning.
It should be noted that deep neural networks consist of tens or hundreds of layers of interconnected nodes. These systems work in synergy to process information and make more accurate predictions. The way they work is directly inspired by the human brain. By analogy, the computational nodes can be likened to neurons and the network corresponds to the brain organ itself.
Deep learning is used in several real-life situations in our era. These include :
- autonomous cars,
- machine translation,
- medical diagnoses,
- malware and fraud detection,
- intelligent robots,
- space exploration,
- automatic moderation of social networks...
Many other fields are keen on the use of Deep Learning, and the Deep Learning training The use of Deep Learning is also a way to meet pressing recruitment needs.
NLP: definition and usefulness
Known as Natural Language Processing, NLP is an offshoot of artificial intelligence. Its main mission is to give machines the ability to understand human language in its written or spoken form. The difficulty lies in the significant difference between machine language and user language. In the case of machines, language must be structured according to specific, precise codes, so that instructions can be understood easily and unambiguously. Human expression, on the other hand, is often very imprecise and can quickly lead to confusion.
To solve this problem, specific algorithms are created to analyse the meaning of words used by humans. They are used to rid human communication of ambiguity, recognise certain references and produce machine language.
The fields of application of NLP are numerous. It is used on a daily basis in voice assistance programs, which are able to respond to instructions by listening to speech alone. NLP is also used for the automatic translation of entire texts, without human intervention. This is the underlying function of Google Translator. NLP is also regularly used in marketing. By analysing the keywords entered by users, Google or Bing's algorithms are able to present Internet users with appropriate advertisements and content.
Artificial intelligence is a very broad field with a wide range of applications. It is a revolution in the making, which will lead to the creation of new professional needs to be satisfied. Indeed, data manipulation and analysis are skills that will be essential to be an IT pioneer in the future. They are required to explore each of the sub-disciplines of artificial intelligence. This is why it is important to complete training in Data Science, Machine Learning, Deep Learning and Natural Language Processing. By mastering these areas, training in artificial intelligence will be made easier.