What is Data Science?
Data Science is the extraction of relevant information from the data. It uses various techniques from many fields, such as mathematics, machine learning, computer programming, statistical modeling, engineering and data visualization, recognition and learning of patterns, modeling of uncertainties, data storage and cloud computing. Data science does not necessarily imply big data, but the fact that the data is expanding makes large data an important aspect of data science.
Data science is the most used technique among AI, ML and itself. Data science professionals are usually experts in mathematics, statistics and programming. Data scientists solve complex data problems to show information and correlation relevant to a business.
What is Artificial Intelligence (AI)?
Artificial intelligence refers to the simulation of a function of the human brain through machines. The primary human functions performed by an AI machine include logical reasoning, learning and self-correction. Artificial intelligence is a broad field with many applications, but it is also one of the most complicated technologies to work with. Machines are not intrinsically intelligent and, to be so, we need a lot of computing power and data to be able to simulate human thinking.
Artificial intelligence is classified into two parts, the general AI and the narrow AI. General AI refers to making machines intelligent in a wide range of activities that involve thinking and reasoning. For example, general AI would mean an algorithm that is capable of playing all kinds of table games, while limited AI will limit the range of capabilities of the machine to a specific game such as chess or scrabble. Currently, only narrow AI is available to developers and researchers. General AI is only a dream of researchers and of perception among the masses that will take a long time for the human race to achieve it
What is Machine Learning?
Machine Learning is the ability of a computer system to learn from the environment and improve its experience without the need for explicit programming. Machine learning focuses on allowing algorithms to learn from the data provided, collect ideas and make predictions about data that was not previously analyzed using the information collected. Machine Learning can be done using multiple approaches. The three basic models of machine learning are supervised, not supervised and reinforced.
In the case of supervised learning, the tagged data is used to help machines recognize features and use them for future data. For example, if you want to classify images of cats and dogs, you can enter the data of some tagged images and the machine will sort all the remaining images for you. On the other hand, in unsupervised learning, we simply place the data without labeling and let the machine understand the characteristics and classify it. The automatic reinforcement learning algorithms interact with the environment to produce actions and then analyze errors or rewards. For example, to understand a chess game, an ML algorithm will not analyze individual movements, but will study the game as a whole.
The difference between AI, ML and Data Science :
Artificial Intelligence is a very broad term with applications ranging from robotics to text analysis. It remains an evolving technology and machine learning is a subset of artificial intelligence that focuses on a limited range of activities. In fact, it is the only real artificial intelligence with some applications in real-world problems.
Data Science is not exactly a subset of Machine Learning, but it uses ML to analyze data and make predictions about the future. It combines machine learning with other disciplines such as big data analysis and cloud computing.
At NewGenApps, we focus on the development of new age solutions that take advantage of these technologies and help solve real business problems. If you are looking for a company that can make sense of your data and provide important information for your company, do not hesitate to contact us.