- Python: Data Science is all about programming. And Python is one of the most well-known languages of the world. Why? The reasons are simple.
- Ease of computing: Python is an easy language to master. It hardly has any punctuation and works solely on codes and indentation. This makes Pythona universal favorite.
- Powerful language: Python is a powerful language as it can be used to generate many different types of software. It is one of the best languages available under object-oriented programming. Its power lies in the generation of complex software that is now used everywhere, all over the world.
- Machine Learning:Machine learning is a fundamental concept of Data Science. It is safe to say that Data Science is driven by the concepts of Machine Learning. For a computer to learn the science behind processing data, it has to go through the process of machine learning. Possessing the required knowledge for Machine Learning is an important step in becoming a Data Scientist. It is only natural for a person to acquire this skill in such a scenario. Predictive analysis is yet again an important part of machine learning that you will be required to master.
- Neural Networks:Neural networks in Artificial Intelligencerefers to the working of neurons in a human brain. The reason why this is one of the most important parts of learning about AI, is the simulation of the neuron patterns to form a cognitive learning system. Neurons in our brains are designed to retrieve memory from the slightest of hints produced. This is an important capability that all Artificial Intelligence-driven machines are required to have. The neurons are also responsible for all other activities that make humans the most intelligent on the planet. In that light, it is important to say that a neural network can be primarily of three types:
- Feed forward Neural Network:This is the type of neural network that is used most commonly today. In Artificial Intelligence, it is used to perform some basic operations like basic pattern recognition and image recognition. Feed forward neural network is so-called as it facilitates the flow of information in one direction only. This genre of a neural network can further be divided into single-layered networks and multi-layered networks.
- Recurrent Neural Network:Recurrent neural networks, abbreviated as RNN, make use of loops to perform some recurrent operations. Quite naturally, these are way more complicated than feed-forward neural networks and can do much more complex tasks related to image processing. In fact, recurrent neural networks are used in natural language processing and speech recognition operations.
- Convolutional neural networks: This genre of neural networks is quite complicated and was developed with visual recognition in mind. Today, it is used for operations like object recognition and analysis of videos.
- Deep Learning: One of the most important components of the whole Artificial Intelligence package is Deep Learning. Now, we spoke about the usage of neural networks in the previous point and that is clear to us given its content. However, there is more to it than meets the eye. When we say ‘deep learning’, we mean the cognitive ability of a machine to imitate the way humans learn and remember things. One very important aspect of Artificial Intelligence is making some important decisions and that is facilitated by a complex structure of neurons. The study and subsequent imitation of this process are essentially what we mean by deep learning.
- Natural Language Processing:Natural Language Processing is a sub field of Artificial Intelligence and is essentially concerned with the processing of natural languages. It deals with the interaction between humans and computers using natural languages. This requires the computer or the software to understand the respective languages and to know what to say after what and how exactly to respond. Given that Artificial Intelligence is a lot about interacting with humans, Natural Language Processing is a crucial part of designing an interface that is to understand the natural language of humans
- SQL:SQL is the abbreviation of Structured Query Language and is used to manipulate information in a database management system. Although the topic does not sound like one that has something to do with artificial intelligence, it actually is more than helpful to the eye. Databases are required to store the information that will be fed to the machine, which in turn will serve as the data that the machine will use to learn various things. Hence, it is important to know the usage of databases, and hence, of SQL.
- NOSQL(Not only SQL):NOSQL is mostly a simpler, or a more refined version of SQL. It deals with the retrieval of information from a database that does not have relational schemas. As for SQL, relational schemas formed a crucial part of the designing system. However, in NOSQL, it is completely the opposite. It is used mainly for the databases where relational tables are not necessary. Therefore, a small amount of data works just fine on NOSQL. Needless to say, using NOSQL in such situations is way better than using SQL as the latter is more complicated than the former.
- Hadoop:Hadoop is a collection of open-source software that was introduced by Apache. This software was introduced to take care of a large volume of data which can otherwise be difficult to handle. Wondering why Hadoop is essential for Artificial Intelligence? Let us explain how! With Hadoop, feeding data to your artificial intelligence-driven device becomes a thousand times easier than it would have normally been. Moreover, the device will also be able to save further information using this software. However, as a designer, you must know how to operate Hadoop so that you can program your machine accordingly.
- Elastic Search:Developed using the language Java, Elastic search is a search engine that is based essentially on the Luce library. This, too, plays an important role when it comes to Artificial Intelligence.
- Visualization of Data:Data visualization is a topic that is being widely explored by computer scientists. Artificial Intelligence is not only being driven by this technology, but it is also being used to develop the same. Through data visualization, it is possible to translate a large volume of data into images that are easier to decipher and use. This allows easier communication between a machine and the user as visualization increases readability.
- Database Management Systems
- Health-care analytics
What is python?
Python is an interpreted, high-level and general-purpose programming language. Python’s design philosophy emphasizes code readability with its notable use of significant indentation.
What are the uses of Python?
What is cloud computing?
Cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale.
Cloud computing is very important as it allows people access to the same kinds of applications through the internet. … This means the device accessing the cloud doesn’t need to work as hard. By hosting software, platforms, and databases remotely, the cloud servers free up the memory and computing power of individual computers.
Benefits of cloud computing :
Written by U Roy, DOCC kolkata
“The right way” and “the wrong way” aren’t as cut and dry as with back-end development.In back-end development, if what you are expecting to happen happens, you’ve succeeded. Surely they are different ways to get there, some better than others. But in front-end development, the paths to completing a task seem endless. Even if you’ve seemingly succeeded, it can feel like just a matter of time until a bug is found in how you’ve done it.
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- Efficiency: The usage of the pre-created functions and templates allows to carry out the projects more effectively. The developers have to write less code, which results in delivering projects meeting higher standards and more quickly.