Fields
Fields refer to the different areas of study within Data Science and Artificial Intelligence that focus on specific topics or problems. These fields include but are not limited to machine learning, natural language processing, computer vision, data mining, and big data analytics. Machine learning involves the use of algorithms and statistical models to enable computer systems to learn from data and improve their performance on a specific task. Natural language processing focuses on the interaction between computers and human language, enabling machines to understand, interpret, and generate human language. Computer vision involves the use of algorithms to enable machines to interpret and understand visual data from the world around them. Data mining involves the process of discovering patterns and insights from large datasets, while big data analytics involves the use of advanced analytics techniques to analyze and extract insights from large and complex datasets.
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- Peer-to-Peer Networks: Peer-to-Peer (P2P) Networks are decentralized computer networks where each node (or peer) in the network acts as both a client and a server, allowing for direct communication and file sharing between nodes without the need for a central ser ... Read More >>
- Time-division Multiplexing: Time-division multiplexing (TDM) is a method of transmitting and receiving independent signals over a common signal path by means of synchronized switches at each end of the transmission line so that each signal appears on the line only a f ... Read More >>
- Equal Frequency Binning: Equal Frequency Binning is a data preprocessing technique used in data science and machine learning to transform continuous variables into categorical variables. It involves dividing a continuous variable into a set of intervals or bins, wh ... Read More >>
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