Knowledge Assimilation of Machines Using Various Approaches
Ebin P M1, Kavitha Nair R2, Pradeeba V3
1Ebin P M, Bachelor’s Degree, Department of Computer Science and Engineering, Kolkata Engineering Hindustan University, Chennai (Tamil Nadu), India.
2Kavitha Nair R, Assistant Professor Department of Computer Science & Engineering, KITS Engineering College, Institute of Technology, (Andhra Pradesh), India
3Pradeeba V, Assistant Professor, Department of Computer Science & Engineering, KITS Engineering College, Institute of Technology, (Andhra Pradesh), India
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 257-260 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3484048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Machine learning (ML), Artificial Intelligence (AI) and Data science are some of the top trending topics today. Machine learning can be seen as a branch of Artificial Intelligence and using machine learning; programs can scan and process huge databases. One of the core objectives of machine learning is to construct algorithms that can learn from the previous data and make predictions on new input data also called an automated learning. Knowledge assimilation of machines can be done through supervised learning, unsupervised learning, semi supervised learning and reinforcement learning. In this article, we present two most widely used supervised learning algorithms for knowledge assimilation. The machines learn things from data, usually known as training data, and apply the knowledge to different circumstances and this learning is a continuous process.
Keyword: Decision Tree, Machine Learning, Supervised Learning, Support Vector Machine.
Scope of the Article: Machine/ Deep Learning with IoT & IoE