Machine Learning — An Introduction

Machine Learning — An Introduction

Machine Learning — An Introduction. Gain a solid understanding of Machine Learning, its algorithms, and use cases

At present day, the emerging field of Artificial Intelligence has become the biggest hype for the current generation. AI is a vast ocean in computer science and not only deals with mere things in computer science, but it covers a whole bunch of stuff like Image Processing, NLP, Summarization, Computer Vision, etc. To hail the whole concept of Artificial Intelligence, there has to be strong equipment to strengthen the base ideology of Statistics and Probability for accurate decisions. And here’s where computer scientists coined the term ‘Machine Learning’.


Every understanding starts with a definition. _‘Machine Learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed’. _This definition is brought by Arthur Samuel who first coined the term Machine Learning while working at IBM. It states that Machine Learning is a subfield of computer science that can be used to train or teach a computer to learn itself without being programmed in a precise manner.


The first and foremost important advantage of Machine Learning (ML) is that it doesn’t need to be programmed in a highly efficient manner and they can be taught like how humans teach a four-year-old child. This advantage was loved by computer scientists and data scientists as they were able to manage their work and time efficiently. Another important advantage of ML is that they can improve their skills over time by powerful algorithms.

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