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Machine Learning: What it is and Why it Matters

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Machine Learning has begun to reshape how we live, so we need to understand what Machine Learning is and know why it matters. A good start at a Machine Learning definition is that it is a core sub-area of Artificial Intelligence (AI). ML applications learn from experience (well data) like humans without direct programming. When exposed to new data, these applications learn, grow, change, and develop by themselves. In other words, with Machine Learning, computers find insightful information without being told where to look.


Machine Learning Algorithms: 4 Types You Should Know

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Machine Learning came a long way from a science fiction fancy to a reliable and diverse business tool that amplifies multiple elements of the business operation. Its influence on business performance may be so significant that the implementation of machine learning algorithms is required to maintain competitiveness in many fields and industries. The implementation of machine learning into business operations is a strategic step and requires a lot of resources. Therefore, it's important to understand what do you want the ML to do for your particular business and what kind of perks different types of ML algorithms bring to the table. In this article, we'll cover the major types of machine learning algorithms, explain the purpose of each of them, and see what the benefits are.


All about Machine Learning

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In the previous article, we studied Artificial Intelligence, its functions, and its python implementations. In this article, we will be studying Machine Learning. One thing that I believe is that if we are able to correlate anything with us or our life, there are greater chances of understanding the concept. So I will try to explain everything by relating it to humans.


A Beginner's Guide to Machine Learning: What Aspiring Data Scientists Should Know - DZone AI

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Before choosing a machine learning algorithm, it's important to know their characteristics to generate desired outputs and build smart systems. Data science is growing super fast. As the demand for AI-enabled solutions is increasing, delivering smarter systems for industries has become essential. And the correctness and efficiency through machine learning operations must be fulfilled to ensure the developed solutions complete all demands. Hence, applying machine learning algorithms on the given dataset to produce righteous results and train the intelligent system is one of the most essential steps from the entire process.


Allowing Computers to learn from Data : Gold of 21st Century

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As a Data scientist, I think that machine learning, the application and science of algorithms that can make sense of data, are the most exciting of all the fields within computer science. As a society, we are living in a time when data is abundant and we can turn this data into knowledge by using self-learning algorithms from the field of machine learning. The plethora of powerful open source algorithms that have been developed in recent years has made now our best time ever to learn more about machine learning and learn how to use them to spot patterns in data and anticipate future events was probably not the case before many useful open source libraries were created. Despite the fact that we live in an age of modern technology, there is one resource that is plenty available to anyone: a large amount of structured and unstructured data. A subfield of artificial intelligence that evolved in the second half of the 20th century was machine learning.