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Statistical learning vs machine learning

Statistical learning and machine learning are similar but not identical. Let’s start by defining statistical learning to make the distinction clearer.

Statistical learning and machine learning are complementary because machine learning works with data such as statistics. However, keep in mind that their objectives, processes, and outcomes are all different.

Machine learning Statistical learning
Subfield of Artificial Intelligence Subfield of mathematics
Uses algorithms Uses equations
Requires minimum human effort; is automated Requires a lot of human effort
Can learn from large data sets Deals with smaller data sets
Has strong predictive abilities Gives a best estimate: you gain some insights into one thing, but it’s of little or no help with predictions
Makes predictions Makes inferences
Learns from data and discovers patterns Learns from samples, populations, and hypotheses

The similarity of methods used in statistical modelling and machine learning appears to have led people to believe they are the same thing. This is understandable, but it is simply not correct.

The primary distinction between machine learning and statistics is their intended use. Machine learning models are created with the goal of making the most accurate predictions possible. Statistical models are intended to infer relationships between variables.

 

Artificial intelligence vs machine learning

Artificial intelligence is what machine learning is. Artificial intelligence, on the other hand, is not the same as machine learning. Because machine learning is a subset of artificial intelligence, this is the case. Artificial intelligence includes fields like computer vision, robotics, and expert systems in addition to machine learning.

Artificial intelligence refers to a computer’s ability to think abstractly, analyse things in context, and be creative despite not being intelligent. It’s a machine that can solve problems that would normally be solved by humans using their natural intelligence. Machine learning is a subset of artificial intelligence, which is much broader and more general.

In terms of approach, there are two types of artificial intelligence: weak and strong. Artificial intelligence that isn’t self-aware imitates intelligence. Consider inquiring about the weather on Google. Strong artificial intelligence, on the other hand, completes tasks normally associated with humans and develops self-awareness. There are currently no real-world examples of powerful artificial intelligence. It’s still just a science fiction story.

Machine learning is a broad subset of artificial intelligence. It refers to the process by which a machine learns from experience. It only deals with algorithms that automatically extract patterns from data. The idea behind machine learning is to take a data set and feed it into an algorithm that learns from it, and as a result, the algorithm makes predictions.

 

Because the modern world is data-driven, it is critical to systemize and analyse information that comes from multiple sources. Machine learning is an excellent choice for comprehensively structuring data in order to make evidence-based decisions. The Nihka Technology Group provides a range of products that use machine learning to make informed decisions about your information security and networking risks.
To find out more, visit our website at www.Nihka.co.za

 

The Nihka Technology Group is a South African technology company based in Johannesburg, South Africa. The Group is focused on bringing the digital future to both the private and public sectors, locally and globally by delivering innovative, integrated technologies and intelligent solutions. Nihka offers end-to-end multi-dimensional consulting with an emphasis on integrating the human potential. Bringing EQ into AI.
www.nihka.co.za

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