Machine learning is one of the best technologies there is and an application of artificial intelligence. It provides the system with the ability to learn and improve automatically from experience without being explicitly programmed.

Machine learning focuses on the development of a computer program that can access data and use it to learn independently. It is a category of algorithms that allows the software application to become more accurate in getting an outcome.

However, even before this technology was globally accepted, many multinational tech companies were using it. It has been through several phases of modification and has become a topic of debate.

This advanced technology offers an opportunity to leverage competition and form new collaborations for entire business models. Therefore, it has garnered a lot of myths and misconceptions.

In this article, we will uncover the most common misconceptions regarding machine learning. This will help you gain in-depth knowledge and clarity on machine learning.

Machine learning and artificial intelligence will replace humans

This is one of the major misconceptions which we have to clarify.

AI and machine learning came into existence to help us do our jobs better with utmost accuracy. It helps you do the critical and complex tasks effortlessly in no time while increasing your productivity.

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But, while both these technologies are exceptional, they still require human intervention in certain tasks like:

  • Long term planning and execution strategy
  • Conceptual and creative thinking
  • Understanding the Pros and cons of following the process
  • Decision making that requires domain knowledge
  • Recommendation of machines based on transparency

Machine learning ignores pre-existing knowledge

Many feel that machine learning is adapted from a blank slate approach of the learning algorithms. This would mean that machine learning only drives knowledge from the algorithms.

However, this is not the case. In reality, all algorithms in machine learning do not have a blank slate and some user data is required to modify the knowledge or redefine the pre-existing body of knowledge.

Artificial intelligence and machine learning are both the same
This is one of the biggest myths and the truth is that AI and machine learning are different but related concepts.

The ultimate goal of both technologies is to solve new and complex situations through data as there is a huge gap between the AI and the human thought process.

In simple words, artificial intelligence takes care of problems that are easy for humans but difficult for machines, whereas machine learning is employed in situations that are easy for machines but complex for humans.

In modern analytics, complex models are not accurate

Most people think that machine learning is used to clutter complexities, and yes, it does that but in the most simplified manner.

Many people tend to believe that a simple infrastructure of machine learning tends to be more accurate, however, that is completely wrong.

A simple infrastructure can come with complex data and complex models can come with the simplest of data. This clears all the misconceptions that modern analytic’s complex models are not accurate.

Machine learning only summarises data

Well, that’s not the case at all. Machine learning consulting services’ main purpose is to predict the future with the existing database. Machine learning algorithms may not be as smart as human thinking, but it sure is a million times faster.

It can help to automate advanced statistical analysis and automatically apply models with the highest certainty.
Machine learning incorporates advanced and predictive data analytics techniques which teaches computers to perform and to do what comes naturally. It is used in analytics to support user data queries with natural language.

Machines learn from experience

It is a common belief that machine learning solutions learn from experience. However, machine learning is an application of artificial intelligence that provides the system with the ability to automatically learn and improve from experience.

It’s purely dependent on data. It requires data to learn from and create algorithms to apply for future situations.

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Take the following for example:

  • A method to classify or represent the components of data.
  • Metrics to evaluate success and score
  • Extract a generalised explanation
  • Optimise the model parameters to the data
  • Analyse transactional data to detect and flag irregularities

This revolutionary technique is going to rule the world with a faster, powerful and scalable solution to clutter all the complexities. It serves a much broader sense and truly is the future in technology advancement.

This advanced technology is an indispensable part of artificial intelligence. It enables automation of repetitive high-volume tasks to analyse organisational data by precise algorithms.

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