Introduction

Countless companies are either working on machine learning projects or are dreaming of using the technology as quickly as possible. However, over time, many of these ambitious projects don’t deliver the desired outcome. This is often due to the poor quality of the available data that is fed into the algorithms. “Garbage in, garbage out” is an iron law in the field of machine learning. That’s why data scientists are of crucial importance in any machine learning project. They analyze and clean the data, transform it into the desired format with the required quality.

What is Ai (Artificial Intelligence) / ML (Machine Learning)?

Artificial Learning vs Machine Learning vs Deep Learning

Artificial Learning, Machine Learning and Deep Learning Differences

• A.I Human Intelligence exhibited by machines. In short, using machines that can mimic human functions such as learning and problem-solving. A.I. involves machines that behave and think like humans using algorithmic thinking in general.
• Artificial intelligence and machine learning are used interchangeably often but they are not the same. Machine learning is one of the most active areas and a way to achieve AI. Why ML is so good today; for this, there are a couple of reasons like below but not limited to though.
• The explosion of big data
• Hunger for new business and revenue streams in this business shrinking times
• Advancements in machine learning algorithms
• Development of extremely powerful machine with high capacity & faster computing ability
• Storage capacity
• MLaaS is needed for data scientist’s work, architects, and data engineers who have domain expertise. Everyone needs to have a better understanding of the possibilities of Machine Learning.

What Machine Learning can do:

Such as

• Forecast
• Memorize
• Reproduce
• Choose best item

What Machine Learning can’t do:

Such as

• Create something new
• Get smart really fast
• Go beyond their task
• Kill all humans

Traditional Ai vs Modern Ai Approach

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