Top datasets to actualize machine learning and data training tutorial

Yes, due to many obvious reasons, Bill Gates is right and we will prove it in this blog.

Though the term, machine learning was tossed by Arthur Samuel in 1959 while working at the IBM, the actual serviceability of it started popping up after 2010. So, Dave Waters compares the advancement of machine learning with the baby – “A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning.”

Recently, machine learning market has witnessed exceptional growth and it is estimated to reach $21 billion by 2024. The reason why every expert is quoting machine learning as the future of all businesses is because of its versatility. From the mining industry to supply chain, marketing, recruitment, mobile app development, aviation, film making, machine learning is streamlining the sales -cycle and letting business owners derive many financial benefits.

However, developing applications which run on the machine learning technology is not as easy as pie. Data scientists have to decode many challenges to actualize machine learning. One of the biggest challenges is finding suitable dataset. Because, without finding a suitable dataset and training machine learning algorithm on it, machine learning can never come to life.

Thus looking at the urgency, I will talk about everything about datasets and how to actually use datasets, in this blog. I will also share a tutorial showing how to train a machine learning algorithm using a dataset. (For that, I will train Genetic Algorithm on KDD dataset.)

What is a dataset?

To understand datasets more rationally, we have to understand the working method of machine learning including the concept of ‘training data’

Machine learning is the technology in which algorithms perform specific tasks without human command. The one major task machine learning algorithms can easily satisfy is predication. To do predication, algorithms make rules and to make rules, algorithms rely on big data. Still confused?

Consider the brain of a football player. Throughout his career, his brain has collected many data related to leg movements of goal-keepers and their next action based on their leg movements. Using this data, his brain makes so many rules; I.e if goalkeeper moves his leg to left, he will jump to the right. These rules are being executed in real-time while he is playing football; his brain predicts the next action of goal-keepers based on his leg movement, using the rules his brain has made ‘using’ collected data. Meaning, without the data (datasets), his brain can never ever make rules which do predication.

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Full Text： BDAN

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