Machine learning is a concept which allows the machine to learn from examples and experiences without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Just to elaborate, a computer programmer instead of writing the code, he or she basically feed data to the generic algorithm and the algorithm/machine builds the logic based on the given data.
The process of learning starts with observation or data, such as examples, direct experience, or instruction, in order to look for the pattern in data and make a better decision in the future based on the examples that we provide. The essential point is to permit the computers to learn automatically without any human mediation or assistance and adjust actions accordingly.
Let’s understand it by referring to our day to day activities. Hope many of you would be using ride-sharing app likes Uber, Ola, Meru etc. so have you ever wondered how do they determine the price of your ride? How do these services optimally match you with other passengers to minimize deviations? Still thinking…no worries…let’s understand it by another example. Hope you all would have done online shopping? So while checking for a product, did you noticed when it recommends for a product similar to what you are looking for? or did you noticed “the person bought this product also bought this” combination of products. How are they doing this recommendation? This is machine learning. Hope it’s simple now to understand ML.
Machine learning can be categorized as Supervised and Unsupervised.
Supervised Algorithms – It requires a data scientist with machine learning abilities to provide both input and desired output in addition to furnishing feedback about the accuracy of predictions during algorithm training. Here, data scientist determine which variables, or features, the model should analyze and use to develop predictions. Once training is complete, the algorithm will apply its learning to new data.
Unsupervised Algorithms – It doesn’t require any human interference. These types of algorithms or systems do not need to be trained with desired outcome or data. It works on deep learning (a process for arriving at a decision or a desired result by repeating rounds of analysis or a cycle of operations) to review data and arrive at conclusions. It is used for more complex processing tasks like image recognition, speed to text and natural language generation. It goes through millions of examples of training data and automatically identifies the correlation between many variables. Once trained, the algorithm uses its bank of associations to translate new data or information. These algorithms have just turned out to be possible in the age of big data as they require huge measures of preparing information.
The Process
Assume that you wanted to buy the best mobile phone available in the market. In real life, you would look for several product reviews indicating qualities of the product that you wanted to buy. If reviews are positive and consist of words like “Good”, “Great Buy”, “Incredible” etc. then your brain will automatically draw a conclusion that product is good and you would probably buy it. Whereas if you come across reviews or comments like "awful," "not great quality," "poor camera or battery," etc. then your brain immediately concludes that it’s not of great quality and you will search for another mobile device. I am sure, you all will agree that reviews help us to perform an action based on patterns of words obtained from product reviews.
Therefore, the relationship among the buyers who bought the mobile phone and wrote product reviews will impact the decision of other buyers, and their product reviews, in turn, will impact future buys. In this way, the pattern will exist across the customers who already purchase the product and future buyers of the product. Machine learning attempts to encode this human decision-making process into the algorithm.
Why machine learning is important and must
There is a rising interest in machine learning, due to the same factors that have made data mining and Bayesian analysis (using the knowledge of prior events to predict future events) more popular than ever. Other factors that contribute to the rising popularity of ML are growing volumes of business transactions, the availability of different data points, economical and powerful computational processing and affordable data storage.
These factors have made it possible to quickly and automatically produce models with the capability to analyze larger and complex data and delivery quick and accurate results on the larger scale. Today many organization are investing and developing this model as it helps them to identify profitable opportunities or avoiding unknown business risks.
Current Perspective
The machine learning is one of the hottest domains for technology-based enterprises. The major giants like Amazon, Google, Microsoft, IBM, and others are racing to sign customers up for platform services that cover the wide range of machine learning activities including data collection, it's interpretation, training, and application deployment. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development.
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