We are not so far away from
Machine Learning
Malavika A.S
Software Developer at PiXL.AI

In the last few years, there has been a data revolution that has transformed the way we source, capture, and interact with data. From fortune 500 firms to start-ups, healthcare to fin-tech, machine learning, and data science has become an integral part of everyday operations of most companies. There has been a lot of changes in computing technologies and thus machine learning has got a lot of achievements as compared to past years.

Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "Machine Learning" in 1959. The initial idea of machine learning is born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks. Researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they can independently adopt it more easily. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

Let's talk about some machine learning methods:

There are many ways in which the machine learns. Either Supervised learning, Unsupervised learning or Reinforcement learning. Apart from giving a fixed defining let us explain these methods in our daily life. Everyone uses Facebook or any other social media in our day to day life, simply we can say that our day starts with a cup of coffee and Facebook. From an album of tagged photographs, facebook recognizes your friend in a picture this is a good example of supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we can say that when the machine is learning from labeled data, it is supervised learning. Another example can be nowadays we get recommendations on the songs or videos that you are most interested in. Interesting in a sense is that we get recommendations on new songs based on your past music choices. The model is training a classifier on genres of your song collection This is what Netflix or Pandora do all the time, they collect the songs or movies that you like already, and then evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.

Unlike supervised learning, no teacher is provided that means no training will be given to the machine. Therefore machine is restricted to find the hidden structure in unlabeled data by our-self. The best example is fraud detection from the banking industry. Analyze bank data for suspicious-looking transactions and flag the fraud transactions and it is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of fraud and not fraud. The model tries to identify outliers by looking at anomalous transactions and flags them as fraud.

The third method is reinforcement learning. Using this algorithm, the machine is trained to make specific decisions from feed backs. The machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from experience and tries to capture the best possible knowledge to make accurate business decisions.

Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method can considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.

Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use “pattern recognition” to produce reliable results. Machine learning is starting to reshape how we live, and it’s time we understood what it is and why it matters.

We are not far away from ML

We use the applications of machine learning every day and perhaps have no idea that they are driven by ML. One Virtual Personal Assistants. Google, Siri, Alexa works on these applications. They assist in finding information when asked over voice. For answering, your assistant looks out for the information, recalls your related queries, or sends a command to another phone app to collect info.

We use the service of GPS navigation it is a pure example of traffic prediction with ML. While we do that, our current locations and velocities are being saved at a central server for managing traffic. This data is then used to build a map of current traffic. It also performs a congestion analysis.

We use Uber and Ola services and after getting the ride the app estimates the price of the

ride and we pay the amount. It is a common process. But We don't even know that these are the applications of ML. Jeff Schneider, the engineering lead at Uber ATC reveals in an interview that they use ML to define price surge hours by predicting the rider demand.

The video surveillance system nowadays is powered by AI that makes it possible to detect crime before they happen. They track unusual behaviour of people like standing motionless for a long time, stumbling, or napping on benches, etc. The system can thus give an alert to human attendants, which can ultimately help to avoid mishaps. And when such activities are reported and counted to be true, they help to improve the surveillance services.

When it comes to social media service as it has already mentioned as an example of Supervised learning, Here are a few examples that we had noticed, using, and loving in your social media accounts. Features like "People You May Know", "Face Recognition", "Similar Pins" are wonderful features that are nothing but the applications of ML.

There are several spam filtering approaches that email clients use. To ascertain that these spam filters are continuously updated, they are powered by machine learning. Over 325, 000 malware are detected every day and each piece of code is 90–98% similar to its previous versions. The system security programs that are powered by machine learning understand the coding pattern. Therefore, they detect new malware with a 2–10% variation easily and offer protection against them.

Everyone would have used an Online Customer Support. Several websites nowadays offer the option to chat with customer support representatives while they are navigating within the site. But it does not mean, every website has a live executive to answer our queries. In most of the cases, you talk to a chatbot it is another example that validates the blog topic.

Whenever you execute a search on google search engines it uses machine learning and hence improves the search results. The algorithms at the backend keep a watch at how you respond to the results. If you open the top results and stay on the web page for long, the search engine assumes that the results displayed were following the query. Similarly, if you reach the second or third page of the search results but do not open any of the results, the search engine estimates that the results served did not match requirements. This way, the algorithms working at the backend improve the search results.

If you purchase a product through online shopping a few days back then you keep receiving emails for shopping suggestions related to those products. If not this, then you might have noticed that the shopping website or the app recommends you some items that somehow matches with your taste. They refine or filter the product and thus improves the product search.

Machine learning is proving its potential to make cyberspace a secure place and tracking monetary frauds online is one of its examples.Except for the examples shared above, there are several ways where machine learning has been proving its potential. In short, we are using the different applications of MI without knowing how they perform.

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