The science of making computers to learn, to act like humans, and to improve their learning capacity with time is termed as Machine Learning. This is done by feeding them data in the form of observations and real-world interactions. This will make the machine learn from examples and experience, without being explicitly programmed. Data is just fed to the generic algorithm and based on the given data, the machine builds the logic, instead of writing the code. In simple words, it allows the computer to act without being explicitly programmed. Machine learning applications provide results on the basis of past experience. These algorithms are able to perform necessary tasks by generalizing from experience. They can receive input data and then use it to predict the output.

These algorithms are designed in such a way that they will learn, adapt and improve with time.  ML is the answer to the question, “How are we able to build computers that can automatically improve with experience and time and what fundamental laws govern this process?”. It is an application of Artificial Intelligence (AI).  It enables the machine to do data-driven decisions rather than carrying out a specific task. The main focus is to increase the ability of the computer to develop and access data and make it useful for learning purposes. Machine Learning processes are similar to that of Predictive Modeling and Data Mining, in a way that both require the search through data, to adjust program actions, by looking for patterns. The primary focus is to make the computer learn automatically without any human interference or intervention and adjust accordingly to the scenario. Thus, ML applications have the capability to provide results on the basis of past experience.

The Application Of Machine Learning

When people do shopping on the internet, they are served with ads related to their purchase. This is due to the fact that the recommendation engine uses ML to personalize online ad delivery. Machine Learning has provided us with Self-driving Cars, Speech recognition, Image Recognition, Medical Diagnosis, Prediction Systems, Financial Services, Regression and Extraction, Effective Web Search and improved understanding of the human genome. Human Resource (HR) systems are using learning models to identify the characteristics of employees, to find the best applicants for open positions. Additionally, Virtual Assistant technology is powered by ML. Deep Learning models are combined by smart assistants to interpret natural speech and bring in relevant data, i.e. personal schedule or defined preferences, and action is taken accordingly on behalf of the user. The actions can be booking a flight or driving directions, for example. Even in Self-driving cars, deep learning neural networks determine the optimal actions, by identifying objects, like steering a vehicle safely down the road.

The use of Machine Learning has increased a lot these days, one of the well-known examples is Facebook’s News Feed. It uses ML to personalize the feed of each member. The News Feed will show more of a friend’s activity at the top of the feed if someone frequently reads or likes a particular friend’s posts. The software utilizes statistical and predictive analysis to identify the patterns in the data and then using that data to set the News Feed accordingly.

Conclusion

As a conclusion we can say that Machine Learning has made an incredible breakthrough in the field of AI. It is indeed an amazing and exciting technology, as it gives the computer the ability to learn which makes it more similar and relatable to humans. While Machine Learning has some potentially frightening implications, it is also one of the ways through which technology can be taken to the next level and improve our daily lifestyle.

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