MACHINE LEARNING


Overview

Machine Learning (ML) is a field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do. Its key focus is to allow computer systems to learn from experience without any human intervention. Machine learning is not a new technology. Most of the learning algorithms, such as neural networks, are based on decades-old research. Its growth is tied to developments in three important areas:

1.Availability of data: Billions of people are online with billions of connected devices or sensors. It generates a large amount of data which when combined with decreasing costs of data storage, is easily available for use. Machine learning uses this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.

2.Computation power: Extremely powerful computers and the ability to connect remote processing power through the Internet makes it possible for machine-learning techniques to process enormous amounts of data.

3. Innovations of the algorithm: The techniques of machine learning, specifically in layered neural networks – also known as “deep learning” – have given a boost to new services.

Scope of Machine Learning:

Machine learning technology is highly in demand. Both start-ups and large companies want to hire professionals in machine learning. If you are good at designing and implementing machinelearning algorithms, you can be a machine-learning engineer. You can be a data scientist, analyse, and interpret large chunks of data. If you have a strong background in statistics, economics, and calculus, you can also be a data analyst. You can also be a data architect and take care company’s big data ecosystem.

Applications of Machine Learning:

Although machine learning evokes thoughts of science fiction, it already has many uses today, for example:
1. Spam email filtering: Email services use machine learning to filter incoming emails. Users can train their spam filters by marking emails as “spam”.
2. Personalized content: Online services use machine learning to personalize the user’s experience. Services, like Flipkart or Netflix, “learn” from your previous purchases and the purchases of other users in order to recommend relevant content for you.
3. Fraud detection: Banks use machine learning to check for strange activity on your account. Any unusual activity, such as foreign transactions, could be flagged by the algorithm.
4. Speech recognition: Applications use natural language processing to optimize speech recognition functions. Microsoft’s “Cortana”, Amazon’s “Alexa” or Apple’s “Siri” are examples of some intelligent personal assistants.

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