Machine Learning from Day-to-Day Life

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Machine learning is the science of geeting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recogintion, effective web search and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.

Machine learning is a type of artificial intelligence that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The processes involved in machine learning are similar to that of data mining and predictive modeling. Both require searching through data to look for patterns and adjusting program actions accordingly.

Machine learning focuses on the development of computer programs that can access data and use it to  learn for themselves. The process of learning begins with observations or data. Some machine learning methods are as follows:

1. Supervised machine learning algorithms
2. Unsupervised machine learning algorithms
3. Sem-supervised machine learning algorithms
4. Reinforcement machine learning algorithms

Here are few Machine learning that we use everyday and perhaps have no idea that they are driven by machine language

1. Virtual personal assistants
2. Predictions while commuting
3. Videos surveillance
4. Social media services
5. Email spam and malware filtering
6. Online customer support
7. Search engine result refining
8. Product recommendations
9. Online fraud detection

Advantages of Machine Learning

• It is used in variety of applications such as banking and financial sector, healthcare, retail, publishing and social media, robot locomotion, game playing, etc.
• It is used by google and facebook to push relevant advertisements based on users past search behaviour.
• It has capabilities to handle multi-dimensional and multi-variety data in dynamic or uncertain environments.
•  It allows time cycle reduction and efficient utilization of resources. 
•  Due to machine learning there are tools available to provide continuous quality improvement in large and complex process environments.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster more accurate results in order to identify profitable opportunities or dangerous risks. It may also require additional time and resources to train it properly.

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