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What is machine learning?

Introduction

Today we are going to discuss what is machine learning and it’s application in near future that will be used in most of the technology.

Machine learning works on the development of computer programs that can access data and use it to automatically learn and improve from experience. It is the scientific study of algorithms and statistical model that computer system use to perform a given task effectively without using any explicit instructions instead it relays on different patterns and inferences.

We can say it is a subset of artificial intelligence, we will discuss later what is AI. Machine Learning algorithms builds a mathematical model of a training data in order to make predictions or decisions without being explicitly programmed to do the given task.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Some machine learning methods

Machine learning algorithms are often categorized as supervised or unsupervised.

  • Supervised machine learning algorithms can apply what has been learned in the past to new data using labelled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values.
  • In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labelled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data.
  • Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labelled and unlabeled data for training – typically a small amount of labelled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy.
  • Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance.

Application of Machine learning

1. Virtual Assistants are integrated to a variety of platforms.

For example:

  • Smart Speakers: Amazon Echo and Google Home
  • Smartphones: Samsung Bixby on Samsung S8
  • Mobile Apps: Google Allo

2. Online Transportation Networks:

When booking a cab, the app estimates the price of the ride. When sharing these services, how do they minimize the detours? The answer is machine learning. Jeff Schneider, the engineering lead at Uber ATC reveals in a an interview that they use ML to define price surge hours by predicting the rider demand. In the entire cycle of the services, ML is playing a major role.

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

Satish Gupta
Done bachelors in Mechanical Engineering in 2018. #LEARNER #BLOGGER

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