Machine Learning- It’s Algorithms and Advantages

Machine Learning – It’s Algorithms and Advantages

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Today, the world is moving at a rapid pace driven mainly by changing technologies. These changes have made our life simpler and faster. In this article, we will understand how Machine Learning is making a significant impact on our life. 

Table Of Contents
  1. What is Machine Learning (ML)?
  2. Features of Machine Learning
  3. Machine Learning Algorithms
  4. Advantages of Machine Learning 

What is Machine Learning (ML)?

ML is a scientific study of algorithms and models of data. These algorithms and models perform a particular task themselves. Such tasks complete without giving explicit instructions to a computer system. In this process, patterns, continuous teaching, and interfaces enable them to make decisions using data. It is the branch of computer science. In short, it deals with the development of computer programs that teach and perform themselves.  

Features of Machine Learning

  • Uses data to detect patterns in a data set and adjusts program actions accordingly
  • Focuses on the development of programs. Such programs can teach themselves and can grow and change when exposed to new data
  • It enables computers to find out hidden insights. These Hidden insights are explored using iterative algorithms and without being explicitly programmed.
  • Data analysis methods that automate analytical model building.

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Machine Learning Algorithms

MLalgo Machine Learning

ML uses certain types of algorithms, which creates a natural pattern in its memory. Broadly, it has been classified into three categories-

Supervised Learning 

As the name suggests, in supervised learning, the machine is trained under supervision by using data, which is well “labeled.” It means that some data is already tagged with the correct answer. In the beginning, the machine gets trained with the input data as well as the output data. Hence, appropriate rules map input and output. Once the machine gets trained, it is provided with a new set of data. That way, the supervised learning algorithm analyzes the given data based on the training provided and produces a correct outcome.

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Supervised learning gets further classified into two groups-

Classification

In this case, the output variable is a category. For example, “dark” or “bright” or “pass” and “fail.”

Regression

In this case, the output variable is a real value. For example, “height” or “width.”

Un-Supervised Learning

In this technique, the machine is trained with unlabeled data. The machine works on its own and discovers hidden structures and information. It mainly deals with unlabeled and unstructured data. 

Unsupervised machine learning output can be applied in supervised machine learning techniques. The input data here may help with better-reproduced outputs. Unsupervised learning helps to find structures that can be useful for categorization. 

Some of the applications of unsupervised learning are-

  • Clustering
  • Anomaly detection
  • Association mining
  • Latent variable models

Reinforcement Learning

In the reinforcement learning algorithm, the machine gets trained through a system of reward and punishment. In this technique, the machine receives rewards for correct output and penalties for giving incorrect output. That way, the machine learns without human intervention by maximizing its reward and minimizing its penalty.

Advantages of Machine Learning 

ML has evolved to solve the real problems of human life. It automates the processes used in multiple industries like banking, retail, healthcare, defense, telecom, etc. At present, machine learning has become one of the extensively used technology. 

Across sectors

Multiple industries use ML applications. This includes the financial sector, healthcare, retail, social media, e-commerce, robots, automation, gaming applications, etc.

In advertising

In our daily life, we use social media, where ML plays a prominent role. We often observe that Facebook and Google show relevant advertisements based on their previous search activity. This happens due to the machine learning algorithm. Similarly, the suggested videos on YouTube are also an example of an ML application. No doubt, machine-learning technology provides better results.

Multi-variety and multi-dimensional data

ML is capable of handling multi-variety and multi-dimensional data in a dynamic or unfavorable environment. Hence, it helps in the reduction of the time cycle and proper utilization of the resource.

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Quality improvement in a long and complicated process

ML technology is capable of creating tools that help in continuous quality improvement in a long and complicated process.

Leveraged in enhance customer experience and business performance

This includes dynamic pricing, chatbots, a 360-degree view of the customer, recommendations, customer sentiment analysis, etc.

Conclusion

Machine Learning is instrumental in connecting computer systems with human life. It helps in solving real-world problems and automating processes. ML evolution has played a significant role in enhancing and developing Machine intelligence as future intelligence. Due to these reasons, it is ‘the technology’ for mainstream adoption in the years to come. 

Find the best books here to learn Machine leaning.

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