Machine learning

Machine learning


Machine learning is an application of artificial intelligence, Field of computer science that uses statistical techniques to provide computer systems, the ability to “learn” with data, without being explicitly programmed.

Simply say, An initial step to achieve the vision artificial intelligence.

In machine learning, we use machine learning algorithms that iteratively learn from data & allows computers to find hidden insights into data.

So simply we can say that Machine learning is a method of data analysis that automates analytical model building“.

 Evolution of machine learning

The name machine learning was coined in 1959 by Arthur Samuel. The machine learning that we are seeing today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks. Researchers interested in artificial intelligence wanted to see if computers could learn from data.

Using computational statistics and machine learning algorithms, the prediction of data is created and to optimize that prediction to an optimum level or higher accuracy, the Mathematical optimization technique is used.

The way the computational power of machines increases. The ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster become more feasible.  This lead to the new development in machine learning.

Widely publicized application of machine learning:

  • The heavily hyped, self-driving car? The essence of machine learning.
  • Online recommendation offers such as those from Amazon and Netflix, Flipkart?Machine learning applications for everyday life.
  • Knowing what customers are saying about you on Twitter(Customer Behaviour)? Machine learning combined with linguistic rule creation.
  • Fraud detection generally use in the banking system? One of the more obvious, important uses in our world today.

 Some machine learning methods

Two of the most widely adopted machine learning methods,

  • Supervised learning
  • Unsupervised learning

Here’s an overview of the most popular types.

  • Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.
  • In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled.           The data given to the unsupervised algorithm are not labeled, which means only the input variables(X) are given with no corresponding output variables. In unsupervised learning, the algorithms are left to themselves to discover interesting structures in the data.                                                                                                                           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 labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data.
  • 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.

Why is machine learning important?

Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.

All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

What’s required to create good machine learning systems?

  • Data preparation capabilities.
  • Algorithms – basic and advanced.
  • Automation and iterative processes.
  • Scalability.
  • Ensemble modeling.

Ham vs Spam Detection model:

Ham vs spam model

Let’s talk about a use case of Machine learning algorithm. Do you ever notice how the HAM VS SPAM detection is happening in your email?

  • Ham  ► good email
  • Spam  ► unnecessary emails

To filter out and detect which mail is ham /spam, we have a given set of data which contains most the emails. A label or tag attached to each message/ mail. Whether it is a spam or ham ?. And now we need to train our model over these emails. And find out which of the email is spam and which one is ham.

Conclusion

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. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

About the author

Vikram singh

Founder of Ai Venture,
An artificial intelligence specialist who teaches developers how to get results with modern AI methods via hands-on tutorials.
GANs are my favorite one.

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