Machine Learning

What is Machine Learning?

 

Well, Machine Learning is a concept which allows the machine to learn from examples and experience, and that too without being explicitly programmed. So instead of you writing the code, what you do is you feed data to the generic algorithm, and the algorithm/ machine builds the logic based on the given data.

This blog on What is Machine learning will make your understanding more clear. This blog will tell you about:

Have you ever shopped online? So while checking for a product, did you noticed when it recommends for a product similar to what you are looking for? or did you noticed “the person bought this product also bought this” combination of products. How are they doing this recommendation? This is machine learning.

* “Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”


The above definition encapsulates the ideal objective or ultimate aim of machine learning, as expressed by many researchers in the field. The purpose of this article is to provide a business-minded reader with expert perspective on how machine learning is defined, and how it works. References and related researcher interviews are included at the end of this article for further digging.



Machine Learning Basic Concepts

 

There are many different types of machine learning algorithms, with hundreds published each day, and they’re typically grouped by either learning style (i.e. supervised learning, unsupervised learning, semi-supervised learning) or by similarity in form or function (i.e. classification, regression, decision tree, clustering, deep learning, etc.). Regardless of learning style or function, all combinations of machine learning algorithms consist of the following:

  • Representation (a set of classifiers or the language that a computer understands)
  •  Evaluation (aka objective/scoring function)
  • Optimization (search method; often the highest-scoring classifier, for example; there are both off-the-shelf and custom optimization methods used) 

How machine learning works

 

Machine learning algorithms are often categorized as supervised or unsupervised. Supervised algorithms require a data scientist or data analyst with machine learning skills to provide both input and desired output, in addition to furnishing feedback about the accuracy of predictions during algorithm training.

 Data scientists determine which variables, or features, the model should analyze and use to develop predictions. Once training is complete, the algorithm will apply what was learned to new data.

Unsupervised algorithms do not need to be trained with desired outcome data. Instead, they use an iterative approach called deep learning to review data and arrive at conclusions. Unsupervised learning algorithms -- also called neural networks -- are used for more complex processing tasks than supervised learning systems, including image recognition, speech-to-text and natural language generation.

These neural networks work by combing through millions of examples of training data and automatically identifying often subtle correlations between many variables. Once trained, the algorithm can use its bank of associations to interpret new data. 

These algorithms have only become feasible in the age of big data, as they require massive amounts of training data.

 Evolution of Machines

 

As you know, we are living in the world of humans and machines. The Humans have been evolving and learning from their past experience since millions of years. On the other hand, the era of machines and robots have just begun. You can consider it in a way that currently we are living in the primitive age of machines, while the future of machine is enormous and is beyond our scope of imagination.
In today’s world, these machines or the robots have to be programmed before they start following your instructions. But what if the machine started learning on their own from their experience, work like us, feel like us, do things more accurately than us? These things sound fascinating, Right? Well, just remember this is just the beginning of the new era.

 

Types of machine learning algorithms

 

Just as there are nearly limitless uses of machine learning, there is no shortage of machine learning algorithms. They range from the fairly simple to the highly complex. Here are a few of the most commonly used models:

  • This class of machine learning algorithm involves identifying a correlation -- generally between two variables -- and using that correlation to make predictions about future data points.
  • Decision trees. These models use observations about certain actions and identify an optimal path for arriving at a desired outcome.
  • K-means clustering. This model groups a specified number of data points into a specific number of groupings based on like characteristics.
  • Neural networks. These deep learning models utilize large amounts of training data to identify correlations between many variables to learn to process incoming data in the future.
  • Reinforcement learning. This area of deep learning involves models iterating over many attempts to complete a process. Steps that produce favorable outcomes are rewarded and steps that produce undesired outcomes are penalized until the algorithm learns the optimal process.

 

 



 

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