Artificial Intelligence Programming - Understanding Machine Learning

  • 02 Feb 2024
  • 12 Jul 2024
  • Programming Languages
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Artificial intelligence Programming – Understanding Machine Learning

Introduction

In an age where technology is advancing at an alarming rate, we need to be updated on the latest. In this blog we will discuss about the fundamentals of Machine Learning, a sub-field of Artificial Learning, its applications, how it works and its types. The prime focus of Machine Learning is to build computer systems which learn from data. Machine Learning encompasses a broad range of techniques enabling software applications to better performance over a period of time. Algorithms related to ML are trained to detect patterns in information they utilize old data as an input for foresight, classification, dimensionality reduction and new content generation with minimized human intervention.

Machine Learning permits computers to function autonomously without any explicit programming. Applications related to ML are provided with fresh data and then independently grasp, develop, scale up and adapt. ML gains knowledge of insight from massive volumes of information by algorithm leveraging for pattern identification and adaptation. Although ML is not a concept that is new, dating all the way back to World War 2 when using the Enigma machine, the skill to apply complicated calculations to rising volumes and diversity of information available is only a recent development.

Presently, with rising amount of data and Internet of Things, ML has become a necessity for getting solutions spread across numerous sectors like –

1. Computational Finance

2. Automobile and Aerospace manufacture

3. Voice recognition

4. DNA sequencing and Computational Biology

5. Facial recognition and vision

How it works?

Algorithms of ML are moulded on training datasets for the creation of a model. As fresh input information is fed to the trained algorithm and utilizes the developed model for predictions. Furthermore, prediction is examined for accuracy. On the basis of the accuracy, the algorithm undergoes repeated training with a training dataset and is done till accuracy is obtained.

Types of ML

There are several ways to train ML algorithms, each having its own pros and cons. On the basis of the methods and learning habits, ML is categorized on a broad scale into four primary types –

Supervised

As the name suggests, this kind of Machine Learning involves supervision, here in accordance with labelled datasets machines are trained enabling them to foresee outputs based on that training. The dataset that is labelled specifies input and output parameters which are mapped already. Therefore, the machine being trained with input and its output that’s in correspondence. The output can be foreseen utilizing the tested dataset in a series of phases. The main aim here is to map the variable of the input with the output.

Supervised ML can be further divided into two categories namely classification and regression.

Classification

 These are in reference to algorithms addressing classification issues in which the variable o the output is categorized such as a yes or no, gender, true and false etc. Applications in actuality would be filtering in spam and emails.

Regression

Algorithms of regression manage regression issues whereas both the variables of input and output possess a linear relation. These predict repeated output variables. Weather prediction and trend analysis in the market would be some examples.

Unsupervised

Unsupervised Machine Learning refers to learning techniques that do not require any supervision. Unlabelled datasets are used to enable predictions of output devoid of supervision. Its goal is to classify the dataset which is not sorted based on similar patterns of input. If we take a container with fruits as an example, it will classify according to colour, shape and size.

Unsupervised ML is divided into two sections which are clustering and association.

Clustering

This technique is defined as grouping of objects into clusters which are based on object similarity and difference. Classifying customers by products purchased would be a fine example.

Association

This technique refers to the identification of typical relations between variables of huge datasets. It maps variables in association by determining dependency of different data objects. Market data analysing and web utilization mining are some good examples.

Semi-supervised

This type of learning consists of features of supervised as well as unsupervised ML. It utilizes the mixture of labelled and unlabelled sets of data for training algorithms. 

Reinforced

This type of learning is a process based on feedback. The component of Artificial Intelligence automatically creates a stock of its environment by method of hit and trial, execution, learning from results and performance improvement. There is reward for good actions and penalization for bad ones. Hence, it targets maximization of rewards by executing good actions. It lacks labelled information and agents gain only from experience. It applies to game theories and systems that are multi-tangent in nature.

This kind of learning is divided into two kinds of methods namely positive and negative reinforcement learning.

Positive Reinforcement Learning

This learning refers to the addition of a reinforcing stimulus post certain behaviour, enabling it to repeat. This means that a reward is added post a behaviour.

Negative Reinforcement Learning

This type of reinforcement learning is the strengthening certain behaviour which keeps negative results at bay.

Best Machine Learning Applications

Verticals in industries that manage huge amounts of data realized the importance or Machine Learning. It is being widely adopted in the Healthcare industry. It aids medical practitioners analyse events that are trending or flagged helping in improvement of patient diagnosis and treatment. In the manufacture of newly discovered drugs it speeds up the phases involved. This makes it more affordable and shortens the process. In the sector of finance technology of Machine Learning is used to tackle fraud and obtain essential insights from big volumes of information. Machine Learning is used extensively in the retail sector for item recommendation from user history. In the travel industry it expands scope by managing prices and traffic patterns.

Conclusion

Computers have the ability to learn and memorize generating correct outputs with the help of Machine Learning. ML has enabled organizations to make decisions that are informed and which are critical for business streamlining and operations. ML aids in providing ease to their loads of work with data-driven decisions while searching for new methods. These are a great boon to industry verticals across the globe. As algorithms get more and more intelligent daily, we thereby anticipate an elevation in the trajectory of Machine Learning in the near future.

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