Introduction to Machine Learning

 Introduction to Machine Learning.

On this article we will cover Introduction to Machine Learning. Machine learning is a sub-section of Artificial Intelligence(AI) whereby machines intergrate algorithms into their systems to copy the way human beings function.

How machine learning functions.

Machine learning involves the use of algorithms which are divided into 3 main parts.

  • Decision making process.

Data(both labelled and unlabelled) is input into the system and by the use of algotiths it is able to predict how the data will behave.

  • Error functioning.

Error functions are used to check whethe the data presented in the system is accurate or inaccurate.

  • Process of optimizing the model.

The weights in the model and the example are checked and regularised to an optimum level until accuracy is attained.

Methods used in machine learning.

  1. Supervised machine learning.
    This method uses labelled data to train algorithms to predict and classify data.Once the labelled data is input into the system,it balances the weights to avoid overfitting and underfitting.The system then optimises the weights until an accurate model is archieved.Some of the methods used include support vector machine,linear regression,logistic regression,random forest etc.
  2. Unsupervised machine learning.
    Unlabled data is input into the system and without the need of a human supervisor,the algorithms predict the patterns and divide them into groups.It is able todifferentiate the context and group the similar ones into one group.It also reduces the number of features and functionalities of a model by using principal component analysis and singular value decomposition methods.
  • Semi-supervised machine learning.

It combines the use of both supervised and unsupervised data labelled and unlabelled assets.It combines a small part of labelled data and a large part of unlabelled assets.

  1. Reinforced machine learning.

This method uses trial and error until an accurate model is attained.It behaves similarly to unsupervised learning only that the algorithm does not use labelled and unlabelled data.So the reinforced learning comes in to decide whether to do a question and give an answer or which pattern to select.

Other machine learning methods include:

  • Automatic Speech Recognition(ASR).

This method uses natural language  processing to convert human language to a written format.A good example of ASR is Siri in iphones which is a voice search used by iphone users to enquire about various things.

  • Customer Service.

They mostly involve chatbots which  answer FAQs(Frequently Asked Questions) or give advice about various topicsThey are usually used in e-commerce websites to offer assistance to human beings.

  • Computer Vision.

They use Artificial Intelligence to convert and distinguish information from images and videos.For instance self driving cars mostly use computer vision to get gps location,monitor traffic,sensors that help evade crowded areas etc.

  • Recommendation engines

They assist customers in checking out during shopping online by intergrating the use of Artificial Intelligence,algorithms and checking the patterns of data.

  • Automated stock trading

They control the stock exchange market even without the presence of human beings.



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