Quick Contact


    Types of Machine Learning

    Machine learning helps organisations by providing useful accurate information regarding their businesses and that lead to more profits and greater accuracy. Figure 3 shows the different types of machine learning:

    Types of Machine Learning
    Supervised Learning:

    It is a type of learning, an algorithm is used to train both input and output labelled data set and with the help training and an algorithm is capable to recognise patterns. For example: Nowadays to unlock a mobile phone, face recognition feature is also present on every mobile phone with the help of it, a mobile phone can be easily unlocked. Like that, a system receives new data, so that it can produce an accurate labelled result (output) by identifying patterns.

  • Unsupervised Learning:

    In this type of learning, an algorithm is used to train both input and output unlabelled data set and an algorithm is not capable to recognise patterns; thus, it senses the data set as data set is not pre-classified and it is a time-consuming method to solve the problems. Usually, it also rearranges the data by grouping it based on similarities. By re-arranging data it may help humans to insights into the data to carry out further analysis. For example, an online shopping website can use this learning algorithm to get a customer’s history based on their past purchased items and to identify and analyse further patterns.

  • Reinforcement Learning:

    This learning also receives unlabelled data just like unsupervised learning, but the main difference is that, in reinforcement learning, an algorithm provides the at least output whether it can be positive or negative feedback. It also learns by interacting with its environment. In starting, it is consist of many mistakes but somehow it continuous with feedback. The aim of this algorithm is how to maximise the reward over some time.

  • AI vs Machine Learning

    Table 1 differentiate between supervised learning and unsupervised learning.

    Artificial Intelligence Machine learning
    AI stands for Artificial intelligence which is capable to learn and apply knowledge ML stands for Machine Learning which is capable to learn itself and apply knowledge
    It focuses to improve success rather than efficiency It focuses to improve efficiency rather than success
    It is like to do smart work like a computer program It works in a simple method like a machine does takes data and learns from data
    It will go to finding the optimal solution It will go for the only solution for that whether it is optimal or not
    It believes in the intelligence It believes in knowledge
    Supervised vs Unsupervised Learning

    Table 2 differentiate between supervised learning and unsupervised learning.

    Supervised machine learning Unsupervised machine learning
    In this, algorithms are trained by using labelled data In this, algorithms are trained by using unlabelled data
    Algorithms are divided into two categories namely classification and regression Algorithms are divided into two categories namely clustering and association
    It is a simpler method It is a quite complex method
    In this, the model prefers taking direct feedback to check whether it is predicting the accurate outcome or not In this, the model does not take any direct feedback
    It required an instructor to train the model It does not require any instructor to train the model
    The desired model gets an Input data along with the output The desired model only gets an input data, no output is provided
    Applications of Machine Learning

    By using Machine learning algorithms, many organisations are using a large amount of data to get better accuracy and efficiency to gain an advantage over their companion. Let’s discuss some of the most common applications are as follows:

    Finance:

    This organisation continuously using a machine learning technique to get useful information from the data and to prevent frauds. For example, banks, use ML to help clients to identify investment opportunities, high-risk profiles, analyse the stock market status and so on.

  • Sales and Marketing:

    Algorithms are being used by marketing industries to improve the relationship between their clients and marketing competitors. It also helps to analyse the client’s data feedback. Based on previous purchasing history some online shopping sites like Amazon and Flipkart provides product recommendations on other shopping sites.

  • Healthcare:

    It is the first industries to implement ML algorithms. It helps the doctors to assess patient’s health in real-time and advice treatment accordingly.

  • Copyright 1999- Ducat Creative, All rights reserved.