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    Machine Learning

    Machine learning is the most important aspect of Artificial Intelligence, it is a subset of Artificial Intelligence. It is a method through which we can feed a lot of data to the machine and make it learn. It is expanding technology which implements computer to learn automatically past data. It uses various Algorithms like building mathematical models and making forecasts using previous data of information. It performs for different tasks like email filtering, Facebook auto-tagging, Image acceptance, speech acceptance, and many more.

    Nowadays, we have to generate an unlimited amount of data. As per research, in the year 2020, 1.5mb of data will be created every second for every person on earth. So the availability of so much data, it is finally possible to build predictive models that can study and analyze complex data to find useful insights and deliver more accurate results. Companies like Amazon and Netflix frame using tons of data to identify any profitable opportunity and avoid any unwanted risk. So the essential thing for artificial intelligence is data for artificial intelligence or machine learning or deep learning.

    Machine Learning

    With this data, we can find a way to analyze, process, and draw useful understanding for this data to grow businesses or find solutions to some problems. Information is the solution. We need to know how to handle the data. And the way to handle data is through machine learning, deep learning, and artificial intelligence.

    Importance of Machine learning

    The following are the reasons for the importance of machine learning.

    • Due to the enormous production of data, we need to find the method used to structure, analyze, and draw useful data insights. It is used to solve problems and find solutions through the most complicated tasks faced by organizations.
    • It is used to improve decision making. So making use of various algorithms, machine learning can be used to make better business decisions. E.g., focus sales, predict any downfalls in the stock market, and identify any risk.
    • It helps uncover patterns and trends in data. Finding hidden pattern data and withdraw critical insights from data is an essential part of machine learning. Building predictive models and using statistical techniques, machine learning allows us to dig below the surface and explore the data at a minute scale. Whereas understanding of data and obtain manually takes a lot of time. But using machine learning, we can perform similar calculations in less than a second.
    • Importance of machine learning is to solve complex problems. Like in the medical field, detecting the genes linked to the deadly ALS (Amyotrophic lateral sclerosis) disease, and building self-drive cars, machine learning can be used to solve most complex problems.

    Difference between Artificial Intelligence and Machine Learning

    Artificial Intelligence and machine learning are the most trending technology now, and they are used to create a smart system. They both correspond to each other.

    Sometimes people use them equivalent to each other both are different in various cases.


    • Artificial Intelligence is used to create intelligent machines to act like human behaviour, whereas Machine learning is used to start to learn from experience programming especially.
    • Artificial Intelligence has two subsets, i.e., Machine learning and Deep learning, whereas machine learning has only one subset, i.e., Deep learning.
    • Artificial Intelligence is a wide range of scope, whereas Machine learning has a limited area.

    Types of machine learning

    There are three types of machine learning:

    1. Supervised learning.
    2. Unsupervised learning.
    3. Reinforcement learning.
    Machine Learning

    Supervised learning:

    It is the primary type of machine learning. We trained the machine using data that is labelled data. It is a process to provide input data as well as correct output data to the Machine learning. We can also say supervised learning is a task-oriented. Supervised means direct an individual activity and make sure that this type of learning machine learns under guidance like in school teachers guide us.

    Same as in supervised learning, machines learn by feeding them label data also gives the correct output data to machine learning.

    Nowadays, it is used in Image classification, fraud detection, spam filtering, Risk Assessment, etc.

    There are two types of supervised learning:

    1. Regression
    2. Classification


    It is used when there is a relation between input and output variable and the prediction of continuous variables like market trends and weather forecasting.

    The following are regression algorithms which come under supervised learning:

    – Regression Trees.

    – Linear Regression.

    – Non-linear regression.

    – Bayesian Linear regression.

    – Polynomial regression.


    It is used when the output variable is categorized like Male-Female, True-False, Yes-No, etc.

    The following are Classification algorithms which come under supervised learning:

    – Decision trees.

    – Logistic Regression.

    – Random Forecast.

    – Support Vector machines.

    Unsupervised learning

    In unsupervised learning, we are going to feed the unlabeled machine data, and the machine has to understand the patterns and discover the outputs on its own. It is used for more complex tasks because we don’t have labelled input data. It involves training by using unlabeled data and allowing the model to act on that information without any guidance. As the name suggests, there is no supervision here. Unsupervised learning cannot directly test regression or classification because we have input data, but there is no corresponding data. So the unsupervised algorithm or model does is it will form two different clusters. One cluster is very similar and the other group which is very different from the first cluster.

    Unsupervised learning Algorithms

    Following are Unsupervised learning Algorithms:

    – K-means clustering.

    – KNN.

    – Anomaly detection.

    – Neural Networks.

    – Principle Component Analysis.

    – Independent Component Analysis.

    – Apriori Algorithms.

    – Singular value decomposition.

    Reinforcement learning

    Learn from Mistakes. Reinforcement learning is quite different when compared to supervised and unsupervised learning. It is a part of machine learning where an agent is established in an environment, and he learns to behave in this environment by performing specific actions and observing the rewards which it gets from those actions. Mainly it is used in advance machine learning areas like self-driving cars and AlphaGo.

    Machine Learning

    Just take an example. Suppose we were dropped off at an isolated island. What do we do? We will all panic. But as time passes, we will how to live on the island. We will explore the environment, and we will understand the climate conditions, the type of food that grows there, the dangers of the island so on.

    This is exactly how reinforcement learning works. It is basically involves an agent, which is we stuck on the island, that is an unknown environment, which is the island, where we must learn by observing and performing actions that result in rewards.

    Supervised learning vs unsupervised learning vs Reinforcement learning

    Supervised Unsupervised Reinforcement
    The machine learns by using labelled data. The machine learns without any supervision. It is using unlabeled data. The agent interacts with the environment by producing actions and discover error or rewards
    It is Labelled data type of data It is Unlabeled data type of data It consist No-pre-defined data
    Regression and classification Association and clustering Reward-based
    Map labelled input to known output Understand pattern and discover output Follow train and error method
    It uses Linear regression, logistic regression, KNN, etc. It uses K-means, C-means, etc. It uses Q-learning, SARSA, etc.


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