What is Machine Learning (ML)?

Machine Learning

Machine learning is a type of artificial intelligence where a machine can learn from data without being explicitly programmed. The term machine learning was coined by Arthur Samuel in 1959, who used it to refer to the idea that the computer could be programmed using statistical techniques to automatically learn from examples.

Machine learning works by going through the training data and finding patterns in it. For example, if you have a set of photos with different flowers in them, machine learning would try to find that all photos with roses share common features such as shape and colour. Once ML finds these patterns, it can then be told to look for these patterns in new data later on.

This type of AI has been used for things like voice recognition software and content recommendations on the internet. It is also used in image editing software to provide filters for image or video editing projects.

Machine learning is a way to program a machine such that it can perform operations automatically through previous experience or past results. In other words, we simply train an algorithm with a large set of data. Various parameters are organized according to the input values, and outputs are mapped accordingly.

When we feed with real data from the previous optimization and history, it predicts the present values. And the result can be improved by tuning the parameters on the dataset.

We feed a large set of data, these are divided into train data and test data. Our algorithm learns from train data, and we predict the accuracy and precision of our algorithm on Test Data.

pair machine learning

Machine learning is closely related to computational statistics, which makes predictions using computers. It also focuses on mathematical optimization of methods, theory and application domains to machine learning.

Types of Machine Learning

Machine Learning can be broadly classified into three types:

Supervised Learning

Supervised learning instructs models to produce the desired output using a training set. This training dataset contains both right and incorrect outputs, allowing the model to improve over time. The algorithm monitors its accuracy using the loss function and makes adjustments until the error is suitably reduced.

In this, a predefined output is given to the algorithm, such that it distinguishes or maps the values easily. In simple words, outputs are known in the dataset. They too are of two types:

  1. Linear Regression: Here values that are being mapped are in a definite range. Linear regression is a statistical technique that is often used to determine the connection between a dependent variable and one or more independent variables in order to make predictions about future events.

    Simple linear regression is used when there is only one independent variable and one dependent variable. Multiple linear regression is used when the number of independent variables rises. It aims to depict the line of greatest fit for each form of linear regression, which is derived using the least-squares approach. Unlike other regression models, however, when shown on a graph, this line is straight.
  2. Logistic Regression: While linear regression is appropriate when the dependent variable is continuous, logistic regression is appropriate when the dependent variable is categorical, with binary outputs such as “true” and “false” or “yes” and “no.” While both regression models attempt to comprehend correlations between data inputs, logistic regression is mostly employed to address binary classification issues, such as spam detection.

Unsupervised Learning

Unsupervised and supervised machine learning is commonly addressed in the same breath. Unsupervised learning, in contrast to supervised learning, makes use of unlabeled data. It extracts patterns from the data that aid in the resolution of grouping or association difficulties. This is especially beneficial when subject matter experts are unclear about a data set’s common qualities. Hierarchical, k-means, and Gaussian mixture models are all popular clustering techniques.

In this learning type, outputs are unknown, and data is provided to the algorithm. It creates clusters within the dataset and identifies them separately such that each cluster has its own unique characteristics.   

Reinforcement Learning

This type of learning is a combination of both supervised and unsupervised learning and also they are based on a win or loss system, the machine is simply run on values such that if the specific objective is achieved then a reward is given and it learns that otherwise, it try another approach.

For example, a robot is being placed on a table, on one side of it there is water and on the other side, there is fire. When Robot moves toward the fire, no award is given or loss is found hence it learns to avoid it in future and when it moves toward the water, it gets awarded.

Machine Learning has a great scope in future. In real life example, you can see in Gmail (Google App), it automatically marks spam emails and moves them towards trash.

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