MACHINE LEARNING: The new captivating topic in today’s world

What Is Machine Learning?

Machine Learning is a growing technology that enables computers to learn automatically from past data. ML uses various algorithms for building mathematical models and making predictions using historical data or information. Currently, it is being used for various tasks such as image recognition, speech recognition, email filtering, Facebook auto-tagging, and recommender system.

ML is said as a subset of artificial intelligence that is mainly concerned with the development of algorithms .hich allows a computer to learn from the data and past experiences on their own. The term ML was first introduced by Arthur Samuel in 1959. We can define it in a summarized way “ML enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed programmed”.

With the help of sample historical data, which is known as training data, machine learning algorithms build a mathematical model. Which helps in making predictions or decisions without being explicitly programmed. ML brings computer science and statistics together for creating predictive models. Machine learning constructs or uses the algorithms that learn from historical data. The more we will provide the information, the higher will be the performance. A machine has the ability to learn if it can improve its performance by gaining more data

Machine Learning

How Does Machine Learning Work?

A Machine Learning system learns from historical data, builds the prediction models, and whenever it receives new data, predicts the output for it.

The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately.

Suppose we have a complex problem, where we need to perform some predictions, so instead of writing a code for it, we just need to feed the data to generic algorithms, and with the help of these algorithms, the machine builds the logic as per the data and predict the output. ML has changed our way of thinking about the problem. The below block diagram explains the working of the Machine Learning algorithm

Machine Learning Working

Features Of Machine Learning

  • ML uses data to detect various patterns in a given dataset.
  • It can learn from past data and improve automatically.
  • It is a data-driven technology.
  • Machine learning is much similar to data mining as it also deals with a huge amount of data.

Need For Machine Learning

The importance of machine learning can be easily understood by its uses cases, Currently, machine learning is used in self-driving carscyber fraud detectionface recognition, and friend suggestion by Facebook, etc. Various top companies such as Netflix and Amazon have built machine learning models that are using a vast amount of data to analyze the user interest and recommend products accordingly.

Following are some key points that show the importance of Machine Learning:

  • Rapid increment in the production of data
  • Solving complex problems, which are difficult for a human
  • Decision making in various sectors including finance
  • Finding hidden patterns and extracting useful information from data.

Classification Of Machine Learning

It Can Be Classified Into 3 Parts

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Supervised Learning

Supervised learning is a type of machine learning method in which we provide sample labeled data to the machine learning system in order to train it, and on that basis, it predicts the output.

The system creates a model using labeled data to understand the datasets and learn about each data, once the training and processing are done then we test the model by providing sample data to check whether it is predicting the exact output or not.

The goal of supervised learning is to map input data with the output data. Supervised learning is based on supervision, and it is the same as when a student learns things under the supervision of the teacher. An example of supervised learning is spam filtering. Supervised Learning is Further Classified into two categories of Algorithms.

  • Classification
  • Regression
Classification Vs Regression

Unsupervised Learning

Unsupervised learning is a learning method in which a machine learns without any supervision.

The training is provided to the machine with the set of data that has not been labeled, classified, or categorized, and the algorithm needs to act on that data without any supervision.

The goal of unsupervised learning is to restructure the input data into new features or a group of objects with similar patterns. In unsupervised learning, we don’t have a predetermined result. The machine tries to find useful insights from the huge amount of data. It can be further classifieds into two categories of algorithms.

  • Clustering
  • Association

Reinforcement Learning

Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward for each right action and gets a penalty for each wrong action. The agent learns automatically with this feedback and improves its performance. In reinforcement learning, the agent interacts with the environment and explores it. The goal of an agent is to get the most reward points, and hence, it improves its performance. The robotic dog, which automatically learns the movement of his arms, is an example of Reinforcement learning.

Machine Learning at present:

Now ML has got a great advancement in its research, and it is present everywhere around us, such as self-driving cars, Amazon Alexa, Catboats, recommender systems, and many more.

It includes Supervised, unsupervised, and reinforcement learning with clustering, classification, decision tree, SVM algorithms, etc.

Modern ML models can be used for making various predictions, including weather prediction, disease prediction, stock market analysis, etc.

Machine Learning Classification Algorithm


  • Before learning machine learning, you must have basic knowledge of the followings. You can easily understand the concepts of machine learning:
  • Fundamental knowledge of probability and linear algebra.
  • The ability to code in any computer language, especially in Python language.
  • Knowledge of Calculus, especially derivatives of a single variable and multivariate functions.
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