Machine-Learning

Introduction

Machine learning, a sub-field of Artificial Intelligence. ML is the field of study which gives a model the capability to learn without being explicitly programmed. ML uses the past Output to generate a program to generate futher prediction. This field is concerned with Aritificial Intelligence ( to make machine to think like human ), to be able to perform an algorithm with much better result and accurate prediction. Like, in youtube , youtube history is used to predict the watching content of the user and later suggesting recommended videos related respectively.

Machine Learning has many accurate models dependent on different accuracy and data consistency. The accuracy is most important, which indicates the correctness of the model for the real life problems and to get more feedback to improve.

                          INPUT           +-----------------+
           Data      ---------------->    |   Traditional   |     ----------------> Output
           Program   ---------------->    |   Programming   |
                                          +-----------------+
                         
                          
                          INPUT           +-----------------+
           Data      ---------------->    |   Machine       |     ----------------> Program
           Output    ---------------->    |   Learning      |
                                          +-----------------+

Types of Machine Learning:

1) Supervised Learning
2) Unsupervised Learning
3) Reinforcement Learning
4) Semi-Supervised Learning

1) Supervised Machine Learning

-> Supervised Learning is the learning in which labelled or continuous data is use to predict by the help of the historical data. This is use to predict the algorithm to interpret the result.

Applications: Risk Evaluation, Forecast Sales

2) Unsupervised Machine Learning

-> Unsupervised Learning is the learning in which unlabelled or discrete data is given to machine for prediction. In this, Machine itself analyze a pattern or symmetry in data and produce such model to predict the results.

Applications: Recommendation System, Anomaly Detection

3) Reinforcement Learning

-> Reinforcement Learning is the learning which is independent of taking any data. In this type of learning machine itself learn to generate the algorithms through interacting with the environment. It grows rapidly and generate algorithms.

Applications: Self Driving Cars, Gaming, Healthcare

4) Semi-Supervised Machine Learning

-> Semi-Supervised Learning is the learning which is combination of the supervised and unsupervised machine learning algorithm. Here, a small amount of labelled data with large amount of unlabelled data is train to generate the algorithm. The algorithm would generate the respective predictions.

Applications: Text classification, Image classification

Resources I got really interesting!

• Supervised vs Unsupervised vs Reinforcement
• Blog on type of Machine Learning by Renu Khandelwal
• Learn with GeeksForGeeks
  
  

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