Machine learning is a subset of Artificial Intelligence (AI), The ability to learn and read automatically. Machine learning systems can Learn and improve his experience and perform a particular task without using certain commands.
Machine learning focuses on the development of software programs that can access and use the data to learn themselves.
The Main aim of Machine Learning (ML) is to allow the computers to learn itself without human interference or Help.
Machine learning programs are constructed a mathematical model based on sample data Know as Training Data, the process to make Guessing or decision Making without being Specific programming instructions, to perform the particular task. Machine learning (ML) is used in different application such as Electronic Mail Filtering and Computer Vision etc.
Table of Contents
a) Different Types of Machine Learning Algorithms:
Machine Learning algorithms are classified into differing kinds.
1. Supervised Learning:
Supervised learning mathematical model constructs a set of data that contains both the inputs and the desired outputs. Such a set of data is called as training data and consists of a set of Training Examples. Each learning examples have one or more inputs and the desired outputs, additionally known as a supervisory signal.
2. Unsupervised Learning:
Unsupervised studying algorithms take a fixed of statistics that contains best inputs, and discover structure within the statistics, like grouping or clustering of information points. The algorithms consequently research from taking a look at data that has no longer been categorized, classified or labelled. Instead of responding to remarks, unsupervised studying algorithms perceive commonalities within the information and react primarily based on the presence or absence of such commonalities in every new piece of records.
3. Reinforcement Learning:
Reinforcement learning is one of three essential machine learning algorithm models, equal to supervised learning and unsupervised learning.
Reinforcement learning is a specific region of machine learning, involved with how software program assistants must take actions in a domain to magnify some idea of accumulative benefits. Because of its planned declaration, The region is constructed in several other control systems, like the game, control, information theories, and some operations research, statistics and genetic programming (algorithms). The environment is generally described as a Markov Decision Process (MDP).
Most of the reinforcement learning algorithms get the benefits of dynamic programming techniques. Reinforcement learning Programs do not accept the exact knowledge of the mathematical model of the Markov Decision Process (MDP), and these programs are used when exact models are impossible. Reinforcement learning algorithms are used in self-driving independent vehicles and in figuring out how to play a game against a human opponent.
4. Feature Learning or Representation Learning:
Feature learning is one of the Machine Learning algorithm model, feature learning also called representation learning.
Feature learning algorithms contains a lot of techniques That Allows a network to naturally appear in the representations needed for feature recognition for fresh Data. This will replace the manual feature engineering that means create features that make Machine Learning with Domain Knowledge of data, It Allows the machine to learn the features, by using this features to perform a particular task.
The feature learning algorithm may be either supervised or unsupervised algorithm.
In supervised representation learning algorithms, options are learned using labelled information. some example are supervised neural networks, multilayer perceptron and (supervised) dictionary learning.
In unsupervised representation learning algorithms, options are learned using with the unlabeled input information. some examples are dictionary learning, independent component analysis.
5. Sparse Dictionary Learning:
The sparse dictionary learning algorithm is one of the feature learning models, its main goal is to identify the representation of the input data, also called as sporadic programming, It is in the form of straight combination or mixes up with primary atoms. These atoms produce in the form of a dictionary. and it is expected to be a sparse array. The model is strongly NP-completeness and tough to resolve. An important heuristic technique model for sparse dictionary learning algorithm is the K-SVD algorithm. The sparse dictionary learning algorithm is used in image de-noising.
Some example applications of the sparse dictionary learning algorithm is in the area of compressed sensing or signal recovery.
6. Association Rule Learning:
Association rule learning algorithm is a rule-based machine learning (RBML) Model for finding the correlation between variables in huge databases, the main purpose is to find powerful rules to search in the databases with the help of some measure of interestingness.
Rule-based Machine Learning is a basic term for any Machine learning models for identifying, learning, and evolving the rules to store. the defining the features of a rule-based Machine Learning Algorithm is to finding and using the set of relational rules that represents the knowledge recorded by the system. this method is different from other machine learning algorithms. Rule-based machine learning algorithm way is to learning classifier systems, association rule learning, and artificial immune systems.
b) Most Important Applications of Machine Learning in Daily Life:
1. Intelligent Personal Assistant (IPA):
Intelligent personal assistant (IPA) Specially designed for particular Task, examples for Personal Assistants such as Apple’s Siri in iPhone, Microsoft’s Cortana, Google Voice Assistant, with the help of these intelligent personal assistants you can find out anything you want. Voice recognition is the most widely used application of Artificial Intelligence in the market today.
Traffic: with the help of machine learning technology models to find out the zones where the Traffic is jam in congested areas.
Online Transportation Systems:-
Uber uses machine learning (ML) to define the price system in rush hours by estimate the rider demands. It is used in transportation in different ways.
3. Security Surveillance Camera Monitoring:
Its a difficult job to monitoring the multiple security camera surveillance data for a person (human), with the help of Artificial Intelligence (AI) you can be done this job easily and fast.
Nowadays days security is more important and the number of security surveillance cameras are fixed in congested areas. A single person can’t monitor multiple video surveillance, by using machine learning technology you can solve this problem.
4. Machine learning (ML) in Social Media Services:
Pinterest pins work on the bases of computer vision, with the help of finding and High-level understanding of digital images and videos. computer vision performs a particular task automatically what the human visual system can do. by using this technique takes the information of the images and videos, Pinterest using this technique to finding the objects called pins in the images and suggest identical pins accordingly.
Facebook also uses machine learning (ML) to give the public information You May Know, Facebook observing everything you did like how many friends that you connect with, what profiles that you visit rarely, your interests hobbies and your works everything etc. It is also used in face recognization, to identify the objects.
And other applications Machine Learning (ML) used such as Online Fraud Detection, Search Engine Result Refining, Online Customer Support and Email Spam and Malware Filtering, self-driving cars etc.
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