Search

Machine learning Filter

Machine Learning Algorithms: [With Essentials, Principles, Types & Examples covered]

The advancements in Science and Technology are making every step of our daily life more comfortable. Today, the use of Machine learning systems, which is an integral part of Artificial Intelligence, has spiked and is seen playing a remarkable role in every user’s life. For instance, the widely popular, Virtual Personal Assistant being used for playing a music track or setting an alarm, face detection or voice recognition applications are the awesome examples of the machine learning systems that we see everyday. Machine learning, a subset of artificial intelligence, is the ability of a system to learn or predict the user’s needs and perform an expected task without human intervention. The inputs for the desired predictions are taken from user’s previously performed tasks or from relative examples.Why should you choose Machine Learning?Wonder why one should choose Machine Learning? Simply put, machine learning makes complex tasks much easier.  It makes the impossible possible!The following scenarios explain why we should opt for machine learning:During facial recognition and speech processing, it would be tedious to write the codes manually to execute the process, that's where machine learning comes handy.For market analysis, figuring customer preferences or fraud detection, machine learning has become essential.For the dynamic changes that happen in real-time tasks, it would be a challenging ordeal to solve through human intervention alone.Essentials of Machine Learning AlgorithmsTo state simply, machine learning is all about predictions – a machine learning, thinking and predicting what’s next. Here comes the question – what will a machine learn, how will a machine analyze, what will it predict.You have to understand two terms clearly before trying to get answers to these questions:DataAlgorithmDataData is what that is fed to the machine. For example, if you are trying to design a machine that can predict the weather over the next few days, then you should input the past ‘data’ that comprise maximum and minimum air temperatures, the speed of the wind, amount of rainfall, etc. All these come under ‘data’ that your machine will learn, and then analyse later.If we observe carefully, there will always be some pattern or the other in the input data we have. For example, the maximum and minimum ranges of temperatures may fall in the same bracket; or speeds of the wind may be slightly similar for a given season, etc. But, machine learning helps analyse such patterns very deeply. And then it predicts the outcomes of the problem we have designed it for.AlgorithmWhile data is the ‘food’ to the machine, an algorithm is like its digestive system. An algorithm works on the data. It crushes it; analyses it; permutates it; finds the gaps and fills in the blanks.Algorithms are the methods used by machines to work on the data input to them.What to consider before finalizing a Machine Learning algorithm?Depending on the functionality expected from the machine, algorithms range from very basic to highly complex. You should be wise in making a selection of an algorithm that suits your ML needs. Careful consideration and testing are needed before finalizing an algorithm for a purpose.For example, linear regression works well for simple ML functions such as speech analysis. In case, accuracy is your first choice, then slightly higher level functionalities such as Neural networks will do.This concept is called ‘The Explainability- Accuracy Tradeoff’. The following diagram explains this better:Image SourceBesides, with regards to machine learning algorithms, you need to remember the following aspects very clearly:No algorithm is an all-in-one solution to any type of problem; an algorithm that fits a scenario is not destined to fit in another one.Comparison of algorithms mostly does not make sense as each one of it has its own features and functionality. Many factors such as the size of data, data patterns, accuracy needed, the structure of the dataset, etc. play a major role in comparing two algorithms.The Principle behind Machine Learning AlgorithmsAs we learnt, an algorithm churns the given data and finds a pattern among them. Thus, all machine learning algorithms, especially the ones used for supervised learning, follow one similar principle:If the input variables or the data is X and you expect the machine to give a prediction or output Y, the machine will work on as per learning a target function ‘f’, whose pattern is not known to us.Thus, Y= f(X) fits well for every supervised machine learning algorithm. This is otherwise also called Predictive Modeling or Predictive Analysis, which ultimately provides us with the best ever prediction possible with utmost accuracy.Types of Machine Learning AlgorithmsDiving further into machine learning, we will first discuss the types of algorithms it has. Machine learning algorithms can be classified as:Supervised, andUnsupervisedSemi-supervised algorithmsReinforcement algorithmsA brief description of the types of  algorithms is given below:1. Supervised machine learning algorithmsIn this method, to get the output for a new set of user’s input, a model is trained to predict the results by using an old set of inputs and its relative known set of outputs. In other words, the system uses the examples used in the past.A data scientist trains the system on identifying the features and variables it should analyze. After training, these models compare the new results to the old ones and update their data accordingly to improve the prediction pattern.An example: If there is a basket full of fruits, based on the earlier specifications like color, shape and size given to the system, the model will be able to classify the fruits.There are 2 techniques in supervised machine learning and a technique to develop a model is chosen based on the type of data it has to work on.A) Techniques used in Supervised learningSupervised algorithms use either of the following techniques to develop a model based on the type of data.RegressionClassification1. Regression Technique In a given dataset, this technique is used to predict a numeric value or continuous values (a range of numeric values) based on the relation between variables obtained from the dataset.An example would be guessing the price of a house based after a year, based on the current price, total area, locality and number of bedrooms.Another example is predicting the room temperature in the coming hours, based on the volume of the room and current temperature.2. Classification Technique This is used if the input data can be categorized based on patterns or labels.For example, an email classification like recognizing a spam mail or face detection which uses patterns to predict the output.In summary, the regression technique is to be used when predictable data is in quantity and Classification technique is to be used when predictable data is about predicting a label.B) Algorithms that use Supervised LearningSome of the machine learning algorithms which use supervised learning method are:Linear RegressionLogistic RegressionRandom ForestGradient Boosted TreesSupport Vector Machines (SVM)Neural NetworksDecision TreesNaive BayesWe shall discuss some of these algorithms in detail as we move ahead in this post.2. Unsupervised machine learning algorithmsThis method does not involve training the model based on old data, I.e. there is no “teacher” or “supervisor” to provide the model with previous examples.The system is not trained by providing any set of inputs and relative outputs.  Instead, the model itself will learn and predict the output based on its own observations.For example, consider a basket of fruits which are not labeled/given any specifications this time. The model will only learn and organize them by comparing Color, Size and shape.A. Techniques used in unsupervised learningWe are discussing these techniques used in unsupervised learning as under:ClusteringDimensionality ReductionAnomaly detectionNeural networks1. ClusteringIt is the method of dividing or grouping the data in the given data set based on similarities.Data is explored to make groups or subsets based on meaningful separations.Clustering is used to determine the intrinsic grouping among the unlabeled data present.An example where clustering principle is being used is in digital image processing where this technique plays its role in dividing the image into distinct regions and identifying image border and the object.2. Dimensionality reductionIn a given dataset, there can be multiple conditions based on which data has to be segmented or classified.These conditions are the features that the individual data element has and may not be unique.If a dataset has too many numbers of such features, it makes it a complex process to segregate the data.To solve such type of complex scenarios, dimensional reduction technique can be used, which is a process that aims to reduce the number of variables or features in the given dataset without loss of important data.This is done by the process of feature selection or feature extraction.Email Classification can be considered as the best example where this technique was used.3. Anomaly DetectionAnomaly detection is also known as Outlier detection.It is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.Examples of the usage are identifying a structural defect, errors in text and medical problems.4. Neural NetworksA Neural network is a framework for many different machine learning algorithms to work together and process complex data inputs.It can be thought of as a “complex function” which gives some output when an input is given.The Neural Network consists of 3 parts which are needed in the construction of the model.Units or NeuronsConnections or Parameters.Biases.Neural networks are into a wide range of applications such as coastal engineering, hydrology and medicine where they are being used in identifying certain types of cancers.B. Algorithms that use unsupervised learningSome of the most common algorithms in unsupervised learning are:hierarchical clustering,k-meansmixture modelsDBSCANOPTICS algorithmAutoencodersDeep Belief NetsHebbian LearningGenerative Adversarial NetworksSelf-organizing mapWe shall discuss some of these algorithms in detail as we move ahead in this post.3.Semi Supervised AlgorithmsIn case of semi-supervised algorithms, as the name goes, it is a mix of both supervised and unsupervised algorithms. Here both labelled and unlabelled examples exist, and in many scenarios of semi-supervised learning, the count of unlabelled examples is more than that of labelled ones.Classification and regression form typical examples for semi-supervised algorithms.The algorithms under semi-supervised learning are mostly extensions of other methods, and the machines that are trained in the semi-supervised method make assumptions when dealing with unlabelled data.Examples of Semi Supervised Learning:Google Photos are the best example of this model of learning. You must have observed that at first, you define the user name in the picture and teach the features of the user by choosing a few photos. Then the algorithm sorts the rest of the pictures accordingly and asks you in case it gets any doubts during classification.Comparing with the previous supervised and unsupervised types of learning models, we can make the following inferences for semi-supervised learning:Labels are entirely present in case of supervised learning, while for unsupervised learning they are totally absent. Semi-supervised is thus a hybrid mix of both these two.The semi-supervised model fits well in cases where cost constraints are present for machine learning modelling. One can label the data as per cost requirements and leave the rest of the data to the machine to take up.Another advantage of semi-supervised learning methods is that they have the potential to exploit the unlabelled data of a group in cases where data carries important unexploited information.4. Reinforcement LearningIn this type of learning, the machine learns from the feedback it has received. It constantly learns and upgrades its existing skills by taking the feedback from the environment it is in.Markov’s Decision process is the best example of reinforcement learning.In this mode of learning, the machine learns iteratively the correct output. Based on the reward obtained from each iteration,the machine knows what is right and what is wrong. This iteration keeps going till the full range of probable outputs are covered.Process of Reinforcement LearningThe steps involved in reinforcement learning are as shown below:Input state is taken by the agentA predefined function indicates the action to be performedBased on the action, the reward is obtained by the machineThe resulting pair of feedback and action is stored for future purposesExamples of Reinforcement Learning AlgorithmsComputer based games such as chessArtificial hands that are based on roboticsDriverless cars/ self-driven carsMost Used Machine Learning Algorithms - ExplainedIn this section, let us discuss the following most widely used machine learning algorithms in detail:Decision TreesNaive Bayes ClassificationThe AutoencoderSelf-organizing mapHierarchical clusteringOPTICS algorithm1. Decision TreesThis algorithm is an example of supervised learning.A Decision tree is a pictorial representation or a graphical representation which depicts every possible outcome of a decision.The various elements involved here are node, branch and leaf where ‘node’ represents an ‘attribute’, ‘branch’ representing a ‘decision’ and ‘leaf’ representing an ‘outcome’ of the feature after applying that particular decision.A decision tree is just an analogy of how a human thinks to take a decision with yes/no questions.The below decision tree explains a school admission procedure rule, where Age is primarily checked, and if age is < 5, admission is not given to them. And for the kids who are eligible for admission, a check is performed on Annual income of parents where if it is < 3 L p.a. the students are further eligible to get a concession on the fees.2. Naive Bayes ClassificationThis supervised machine learning algorithm is a powerful and fast classifying algorithm, using the Bayes rule in determining the conditional probability and to predict the results.Its popular uses are, face recognition, filtering spam emails, predicting the user inputs in chat by checking communicated text and to label news articles as sports, politics etc.Bayes Rule: The Bayes theorem defines a rule in determining the probability of occurrence of an “Event” when information about “Tests” is provided.“Event” can be considered as the patient having a Heart disease while “tests” are the positive conditions that match with the event3. The AutoencoderIt comes under the category of unsupervised learning using neural networking techniques.An autoencoder is intended to learn or encode a representation for a given data set.This also involves the process of dimensional reduction which trains the network to remove the "noise" signal.In hand, with the reduction, it also works in reconstruction where the model tries to rebuild or generate a representation from the reduced encoding which is equivalent to the original input.I.e. without the loss of important and needed information from the given input, an Autoencoder removes or ignores the unnecessary noise and also works on rebuilding the output.Pic sourceThe most common use of Autoencoder is an application that converts black and white image to color. Based on the content and object in the image (like grass, water, sky, face, dress) coloring is processed.4. Self-organizing mapThis comes under the unsupervised learning method.Self-Organizing Map uses the data visualization technique by operating on a given high dimensional data.The Self-Organizing Map is a two-dimensional array of neurons: M = {m1,m2,......mn}It reduces the dimensions of the data to a map, representing the clustering concept by grouping similar data together.SOM reduces data dimensions and displays similarities among data.SOM uses clustering technique on data without knowing the class memberships of the input data where several units compete for the current object.In short, SOM converts complex, nonlinear statistical relationships between high-dimensional data into simple geometric relationships on a low-dimensional display.5. Hierarchical clusteringHierarchical clustering uses one of the below clustering techniques to determine a hierarchy of clusters.Thus produced hierarchy resembles a tree structure which is called a “Dendrogram”.The techniques used in hierarchical clustering are:K-Means,DBSCAN,Gaussian Mixture Models.The 2 methods in finding hierarchical clusters are:Agglomerative clusteringDivisive clusteringAgglomerative clusteringThis is a bottom-up approach, where each data point starts in its own cluster.These clusters are then joined greedily, by taking the two most similar clusters together and merging them.Divisive clusteringInverse to Agglomerative, this uses a top-down approach, wherein all data points start in the same cluster after which a parametric clustering algorithm like K-Means is used to divide the cluster into two clusters.Each cluster is further divided into two clusters until a desired number of clusters are hit.6. OPTICS algorithmOPTICS is an abbreviation for ordering points to identify the clustering structure.OPTICS works in principle like an extended DB Scan algorithm for an infinite number for a distance parameter which is smaller than a generating distance.From a wide range of parameter settings, OPTICS outputs a linear list of all objects under analysis in clusters based on their density.How to Choose Machine Learning Algorithms in Real TimeWhen implementing algorithms in real time, you need to keep in mind three main aspects: Space, Time, and Output.Besides, you should clearly understand the aim of your algorithm:Do you want to make predictions for the future?Are you just categorizing the given data?Is your targeted task simple or comprises of multiple sub-tasks?The following table will show you certain real-time scenarios and help you to understand which algorithm is best suited to each scenario:Real time scenarioBest suited algorithmWhy this algorithm is the best fit?Simple straightforward data set with no complex computationsLinear RegressionIt takes into account all factors involved and predicts the result with simple error rate explanation.For simple computations, you need not spend much computational power; and linear regression runs with minimal computational power.Classifying already labeled data into sub-labelsLogistic RegressionThis algorithm looks at every data point into two subcategories, hence best for sub-labeling.Logistic regression model works best when you have multiple targets.Sorting unlabelled data into groupsK-Means clustering algorithmThis algorithm groups and clusters data by measuring the spatial distance between each point.You can choose from its sub-types - Mean-Shift algorithm and Density-Based Spatial Clustering of Applications with NoiseSupervised text classification (analyzing reviews, comments, etc.)Naive BayesSimplest model that can perform powerful pre-processing and cleaning of textRemoves filler stop words effectivelyComputationally in-expensiveLogistic regressionSorts words one by one and assigns a probabilityRanks next to Naïve Bayes in simplicityLinear Support Vector Machine algorithmCan be chosen when performance mattersBag-of-words modelSuits best when vocabulary and the measure of known words is known.Image classificationConvolutional neural networkBest suited for complex computations such as analyzing visual cortexesConsumes more computational power and gives the best resultsStock market predictionsRecurrent neural networkBest suited for time-series analysis with well-defined and supervised data.Works efficiently in taking into account the relation between data and its time distribution.How to Run Machine Learning Algorithms?Till now you have learned in detail about various algorithms of machine learning, their features, selection and application in real time.When implementing the algorithm in real time, you can do it in any programming language that works well for machine learning.All that you need to do is use the standard libraries of the programming language that you have chosen and work on them, or program everything from scratch.Need more help? You can check these links for more clarity on coding machine learning algorithms in various programming languages.How To Get Started With Machine Learning Algorithms in RHow to Run Your First Classifier in WekaMachine Learning Algorithm Recipes in scikit-learnWhere do we stand in Machine Learning?Machine learning is slowly making strides into as many fields in our daily life as possible. Some businesses are making it strict to have transparent algorithms that do not affect their business privacy or data security. They are even framing regulations and performing audit trails to check if there is any discrepancy in the above-said data policies.The point to note here is that a machine working on machine learning principles and algorithms give output after processing the data through many nonlinear computations. If one needs to understand how a machine predicts, perhaps it can be possible only through another machine learning algorithm!Applications of Machine LearningCurrently, the role of Machine learning and Artificial Intelligence in human life is intertwined. With the advent of evolving technologies, AI and ML have marked their existence in all possible aspects.Machine learning finds a plethora of applications in several domains of our day to day life. An exhaustive list of fields where machine learning is currently in use now is shown in the diagram here. An explanation for the same follows further below:Financial Services: Banks and financial services are increasingly relying on machine learning to identify financial fraud, portfolio management, identify and suggest good options for investment for customers.Police Department: Apps based on facial recognition and other techniques of machine learning are being used by the police to identify and get hold of criminals.Online Marketing and Sales: Machine learning is helping companies a great deal in studying the shopping and spending patterns of customers and in making personalized product recommendations to them. Machine learning also eases customer support, product recommendations and advertising ideas for e-commerce.Healthcare: Doctors are using machine learning to predict and analyze the health status and disease progress of patients. Machine learning has proven its accuracy in detecting health condition, heartbeat, blood pressure and in identifying certain types of cancer. Advanced techniques of machine learning are being implemented in robotic surgery too.Household Applications: Household appliances that use face detection and voice recognition are gaining popularity as security devices and personal virtual assistants at homes.Oil and Gas: In analyzing underground minerals and carrying out the exploration and mining, geologists and scientists are using machine learning for improved accuracy and reduced investments.Transport: Machine learning can be used to identify the vehicles that are moving in prohibited zones for traffic control and safety monitoring purposes.Social Media: In social media, spam is a big nuisance. Companies are using machine learning to filter spam. Machine learning also aptly solves the purpose of sentiment analysis in social media.Trading and Commerce: Machine learning techniques are being implemented in online trading to automate the process of trading. Machines learn from the past performances of trading and use this knowledge to make decisions about future trading options.Future of Machine LearningMachine learning is already making a difference in the way businesses are offering their services to us, the customers. Voice-based search and preferences based ads are just basic functionalities of how machine learning is changing the face of businesses.ML has already made an inseparable mark in our lives. With more advancement in various fields, ML will be an integral part of all AI systems. ML algorithms are going to be made continuously learning with the day-to-day updating information.With the rapid rate at which ongoing research is happening in this field, there will be more powerful machine learning algorithms to make the way we live even more sophisticated!From 2013- 2017, the patents in the field of machine learning has recorded a growth of 34%, according to IFI Claims Patent Services (Patent Analytics). Also, 60% of the companies in the world are using machine learning for various purposes.A peek into the future trends and growth of machine learning through the reports of Predictive Analytics and Machine Learning (PAML) market shows a 21% CAGR by 2021.ConclusionUltimately, machine learning should be designed as an aid that would support mankind. The notion that automation and machine learning are threats to jobs and human workforce is pretty prevalent. It should always be remembered that machine learning is just a technology that has evolved to ease the life of humans by reducing the needed manpower and to offer increased efficiency at lower costs that too in a shorter time span. The onus of using machine learning in a responsible manner lies in the hands of those who work on/with it.However, stay tuned to an era of artificial intelligence and machine learning that makes the impossible possible and makes you witness the unseen!AI is likely to be the best thing or the worst thing to happen to humanity. – Stephen Hawking

Machine Learning Algorithms: [With Essentials, Principles, Types & Examples covered]

10555
  • by Animikh Aich
  • 03rd May, 2019
  • Last updated on 11th Mar, 2021
  • 20 mins read
Machine Learning Algorithms: [With Essentials, Principles, Types & Examples covered]

The advancements in Science and Technology are making every step of our daily life more comfortable. Today, the use of Machine learning systems, which is an integral part of Artificial Intelligence, has spiked and is seen playing a remarkable role in every user’s life. 

For instance, the widely popular, Virtual Personal Assistant being used for playing a music track or setting an alarm, face detection or voice recognition applications are the awesome examples of the machine learning systems that we see everyday. 

Machine learning, a subset of artificial intelligence, is the ability of a system to learn or predict the user’s needs and perform an expected task without human intervention. The inputs for the desired predictions are taken from user’s previously performed tasks or from relative examples.

Why should you choose Machine Learning?

Wonder why one should choose Machine Learning? Simply put, machine learning makes complex tasks much easier.  It makes the impossible possible!

The following scenarios explain why we should opt for machine learning:

Why should you choose Machine Learning?

  1. During facial recognition and speech processing, it would be tedious to write the codes manually to execute the process, that's where machine learning comes handy.
  2. For market analysis, figuring customer preferences or fraud detection, machine learning has become essential.
  3. For the dynamic changes that happen in real-time tasks, it would be a challenging ordeal to solve through human intervention alone.

Essentials of Machine Learning Algorithms

To state simply, machine learning is all about predictions – a machine learning, thinking and predicting what’s next. Here comes the question – what will a machine learn, how will a machine analyze, what will it predict.

You have to understand two terms clearly before trying to get answers to these questions:

  • Data
  • Algorithm

Essentials of Machine Learning Algorithms.

Data

Data is what that is fed to the machine. For example, if you are trying to design a machine that can predict the weather over the next few days, then you should input the past ‘data’ that comprise maximum and minimum air temperatures, the speed of the wind, amount of rainfall, etc. All these come under ‘data’ that your machine will learn, and then analyse later.

If we observe carefully, there will always be some pattern or the other in the input data we have. For example, the maximum and minimum ranges of temperatures may fall in the same bracket; or speeds of the wind may be slightly similar for a given season, etc. But, machine learning helps analyse such patterns very deeply. And then it predicts the outcomes of the problem we have designed it for.

Algorithm

 A graphical representation of an Algorithm

While data is the ‘food’ to the machine, an algorithm is like its digestive system. An algorithm works on the data. It crushes it; analyses it; permutates it; finds the gaps and fills in the blanks.

Algorithms are the methods used by machines to work on the data input to them.

What to consider before finalizing a Machine Learning algorithm?

Depending on the functionality expected from the machine, algorithms range from very basic to highly complex. You should be wise in making a selection of an algorithm that suits your ML needs. Careful consideration and testing are needed before finalizing an algorithm for a purpose.

For example, linear regression works well for simple ML functions such as speech analysis. In case, accuracy is your first choice, then slightly higher level functionalities such as Neural networks will do.

This concept is called ‘The Explainability- Accuracy Tradeoff’. The following diagram explains this better:

Explainability-accuracy tradeoff of Machine LearningImage Source

Besides, with regards to machine learning algorithms, you need to remember the following aspects very clearly:

  • No algorithm is an all-in-one solution to any type of problem; an algorithm that fits a scenario is not destined to fit in another one.
  • Comparison of algorithms mostly does not make sense as each one of it has its own features and functionality. Many factors such as the size of data, data patterns, accuracy needed, the structure of the dataset, etc. play a major role in comparing two algorithms.

The Principle behind Machine Learning Algorithms

As we learnt, an algorithm churns the given data and finds a pattern among them. Thus, all machine learning algorithms, especially the ones used for supervised learning, follow one similar principle:

If the input variables or the data is X and you expect the machine to give a prediction or output Y, the machine will work on as per learning a target function ‘f’, whose pattern is not known to us.

Thus, Y= f(X) fits well for every supervised machine learning algorithm. This is otherwise also called Predictive Modeling or Predictive Analysis, which ultimately provides us with the best ever prediction possible with utmost accuracy.

Types of Machine Learning Algorithms

Diving further into machine learning, we will first discuss the types of algorithms it has. Machine learning algorithms can be classified as:

  • Supervised, and
  • Unsupervised
  • Semi-supervised algorithms
  • Reinforcement algorithms

A brief description of the types of  algorithms is given below:

1. Supervised machine learning algorithms

In this method, to get the output for a new set of user’s input, a model is trained to predict the results by using an old set of inputs and its relative known set of outputs. In other words, the system uses the examples used in the past.

A data scientist trains the system on identifying the features and variables it should analyze. After training, these models compare the new results to the old ones and update their data accordingly to improve the prediction pattern.

An example: If there is a basket full of fruits, based on the earlier specifications like color, shape and size given to the system, the model will be able to classify the fruits.

There are 2 techniques in supervised machine learning and a technique to develop a model is chosen based on the type of data it has to work on.

A) Techniques used in Supervised learning

Supervised algorithms use either of the following techniques to develop a model based on the type of data.

  1. Regression
  2. Classification

1. Regression Technique 

  • In a given dataset, this technique is used to predict a numeric value or continuous values (a range of numeric values) based on the relation between variables obtained from the dataset.
  • An example would be guessing the price of a house based after a year, based on the current price, total area, locality and number of bedrooms.
  • Another example is predicting the room temperature in the coming hours, based on the volume of the room and current temperature.

2. Classification Technique 

  • This is used if the input data can be categorized based on patterns or labels.
  • For example, an email classification like recognizing a spam mail or face detection which uses patterns to predict the output.

In summary, the regression technique is to be used when predictable data is in quantity and Classification technique is to be used when predictable data is about predicting a label.

B) Algorithms that use Supervised Learning

Some of the machine learning algorithms which use supervised learning method are:

  • Linear Regression
  • Logistic Regression
  • Random Forest
  • Gradient Boosted Trees
  • Support Vector Machines (SVM)
  • Neural Networks
  • Decision Trees
  • Naive Bayes

We shall discuss some of these algorithms in detail as we move ahead in this post.

2. Unsupervised machine learning algorithms

This method does not involve training the model based on old data, I.e. there is no “teacher” or “supervisor” to provide the model with previous examples.

The system is not trained by providing any set of inputs and relative outputs.  Instead, the model itself will learn and predict the output based on its own observations.

For example, consider a basket of fruits which are not labeled/given any specifications this time. The model will only learn and organize them by comparing Color, Size and shape.

A. Techniques used in unsupervised learning

We are discussing these techniques used in unsupervised learning as under:

  • Clustering
  • Dimensionality Reduction
  • Anomaly detection
  • Neural networks

1. Clustering

  • It is the method of dividing or grouping the data in the given data set based on similarities.
  • Data is explored to make groups or subsets based on meaningful separations.
  • Clustering is used to determine the intrinsic grouping among the unlabeled data present.
  • An example where clustering principle is being used is in digital image processing where this technique plays its role in dividing the image into distinct regions and identifying image border and the object.

2. Dimensionality reduction

  • In a given dataset, there can be multiple conditions based on which data has to be segmented or classified.
  • These conditions are the features that the individual data element has and may not be unique.
  • If a dataset has too many numbers of such features, it makes it a complex process to segregate the data.
  • To solve such type of complex scenarios, dimensional reduction technique can be used, which is a process that aims to reduce the number of variables or features in the given dataset without loss of important data.
  • This is done by the process of feature selection or feature extraction.
  • Email Classification can be considered as the best example where this technique was used.

3. Anomaly Detection

  • Anomaly detection is also known as Outlier detection.
  • It is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.
  • Examples of the usage are identifying a structural defect, errors in text and medical problems.

4. Neural NetworksNeural Networks in Machine learning

  • A Neural network is a framework for many different machine learning algorithms to work together and process complex data inputs.
  • It can be thought of as a “complex function” which gives some output when an input is given.
  • The Neural Network consists of 3 parts which are needed in the construction of the model.
    • Units or Neurons
    • Connections or Parameters.
    • Biases.

Neural networks are into a wide range of applications such as coastal engineering, hydrology and medicine where they are being used in identifying certain types of cancers.

B. Algorithms that use unsupervised learning

Some of the most common algorithms in unsupervised learning are:

  1. hierarchical clustering,
  2. k-means
  3. mixture models
  4. DBSCAN
  5. OPTICS algorithm
  6. Autoencoders
  7. Deep Belief Nets
  8. Hebbian Learning
  9. Generative Adversarial Networks
  10. Self-organizing map

We shall discuss some of these algorithms in detail as we move ahead in this post.

3.Semi Supervised Algorithms

In case of semi-supervised algorithms, as the name goes, it is a mix of both supervised and unsupervised algorithms. Here both labelled and unlabelled examples exist, and in many scenarios of semi-supervised learning, the count of unlabelled examples is more than that of labelled ones.

Classification and regression form typical examples for semi-supervised algorithms.

The algorithms under semi-supervised learning are mostly extensions of other methods, and the machines that are trained in the semi-supervised method make assumptions when dealing with unlabelled data.

Examples of Semi Supervised Learning:

Google Photos are the best example of this model of learning. You must have observed that at first, you define the user name in the picture and teach the features of the user by choosing a few photos. Then the algorithm sorts the rest of the pictures accordingly and asks you in case it gets any doubts during classification.

Comparing with the previous supervised and unsupervised types of learning models, we can make the following inferences for semi-supervised learning:

  • Labels are entirely present in case of supervised learning, while for unsupervised learning they are totally absent. Semi-supervised is thus a hybrid mix of both these two.
  • The semi-supervised model fits well in cases where cost constraints are present for machine learning modelling. One can label the data as per cost requirements and leave the rest of the data to the machine to take up.
  • Another advantage of semi-supervised learning methods is that they have the potential to exploit the unlabelled data of a group in cases where data carries important unexploited information.

4. Reinforcement Learning

In this type of learning, the machine learns from the feedback it has received. It constantly learns and upgrades its existing skills by taking the feedback from the environment it is in.

Markov’s Decision process is the best example of reinforcement learning.

In this mode of learning, the machine learns iteratively the correct output. Based on the reward obtained from each iteration,the machine knows what is right and what is wrong. This iteration keeps going till the full range of probable outputs are covered.

Process of Reinforcement Learning

The steps involved in reinforcement learning are as shown below:

  1. Input state is taken by the agent
  2. A predefined function indicates the action to be performed
  3. Based on the action, the reward is obtained by the machine
  4. The resulting pair of feedback and action is stored for future purposes

Examples of Reinforcement Learning Algorithms

  • Computer based games such as chess
  • Artificial hands that are based on robotics
  • Driverless cars/ self-driven cars

Most Used Machine Learning Algorithms - Explained

In this section, let us discuss the following most widely used machine learning algorithms in detail:

  1. Decision Trees
  2. Naive Bayes Classification
  3. The Autoencoder
  4. Self-organizing map
  5. Hierarchical clustering
  6. OPTICS algorithm

1. Decision Trees

  • This algorithm is an example of supervised learning.
  • A Decision tree is a pictorial representation or a graphical representation which depicts every possible outcome of a decision.
  • The various elements involved here are node, branch and leaf where ‘node’ represents an ‘attribute’, ‘branch’ representing a ‘decision’ and ‘leaf’ representing an ‘outcome’ of the feature after applying that particular decision.
  • A decision tree is just an analogy of how a human thinks to take a decision with yes/no questions.
  • The below decision tree explains a school admission procedure rule, where Age is primarily checked, and if age is < 5, admission is not given to them. And for the kids who are eligible for admission, a check is performed on Annual income of parents where if it is < 3 L p.a. the students are further eligible to get a concession on the fees.

Decision Trees in Machine Learning Algorithm

2. Naive Bayes Classification

  • This supervised machine learning algorithm is a powerful and fast classifying algorithm, using the Bayes rule in determining the conditional probability and to predict the results.
  • Its popular uses are, face recognition, filtering spam emails, predicting the user inputs in chat by checking communicated text and to label news articles as sports, politics etc.
  • Bayes Rule: The Bayes theorem defines a rule in determining the probability of occurrence of an “Event” when information about “Tests” is provided.

Bayes Rule

  • “Event” can be considered as the patient having a Heart disease while “tests” are the positive conditions that match with the event

3. The Autoencoder

  • It comes under the category of unsupervised learning using neural networking techniques.
  • An autoencoder is intended to learn or encode a representation for a given data set.
  • This also involves the process of dimensional reduction which trains the network to remove the "noise" signal.
  • In hand, with the reduction, it also works in reconstruction where the model tries to rebuild or generate a representation from the reduced encoding which is equivalent to the original input.
  • I.e. without the loss of important and needed information from the given input, an Autoencoder removes or ignores the unnecessary noise and also works on rebuilding the output.

 The Autoencoder

Pic source

  • The most common use of Autoencoder is an application that converts black and white image to color. Based on the content and object in the image (like grass, water, sky, face, dress) coloring is processed.

4. Self-organizing map

  • This comes under the unsupervised learning method.
  • Self-Organizing Map uses the data visualization technique by operating on a given high dimensional data.
  • The Self-Organizing Map is a two-dimensional array of neurons: M = {m1,m2,......mn}
  • It reduces the dimensions of the data to a map, representing the clustering concept by grouping similar data together.
  • SOM reduces data dimensions and displays similarities among data.
  • SOM uses clustering technique on data without knowing the class memberships of the input data where several units compete for the current object.
  • In short, SOM converts complex, nonlinear statistical relationships between high-dimensional data into simple geometric relationships on a low-dimensional display.

5. Hierarchical clustering

  • Hierarchical clustering uses one of the below clustering techniques to determine a hierarchy of clusters.
  • Thus produced hierarchy resembles a tree structure which is called a “Dendrogram”.
  • The techniques used in hierarchical clustering are:
    • K-Means,
    • DBSCAN,
    • Gaussian Mixture Models.
  • The 2 methods in finding hierarchical clusters are:
  1. Agglomerative clustering
  2. Divisive clustering
  • Agglomerative clustering

    • This is a bottom-up approach, where each data point starts in its own cluster.
    • These clusters are then joined greedily, by taking the two most similar clusters together and merging them.
  • Divisive clustering

    • Inverse to Agglomerative, this uses a top-down approach, wherein all data points start in the same cluster after which a parametric clustering algorithm like K-Means is used to divide the cluster into two clusters.
    • Each cluster is further divided into two clusters until a desired number of clusters are hit.

6. OPTICS algorithm

  • OPTICS is an abbreviation for ordering points to identify the clustering structure.
  • OPTICS works in principle like an extended DB Scan algorithm for an infinite number for a distance parameter which is smaller than a generating distance.
  • From a wide range of parameter settings, OPTICS outputs a linear list of all objects under analysis in clusters based on their density.

How to Choose Machine Learning Algorithms in Real Time

When implementing algorithms in real time, you need to keep in mind three main aspects: Space, Time, and Output.

Besides, you should clearly understand the aim of your algorithm:

  • Do you want to make predictions for the future?
  • Are you just categorizing the given data?
  • Is your targeted task simple or comprises of multiple sub-tasks?

The following table will show you certain real-time scenarios and help you to understand which algorithm is best suited to each scenario:

Real time scenarioBest suited algorithmWhy this algorithm is the best fit?
Simple straightforward data set with no complex computationsLinear Regression
  • It takes into account all factors involved and predicts the result with simple error rate explanation.
  • For simple computations, you need not spend much computational power; and linear regression runs with minimal computational power.
Classifying already labeled data into sub-labelsLogistic Regression
  • This algorithm looks at every data point into two subcategories, hence best for sub-labeling.
  • Logistic regression model works best when you have multiple targets.
Sorting unlabelled data into groupsK-Means clustering algorithm
  • This algorithm groups and clusters data by measuring the spatial distance between each point.
  • You can choose from its sub-types - Mean-Shift algorithm and Density-Based Spatial Clustering of Applications with Noise
Supervised text classification (analyzing reviews, comments, etc.)Naive Bayes
  • Simplest model that can perform powerful pre-processing and cleaning of text
  • Removes filler stop words effectively
  • Computationally in-expensive
Logistic regression
  • Sorts words one by one and assigns a probability
  • Ranks next to Naïve Bayes in simplicity
Linear Support Vector Machine algorithm
  • Can be chosen when performance matters
Bag-of-words model
  • Suits best when vocabulary and the measure of known words is known.
Image classificationConvolutional neural network
  • Best suited for complex computations such as analyzing visual cortexes
  • Consumes more computational power and gives the best results
Stock market predictionsRecurrent neural network
  • Best suited for time-series analysis with well-defined and supervised data.
  • Works efficiently in taking into account the relation between data and its time distribution.

How to Run Machine Learning Algorithms?

Till now you have learned in detail about various algorithms of machine learning, their features, selection and application in real time.

When implementing the algorithm in real time, you can do it in any programming language that works well for machine learning.

All that you need to do is use the standard libraries of the programming language that you have chosen and work on them, or program everything from scratch.

Need more help? You can check these links for more clarity on coding machine learning algorithms in various programming languages.

How To Get Started With Machine Learning Algorithms in R

How to Run Your First Classifier in Weka

Machine Learning Algorithm Recipes in scikit-learn

Where do we stand in Machine Learning?

Machine learning is slowly making strides into as many fields in our daily life as possible. Some businesses are making it strict to have transparent algorithms that do not affect their business privacy or data security. They are even framing regulations and performing audit trails to check if there is any discrepancy in the above-said data policies.

The point to note here is that a machine working on machine learning principles and algorithms give output after processing the data through many nonlinear computations. If one needs to understand how a machine predicts, perhaps it can be possible only through another machine learning algorithm!

Applications of Machine Learning

Applications of Machine Learning

Currently, the role of Machine learning and Artificial Intelligence in human life is intertwined. With the advent of evolving technologies, AI and ML have marked their existence in all possible aspects.

Machine learning finds a plethora of applications in several domains of our day to day life. An exhaustive list of fields where machine learning is currently in use now is shown in the diagram here. An explanation for the same follows further below:

  1. Financial Services: Banks and financial services are increasingly relying on machine learning to identify financial fraud, portfolio management, identify and suggest good options for investment for customers.
  2. Police DepartmentApps based on facial recognition and other techniques of machine learning are being used by the police to identify and get hold of criminals.
  3. Online Marketing and Sales: Machine learning is helping companies a great deal in studying the shopping and spending patterns of customers and in making personalized product recommendations to them. Machine learning also eases customer support, product recommendations and advertising ideas for e-commerce.
  4. Healthcare: Doctors are using machine learning to predict and analyze the health status and disease progress of patients. Machine learning has proven its accuracy in detecting health condition, heartbeat, blood pressure and in identifying certain types of cancer. Advanced techniques of machine learning are being implemented in robotic surgery too.
  5. Household Applications: Household appliances that use face detection and voice recognition are gaining popularity as security devices and personal virtual assistants at homes.
  6. Oil and Gas: In analyzing underground minerals and carrying out the exploration and mining, geologists and scientists are using machine learning for improved accuracy and reduced investments.
  7. TransportMachine learning can be used to identify the vehicles that are moving in prohibited zones for traffic control and safety monitoring purposes.
  8. Social Media: In social media, spam is a big nuisance. Companies are using machine learning to filter spam. Machine learning also aptly solves the purpose of sentiment analysis in social media.
  9. Trading and Commerce: Machine learning techniques are being implemented in online trading to automate the process of trading. Machines learn from the past performances of trading and use this knowledge to make decisions about future trading options.

Future of Machine Learning

Machine learning is already making a difference in the way businesses are offering their services to us, the customers. Voice-based search and preferences based ads are just basic functionalities of how machine learning is changing the face of businesses.

ML has already made an inseparable mark in our lives. With more advancement in various fields, ML will be an integral part of all AI systems. ML algorithms are going to be made continuously learning with the day-to-day updating information.

With the rapid rate at which ongoing research is happening in this field, there will be more powerful machine learning algorithms to make the way we live even more sophisticated!

From 2013- 2017, the patents in the field of machine learning has recorded a growth of 34%, according to IFI Claims Patent Services (Patent Analytics). Also, 60% of the companies in the world are using machine learning for various purposes.

A peek into the future trends and growth of machine learning through the reports of Predictive Analytics and Machine Learning (PAML) market shows a 21% CAGR by 2021.

Conclusion

Machine Learning should be designed as an aid that would support mankind.

Ultimately, machine learning should be designed as an aid that would support mankind. The notion that automation and machine learning are threats to jobs and human workforce is pretty prevalent. It should always be remembered that machine learning is just a technology that has evolved to ease the life of humans by reducing the needed manpower and to offer increased efficiency at lower costs that too in a shorter time span. The onus of using machine learning in a responsible manner lies in the hands of those who work on/with it.

However, stay tuned to an era of artificial intelligence and machine learning that makes the impossible possible and makes you witness the unseen!

AI is likely to be the best thing or the worst thing to happen to humanity. – Stephen Hawking

Animikh

Animikh Aich

Computer Vision Engineer

Animikh Aich is a Deep Learning enthusiast, currently working as a Computer Vision Engineer. His work includes three International Conference publications and several projects based on Computer Vision and Machine Learning.

Join the Discussion

Your email address will not be published. Required fields are marked *

Suggested Blogs

Role of Unstructured Data in Data Science

Data has become the new game changer for businesses. Typically, data scientists categorize data into three broad divisions - structured, semi-structured, and unstructured data. In this article, you will get to know about unstructured data, sources of unstructured data, unstructured data vs. structured data, the use of structured and unstructured data in machine learning, and the difference between structured and unstructured data. Let us first understand what is unstructured data with examples. What is unstructured data? Unstructured data is a kind of data format where there is no organized form or type of data. Videos, texts, images, document files, audio materials, email contents and more are considered to be unstructured data. It is the most copious form of business data, and cannot be stored in a structured database or relational database. Some examples of unstructured data are the photos we post on social media platforms, the tagging we do, the multimedia files we upload, and the documents we share. Seagate predicts that the global data-sphere will expand to 163 zettabytes by 2025, where most of the data will be in the unstructured format. Characteristics of Unstructured DataUnstructured data cannot be organized in a predefined fashion, and is not a homogenous data model. This makes it difficult to manage. Apart from that, these are the other characteristics of unstructured data. You cannot store unstructured data in the form of rows and columns as we do in a database table. Unstructured data is heterogeneous in structure and does not have any specific data model. The creation of such data does not follow any semantics or habits. Due to the lack of any particular sequence or format, it is difficult to manage. Such data does not have an identifiable structure. Sources of Unstructured Data There are various sources of unstructured data. Some of them are: Content websites Social networking sites Online images Memos Reports and research papers Documents, spreadsheets, and presentations Audio mining, chatbots Surveys Feedback systems Advantages of Unstructured Data Unstructured data has become exceptionally easy to store because of MongoDB, Cassandra, or even using JSON. Modern NoSQL databases and software allows data engineers to collect and extract data from various sources. There are numerous benefits that enterprises and businesses can gain from unstructured data. These are: With the advent of unstructured data, we can store data that lacks a proper format or structure. There is no fixed schema or data structure for storing such data, which gives flexibility in storing data of different genres. Unstructured data is much more portable by nature. Unstructured data is scalable and flexible to store. Database systems like MongoDB, Cassandra, etc., can easily handle the heterogeneous properties of unstructured data. Different applications and platforms produce unstructured data that becomes useful in business intelligence, unstructured data analytics, and various other fields. Unstructured data analysis allows finding comprehensive data stories from data like email contents, website information, social media posts, mobile data, cache files and more. Unstructured data, along with data analytics, helps companies improve customer experience. Detection of the taste of consumers and their choices becomes easy because of unstructured data analysis. Disadvantages of Unstructured data Storing and managing unstructured data is difficult because there is no proper structure or schema. Data indexing is also a substantial challenge and hence becomes unclear due to its disorganized nature. Search results from an unstructured dataset are also not accurate because it does not have predefined attributes. Data security is also a challenge due to the heterogeneous form of data. Problems faced and solutions for storing unstructured data. Until recently, it was challenging to store, evaluate, and manage unstructured data. But with the advent of modern data analysis tools, algorithms, CAS (content addressable storage system), and big data technologies, storage and evaluation became easy. Let us first take a look at the various challenges used for storing unstructured data. Storing unstructured data requires a large amount of space. Indexing of unstructured data is a hectic task. Database operations such as deleting and updating become difficult because of the disorganized nature of the data. Storing and managing video, audio, image file, emails, social media data is also challenging. Unstructured data increases the storage cost. For solving such issues, there are some particular approaches. These are: CAS system helps in storing unstructured data efficiently. We can preserve unstructured data in XML format. Developers can store unstructured data in an RDBMS system supporting BLOB. We can convert unstructured data into flexible formats so that evaluating and storage becomes easy. Let us now understand the differences between unstructured data vs. structured data. Unstructured Data Vs. Structured Data In this section, we will understand the difference between structured and unstructured data with examples. STRUCTUREDUNSTRUCTUREDStructured data resides in an organized format in a typical database.Unstructured data cannot reside in an organized format, and hence we cannot store it in a typical database.We can store structured data in SQL database tables having rows and columns.Storing and managing unstructured data requires specialized databases, along with a variety of business intelligence and analytics applications.It is tough to scale a database schema.It is highly scalable.Structured data gets generated in colleges, universities, banks, companies where people have to deal with names, date of birth, salary, marks and so on.We generate or find unstructured data in social media platforms, emails, analyzed data for business intelligence, call centers, chatbots and so on.Queries in structured data allow complex joining.Unstructured data allows only textual queries.The schema of a structured dataset is less flexible and dependent.An unstructured dataset is flexible but does not have any particular schema.It has various concurrency techniques.It has no concurrency techniques.We can use SQL, MySQL, SQLite, Oracle DB, Teradata to store structured data.We can use NoSQL (Not Only SQL) to store unstructured data.Types of Unstructured Data Do you have any idea just how much of unstructured data we produce and from what sources? Unstructured data includes all those forms of data that we cannot actively manage in an RDBMS system that is a transactional system. We can store structured data in the form of records. But this is not the case with unstructured data. Before the advent of object-based storage, most of the unstructured data was stored in file-based systems. Here are some of the types of unstructured data. Rich media content: Entertainment files, surveillance data, multimedia email attachments, geospatial data, audio files (call center and other recorded audio), weather reports (graphical), etc., comes under this genre. Document data: Invoices, text-file records, email contents, productivity applications, etc., are included under this genre. Internet of Things (IoT) data: Ticker data, sensor data, data from other IoT devices come under this genre. Apart from all these, data from business intelligence and analysis, machine learning datasets, and artificial intelligence data training datasets are also a separate genre of unstructured data. Examples of Unstructured Data There are various sources from where we can obtain unstructured data. The prominent use of this data is in unstructured data analytics. Let us now understand what are some examples of unstructured data and their sources – Healthcare industries generate a massive volume of human as well as machine-generated unstructured data. Human-generated unstructured data could be in the form of patient-doctor or patient-nurse conversations, which are usually recorded in audio or text formats. Unstructured data generated by machines includes emergency video camera footage, surgical robots, data accumulated from medical imaging devices like endoscopes, laparoscopes and more.  Social Media is an intrinsic entity of our daily life. Billions of people come together to join channels, share different thoughts, and exchange information with their loved ones. They create and share such data over social media platforms in the form of images, video clips, audio messages, tagging people (this helps companies to map relations between two or more people), entertainment data, educational data, geolocations, texts, etc. Other spectra of data generated from social media platforms are behavior patterns, perceptions, influencers, trends, news, and events. Business and corporate documents generate a multitude of unstructured data such as emails, presentations, reports containing texts, images, presentation reports, video contents, feedback and much more. These documents help to create knowledge repositories within an organization to make better implicit operations. Live chat, video conferencing, web meeting, chatbot-customer messages, surveillance data are other prominent examples of unstructured data that companies can cultivate to get more insights into the details of a person. Some prominent examples of unstructured data used in enterprises and organizations are: Reports and documents, like Word files or PDF files Multimedia files, such as audio, images, designed texts, themes, and videos System logs Medical images Flat files Scanned documents (which are images that hold numbers and text – for example, OCR) Biometric data Unstructured Data Analytics Tools  You might be wondering what tools can come into use to gather and analyze information that does not have a predefined structure or model. Various tools and programming languages use structured and unstructured data for machine learning and data analysis. These are: Tableau MonkeyLearn Apache Spark SAS Python MS. Excel RapidMiner KNIME QlikView Python programming R programming Many cloud services (like Amazon AWS, Microsoft Azure, IBM Cloud, Google Cloud) also offer unstructured data analysis solutions bundled with their services. How to analyze unstructured data? In the past, the process of storage and analysis of unstructured data was not well defined. Enterprises used to carry out this kind of analysis manually. But with the advent of modern tools and programming languages, most of the unstructured data analysis methods became highly advanced. AI-powered tools use algorithms designed precisely to help to break down unstructured data for analysis. Unstructured data analytics tools, along with Natural language processing (NLP) and machine learning algorithms, help advanced software tools analyze and extract analytical data from the unstructured datasets. Before using these tools for analyzing unstructured data, you must properly go through a few steps and keep these points in mind. Set a clear goal for analyzing the data: It is essential to clear your intention about what insights you want to extract from your unstructured data. Knowing this will help you distinguish what type of data you are planning to accumulate. Collect relevant data: Unstructured data is available everywhere, whether it's a social media platform, online feedback or reviews, or a survey form. Depending on the previous point, that is your goal - you have to be precise about what data you want to collect in real-time. Also, keep in mind whether your collected details are relevant or not. Clean your data: Data cleaning or data cleansing is a significant process to detect corrupt or irrelevant data from the dataset, followed by modifying or deleting the coarse and sloppy data. This phase is also known as the data-preprocessing phase, where you have to reduce the noise, carry out data slicing for meaningful representation, and remove unnecessary data. Use Technology and tools: Once you perform the data cleaning, it is time to utilize unstructured data analysis tools to prepare and cultivate the insights from your data. Technologies used for unstructured data storage (NoSQL) can help in managing your flow of data. Other tools and programming libraries like Tableau, Matplotlib, Pandas, and Google Data Studio allows us to extract and visualize unstructured data. Data can be visualized and presented in the form of compelling graphs, plots, and charts. How to Extract information from Unstructured Data? With the growth in digitization during the information era, repetitious transactions in data cause data flooding. The exponential accretion in the speed of digital data creation has brought a whole new domain of understanding user interaction with the online world. According to Gartner, 80% of the data created by an organization or its application is unstructured. While extracting exact information through appropriate analysis of organized data is not yet possible, even obtaining a decent sense of this unstructured data is quite tough. Until now, there are no perfect tools to analyze unstructured data. But algorithms and tools designed using machine learning, Natural language processing, Deep learning, and Graph Analysis (a mathematical method for estimating graph structures) help us to get the upper hand in extracting information from unstructured data. Other neural network models like modern linguistic models follow unsupervised learning techniques to gain a good 'knowledge' about the unstructured dataset before going into a specific supervised learning step. AI-based algorithms and technologies are capable enough to extract keywords, locations, phone numbers, analyze image meaning (through digital image processing). We can then understand what to evaluate and identify information that is essential to your business. ConclusionUnstructured data is found abundantly from sources like documents, records, emails, social media posts, feedbacks, call-records, log-in session data, video, audio, and images. Manually analyzing unstructured data is very time-consuming and can be very boring at the same time. With the growth of data science and machine learning algorithms and models, it has become easy to gather and analyze insights from unstructured information.  According to some research, data analytics tools like MonkeyLearn Studio, Tableau, RapidMiner help analyze unstructured data 1200x faster than the manual approach. Analyzing such data will help you learn more about your customers as well as competitors. Text analysis software, along with machine learning models, will help you dig deep into such datasets and make you gain an in-depth understanding of the overall scenario with fine-grained analyses.
5738
Role of Unstructured Data in Data Science

Data has become the new game changer for busines... Read More

What Is Statistical Analysis and Its Business Applications?

Statistics is a science concerned with collection, analysis, interpretation, and presentation of data. In Statistics, we generally want to study a population. You may consider a population as a collection of things, persons, or objects under experiment or study. It is usually not possible to gain access to all of the information from the entire population due to logistical reasons. So, when we want to study a population, we generally select a sample. In sampling, we select a portion (or subset) of the larger population and then study the portion (or the sample) to learn about the population. Data is the result of sampling from a population.Major ClassificationThere are two basic branches of Statistics – Descriptive and Inferential statistics. Let us understand the two branches in brief. Descriptive statistics Descriptive statistics involves organizing and summarizing the data for better and easier understanding. Unlike Inferential statistics, Descriptive statistics seeks to describe the data, however, it does not attempt to draw inferences from the sample to the whole population. We simply describe the data in a sample. It is not developed on the basis of probability unlike Inferential statistics. Descriptive statistics is further broken into two categories – Measure of Central Tendency and Measures of Variability. Inferential statisticsInferential statistics is the method of estimating the population parameter based on the sample information. It applies dimensions from sample groups in an experiment to contrast the conduct group and make overviews on the large population sample. Please note that the inferential statistics are effective and valuable only when examining each member of the group is difficult. Let us understand Descriptive and Inferential statistics with the help of an example. Task – Suppose, you need to calculate the score of the players who scored a century in a cricket tournament.  Solution: Using Descriptive statistics you can get the desired results.   Task – Now, you need the overall score of the players who scored a century in the cricket tournament.  Solution: Applying the knowledge of Inferential statistics will help you in getting your desired results.  Top Five Considerations for Statistical Data AnalysisData can be messy. Even a small blunder may cost you a fortune. Therefore, special care when working with statistical data is of utmost importance. Here are a few key takeaways you must consider to minimize errors and improve accuracy. Define the purpose and determine the location where the publication will take place.  Understand the assets to undertake the investigation. Understand the individual capability of appropriately managing and understanding the analysis.  Determine whether there is a need to repeat the process.  Know the expectation of the individuals evaluating reviewing, committee, and supervision. Statistics and ParametersDetermining the sample size requires understanding statistics and parameters. The two being very closely related are often confused and sometimes hard to distinguish.  StatisticsA statistic is merely a portion of a target sample. It refers to the measure of the values calculated from the population.  A parameter is a fixed and unknown numerical value used for describing the entire population. The most commonly used parameters are: Mean Median Mode Mean :  The mean is the average or the most common value in a data sample or a population. It is also referred to as the expected value. Formula: Sum of the total number of observations/the number of observations. Experimental data set: 2, 4, 6, 8, 10, 12, 14, 16, 18, 20  Calculating mean:   (2 + 4 + 6 + 8 + 10 + 12 + 14 + 16 + 18 + 20)/10  = 110/10   = 11 Median:  In statistics, the median is the value separating the higher half from the lower half of a data sample, a population, or a probability distribution. It’s the mid-value obtained by arranging the data in increasing order or descending order. Formula:  Let n be the data set (increasing order) When data set is odd: Median = n+1/2th term Case-I: (n is odd)  Experimental data set = 1, 2, 3, 4, 5  Median (n = 5) = [(5 +1)/2]th term      = 6/2 term       = 3rd term   Therefore, the median is 3 When data set is even: Median = [n/2th + (n/2 + 1)th] /2 Case-II: (n is even)  Experimental data set = 1, 2, 3, 4, 5, 6   Median (n = 6) = [n/2th + (n/2 + 1)th]/2     = ( 6/2th + (6/2 +1)th]/2     = (3rd + 4th)/2      = (3 + 4)/2      = 7/2      = 3.5  Therefore, the median is 3.5 Mode: The mode is the value that appears most often in a set of data or a population. Experimental data set= 1, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4,4,5, 6  Mode = 3 (Since 3 is the most repeated element in the sequence.) Terms Used to Describe DataWhen working with data, you will need to search, inspect, and characterize them. To understand the data in a tech-savvy and straightforward way, we use a few statistical terms to denote them individually or in groups.  The most frequently used terms used to describe data include data point, quantitative variables, indicator, statistic, time-series data, variable, data aggregation, time series, dataset, and database. Let us define each one of them in brief: Data points: These are the numerical files formed and organized for interpretations. Quantitative variables: These variables present the information in digit form.  Indicator: An indicator explains the action of a community's social-economic surroundings.  Time-series data: The time-series defines the sequential data.  Data aggregation: A group of data points and data set. Database: A group of arranged information for examination and recovery.  Time-series: A set of measures of a variable documented over a specified time. Step-by-Step Statistical Analysis ProcessThe statistical analysis process involves five steps followed one after another. Step 1: Design the study and find the population of the study. Step 2: Collect data as samples. Step 3: Describe the data in the sample. Step 4: Make inferences with the help of samples and calculations Step 5: Take action Data distributionData distribution is an entry that displays entire imaginable readings of data. It shows how frequently a value occurs. Distributed data is always in ascending order, charts, and graphs enabling visibility of measurements and frequencies. The distribution function displaying the density of values of reading is known as the probability density function. Percentiles in data distributionA percentile is the reading in a distribution with a specified percentage of clarifications under it.  Let us understand percentiles with the help of an example.  Suppose you have scored 90th percentile on a math test. A basic interpretation is that merely 4-5% of the scores were higher than your scores. Right? The median is 50th percentile because the assumed 50% of the values are higher than the median. Dispersion Dispersion explains the magnitude of distribution readings anticipated for a specific variable and multiple unique statistics like range, variance, and standard deviation. For instance, high values of a data set are widely scattered while small values of data are firmly clustered. Histogram The histogram is a pictorial display that arranges a group of data facts into user detailed ranges. A histogram summarizes a data series into a simple interpreted graphic by obtaining many data facts and combining them into reasonable ranges. It contains a variety of results into columns on the x-axis. The y axis displays percentages of data for each column and is applied to picture data distributions. Bell Curve distribution Bell curve distribution is a pictorial representation of a probability distribution whose fundamental standard deviation obtained from the mean makes the bell, shaped curving. The peak point on the curve symbolizes the maximum likely occasion in a pattern of data. The other possible outcomes are symmetrically dispersed around the mean, making a descending sloping curve on both sides of the peak. The curve breadth is therefore known as the standard deviation. Hypothesis testingHypothesis testing is a process where experts experiment with a theory of a population parameter. It aims to evaluate the credibility of a hypothesis using sample data. The five steps involved in hypothesis testing are:  Identify the no outcome hypothesis.  (A worthless or a no-output hypothesis has no outcome, connection, or dissimilarities amongst many factors.) Identify the alternative hypothesis.  Establish the importance level of the hypothesis.  Estimate the experiment statistic and equivalent P-value. P-value explains the possibility of getting a sample statistic.  Sketch a conclusion to interpret into a report about the alternate hypothesis. Types of variablesA variable is any digit, amount, or feature that is countable or measurable. Simply put, it is a variable characteristic that varies. The six types of variables include the following: Dependent variableA dependent variable has values that vary according to the value of another variable known as the independent variable.  Independent variableAn independent variable on the other side is controllable by experts. Its reports are recorded and equated.  Intervening variableAn intervening variable explicates fundamental relations between variables. Moderator variableA moderator variable upsets the power of the connection between dependent and independent variables.  Control variableA control variable is anything restricted to a research study. The values are constant throughout the experiment. Extraneous variableExtraneous variable refers to the entire variables that are dependent but can upset experimental outcomes. Chi-square testChi-square test records the contrast of a model to actual experimental data. Data is unsystematic, underdone, equally limited, obtained from independent variables, and a sufficient sample. It relates the size of any inconsistencies among the expected outcomes and the actual outcomes, provided with the sample size and the number of variables in the connection. Types of FrequenciesFrequency refers to the number of repetitions of reading in an experiment in a given time. Three types of frequency distribution include the following: Grouped, ungrouped Cumulative, relative Relative cumulative frequency distribution. Features of FrequenciesThe calculation of central tendency and position (median, mean, and mode). The measure of dispersion (range, variance, and standard deviation). Degree of symmetry (skewness). Peakedness (kurtosis). Correlation MatrixThe correlation matrix is a table that shows the correlation coefficients of unique variables. It is a powerful tool that summarises datasets points and picture sequences in the provided data. A correlation matrix includes rows and columns that display variables. Additionally, the correlation matrix exploits in aggregation with other varieties of statistical analysis. Inferential StatisticsInferential statistics use random data samples for demonstration and to create inferences. They are measured when analysis of each individual of a whole group is not likely to happen. Applications of Inferential StatisticsInferential statistics in educational research is not likely to sample the entire population that has summaries. For instance, the aim of an investigation study may be to obtain whether a new method of learning mathematics develops mathematical accomplishment for all students in a class. Marketing organizations: Marketing organizations use inferential statistics to dispute a survey and request inquiries. It is because carrying out surveys for all the individuals about merchandise is not likely. Finance departments: Financial departments apply inferential statistics for expected financial plan and resources expenses, especially when there are several indefinite aspects. However, economists cannot estimate all that use possibility. Economic planning: In economic planning, there are potent methods like index figures, time series investigation, and estimation. Inferential statistics measures national income and its components. It gathers info about revenue, investment, saving, and spending to establish links among them. Key TakeawaysStatistical analysis is the gathering and explanation of data to expose sequences and tendencies.   Two divisions of statistical analysis are statistical and non-statistical analyses.  Descriptive and Inferential statistics are the two main categories of statistical analysis. Descriptive statistics describe data, whereas Inferential statistics equate dissimilarities between the sample groups.  Statistics aims to teach individuals how to use restricted samples to generate intellectual and precise results for a large group.   Mean, median, and mode are the statistical analysis parameters used to measure central tendency.   Conclusion Statistical analysis is the procedure of gathering and examining data to recognize sequences and trends. It uses random samples of data obtained from a population to demonstrate and create inferences on a group. Inferential statistics applies economic planning with potent methods like index figures, time series investigation, and estimation.  Statistical analysis finds its applications in all the major sectors – marketing, finance, economic, operations, and data mining. Statistical analysis aids marketing organizations in disputing a survey and requesting inquiries concerning their merchandise. 
5876
What Is Statistical Analysis and Its Business Appl...

Statistics is a science concerned with collection,... Read More

Measures of Dispersion: All You Need to Know

What is Dispersion in StatisticsDispersion in statistics is a way of describing how spread out a set of data is. Dispersion is the state of data getting dispersed, stretched, or spread out in different categories. It involves finding the size of distribution values that are expected from the set of data for the specific variable. The statistical meaning of dispersion is “numeric data that is likely to vary at any instance of average value assumption”.Dispersion of data in Statistics helps one to easily understand the dataset by classifying them into their own specific dispersion criteria like variance, standard deviation, and ranging.Dispersion is a set of measures that helps one to determine the quality of data in an objectively quantifiable manner.The measure of dispersion contains almost the same unit as the quantity being measured. There are many Measures of Dispersion found which help us to get more insights into the data: Range Variance Standard Deviation Skewness IQR  Image SourceTypes of Measure of DispersionThe Measure of Dispersion is divided into two main categories and offer ways of measuring the diverse nature of data. It is mainly used in biological statistics. We can easily classify them by checking whether they contain units or not. So as per the above, we can divide the data into two categories which are: Absolute Measure of Dispersion Relative Measure of DispersionAbsolute Measure of DispersionAbsolute Measure of Dispersion is one with units; it has the same unit as the initial dataset. Absolute Measure of Dispersion is expressed in terms of the average of the dispersion quantities like Standard or Mean deviation. The Absolute Measure of Dispersion can be expressed  in units such as Rupees, Centimetre, Marks, kilograms, and other quantities that are measured depending on the situation. Types of Absolute Measure of Dispersion: Range: Range is the measure of the difference between the largest and smallest value of the data variability. The range is the simplest form of Measure of Dispersion. Example: 1,2,3,4,5,6,7 Range = Highest value – Lowest value   = ( 7 – 1 ) = 6 Mean (μ): Mean is calculated as the average of the numbers. To calculate the Mean, add all the outcomes and then divide it with the total number of terms. Example: 1,2,3,4,5,6,7,8 Mean = (sum of all the terms / total number of terms)                = (1 + 2 + 3 + 4 + 5 + 6 + 7 + 8) / 8                = 36 / 8                = 4.5 Variance (σ2): In simple terms, the variance can be calculated by obtaining the sum of the squared distance of each term in the distribution from the Mean, and then dividing this by the total number of the terms in the distribution.  It basically shows how far a number, for example, a student’s mark in an exam, is from the Mean of the entire class. Formula: (σ2) = ∑ ( X − μ)2 / N Standard Deviation: Standard Deviation can be represented as the square root of Variance. To find the standard deviation of any data, you need to find the variance first. Formula: Standard Deviation = √σ Quartile: Quartiles divide the list of numbers or data into quarters. Quartile Deviation: Quartile Deviation is the measure of the difference between the upper and lower quartile. This measure of deviation is also known as interquartile range. Formula: Interquartile Range: Q3 – Q1. Mean deviation: Mean Deviation is also known as an average deviation; it can be computed using the Mean or Median of the data. Mean deviation is represented as the arithmetic deviation of a different item that follows the central tendency. Formula: As mentioned, the Mean Deviation can be calculated using Mean and Median. Mean Deviation using Mean: ∑ | X – M | / N Mean Deviation using Median: ∑ | X – X1 | / N Relative Measure of DispersionRelative Measures of dispersion are the values without units. A relative measure of dispersion is used to compare the distribution of two or more datasets.  The definition of the Relative Measure of Dispersion is the same as the Absolute Measure of Dispersion; the only difference is the measuring quantity.  Types of Relative Measure of Dispersion: Relative Measure of Dispersion is the calculation of the co-efficient of Dispersion, where 2 series are compared, which differ widely in their average.  The main use of the co-efficient of Dispersion is when 2 series with different measurement units are compared.  1. Co-efficient of Range: it is calculated as the ratio of the difference between the largest and smallest terms of the distribution, to the sum of the largest and smallest terms of the distribution.  Formula: L – S / L + S  where L = largest value S= smallest value 2. Co-efficient of Variation: The coefficient of variation is used to compare the 2 data with respect to homogeneity or consistency.  Formula: C.V = (σ / X) 100 X = standard deviation  σ = mean 3. Co-efficient of Standard Deviation: The co-efficient of Standard Deviation is the ratio of standard deviation with the mean of the distribution of terms.  Formula: σ = ( √( X – X1)) / (N - 1) Deviation = ( X – X1)  σ = standard deviation  N= total number  4. Co-efficient of Quartile Deviation: The co-efficient of Quartile Deviation is the ratio of the difference between the upper quartile and the lower quartile to the sum of the upper quartile and lower quartile.  Formula: ( Q3 – Q3) / ( Q3 + Q1) Q3 = Upper Quartile  Q1 = Lower Quartile 5. Co-efficient of Mean Deviation: The co-efficient of Mean Deviation can be computed using the mean or median of the data. Mean Deviation using Mean: ∑ | X – M | / N Mean Deviation using Mean: ∑ | X – X1 | / N Why dispersion is important in a statisticThe knowledge of dispersion is vital in the understanding of statistics. It helps to understand concepts like the diversification of the data, how the data is spread, how it is maintained, and maintaining the data over the central value or central tendency. Moreover, dispersion in statistics provides us with a way to get better insights into data distribution. For example,  3 distinct samples can have the same Mean, Median, or Range but completely different levels of variability. How to Calculate DispersionDispersion can be easily calculated using various dispersion measures, which are already mentioned in the types of Measure of Dispersion described above. Before measuring the data, it is important to understand the diversion of the terms and variation. One can use the following method to calculate the dispersion: Mean Standard deviation Variance Quartile deviation For example, let us consider two datasets: Data A:97,98,99,100,101,102,103  Data B: 70,80,90,100,110,120,130 On calculating the mean and median of the two datasets, both have the same value, which is 100. However, the rest of the dispersion measures are totally different as measured by the above methods.  The range of B is 10 times higher, for instance. How to represent Dispersion in Statistics Dispersion in Statistics can be represented in the form of graphs and pie-charts. Some of the different ways used include: Dot Plots Box Plots Stems Leaf Plots Example: What is the variance of the values 3,8,6,10,12,9,11,10,12,7?  Variation of the values can be calculated using the following formula: (σ2) = ∑ ( X − μ)2 / N (σ2) = 7.36 What is an example of dispersion? One of the examples of dispersion outside the world of statistics is the rainbow- where white light is split into 7 different colours separated via wavelengths.  Some statistical ways of measuring it are- Standard deviation Range Mean absolute difference Median absolute deviation Interquartile change Average deviation Conclusion: Dispersion in statistics refers to the measure of variability of data or terms. Such variability may give random measurement errors where some of the instrumental measurements are found to be imprecise. It is a statistical way of describing how the terms are spread out in different data sets. The more sets of values, the more scattered data is found, and it is always directly proportional. This range of values can vary from 5 - 10 values to 1000 - 10,000 values. This spread of data is described by the range of descriptive range of statistics. The dispersion in statistics can be represented using a Dot Plot, Box Plot, and other different ways. 
9261
Measures of Dispersion: All You Need to Know

What is Dispersion in StatisticsDispersion in stat... Read More