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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]

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  • 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.

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All the big and small businesses are adopting Machine Learning models to improve their bottom-line margins and return on investment.  The use of Machine Learning has gone beyond just technology and it is now used in diverse industries including healthcare, automobile, manufacturing, government and more. This has greatly enhanced the value of Machine Learning experts who can earn an average salary of $112,000.  Huge numbers of jobs are expected to be created in the coming years.  Here are a few reasons why one should pursue a career in Machine Learning:The global machine learning market is expected to touch $20.83B in 2024, according to Forbes.  We are living in a digital age and this explosion of data has made the use of machine learning models a necessity. 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This says a lot, and proves that a career in Machine Learning is in high demand as all businesses are incorporating various machine learning techniques and are improving their business.One can harness this popularity by skilling up with Machine Learning skills. Machine Learning models are now being used by every company, irrespective of their size--small or big, to get insights on their data and use these insights to improve the business. As every company wishes to grow faster, they are deploying more machine learning engineers to get their work done on time. Also, the migration of businesses to Cloud services for better security and scalability, has increased their requirement for more Machine Learning algorithms and models to cater to their needs.Introducing the Machine learning techniques and solutions has brought huge returns for businesses.  Machine Learning solution providers like Google, IBM, Microsoft etc. are investing in human resources for development of Machine Learning models and algorithms. The tools developed by them are popularly used by businesses to get early returns. It has been observed that there is significant increase in patents in Machine Learning domains since the past few years, indicating the quantum of work happening in this domain.Machine Learning SkillsLet’s visit a few important skills one must acquire to work in the domain of Machine Learning.Programming languagesKnowledge of programming is very important for a career in Machine Learning. Languages like Python and R are popularly used to develop applications using Machine Learning models and algorithms. Python, being the simplest and most flexible language, is very popular for AI and Machine Learning applications. These languages provide rich support of libraries for implementation of Machine Learning Algorithms. A person who is good in programming can work very efficiently in this domain.Mathematics and StatisticsThe base for Machine Learning is mathematics and statistics. Statistics applied to data help in understanding it in micro detail. Many machine learning models are based on the probability theory and require knowledge of linear algebra, transformations etc. A good understanding of statistics and probability increases the early adoption to Machine Learning domain.Analytical toolsA plethora of analytical tools are available where machine learning models are already implemented and made available for use. Also, these tools are very good for visualization purposes. Tools like IBM Cognos, PowerBI, Tableue etc are important to pursue a career as a  Machine Learning engineer.Machine Learning Algorithms and librariesTo become a master in this domain, one must master the libraries which are provided with various programming languages. The basic understanding of how machine learning algorithms work and are implemented is crucial.Data Modelling for Machine Learning based systemsData lies at the core of any Machine Learning application. So, modelling the data to suit the application of Machine Learning algorithms is an important task. Data modelling experts are the heart of development teams that develop machine learning based systems. SQL based solutions like Oracle, SQL Server, and NoSQL solutions are important for modelling data required for Machine Learning applications. MongoDB, DynamoDB, Riak are some important NOSQL based solutions available to process unstructured data for Machine Learning applications.Other than these skills, there are two other skills that may prove to be beneficial for those planning on a career in the Machine Learning domain:Natural Language processing techniquesFor E-commerce sites, customer feedback is very important and crucial in determining the roadmap of future products. Many customers give reviews for the products that they have used or give suggestions for improvement. These feedbacks and opinions are analyzed to gain more insights about the customers buying habits as well as about the products. This is part of natural language processing using Machine Learning. The likes of Google, Facebook, Twitter are developing machine learning algorithms for Natural Language Processing and are constantly working on improving their solutions. Knowledge of basics of Natural Language Processing techniques and libraries is must in the domain of Machine Learning.Image ProcessingKnowledge of Image and Video processing is very crucial when a solution is required to be developed in the area of security, weather forecasting, crop prediction etc. Machine Learning based solutions are very effective in these domains. Tools like Matlab, Octave, OpenCV are some important tools available to develop Machine Learning based solutions which require image or video processing.ConclusionMachine Learning is a technique to automate the tasks based on past experiences. This is among the most lucrative career choices right now and will continue to remain so in the future. Job opportunities are increasing day by day in this domain. Acquiring the right skills by opting for a proper Machine Learning course is important to grow in this domain. You can have an impressive career trajectory as a machine learning expert, provided you have the right skills and expertise.
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Why Should You Start a Career in Machine Learning?

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Types of Probability Distributions Every Data Science Expert Should know

Data Science has become one of the most popular interdisciplinary fields. It uses scientific approaches, methods, algorithms, and operations to obtain facts and insights from unstructured, semi-structured, and structured datasets. Organizations use these collected facts and insights for efficient production, business growth, and to predict user requirements. Probability distribution plays a significant role in performing data analysis equipping a dataset for training a model. In this article, you will learn about the types of Probability Distribution, random variables, types of discrete distributions, and continuous distribution.  What is Probability Distribution? A Probability Distribution is a statistical method that determines all the probable values and possibilities that a random variable can deliver from a particular range. This range of values will have a lower bound and an upper bound, which we call the minimum and the maximum possible values.  Various factors on which plotting of a value depends are standard deviation, mean (or average), skewness, and kurtosis. All of these play a significant role in Data science as well. We can use probability distribution in physics, engineering, finance, data analysis, machine learning, etc. Significance of Probability distributions in Data Science In a way, most of the data science and machine learning operations are dependent on several assumptions about the probability of your data. Probability distribution allows a skilled data analyst to recognize and comprehend patterns from large data sets; that is, otherwise, entirely random variables and values. Thus, it makes probability distribution a toolkit based on which we can summarize a large data set. The density function and distribution techniques can also help in plotting data, thus supporting data analysts to visualize data and extract meaning. General Properties of Probability Distributions Probability distribution determines the likelihood of any outcome. The mathematical expression takes a specific value of x and shows the possibility of a random variable with p(x). Some general properties of the probability distribution are – The total of all probabilities for any possible value becomes equal to 1. In a probability distribution, the possibility of finding any specific value or a range of values must lie between 0 and 1. Probability distributions tell us the dispersal of the values from the random variable. Consequently, the type of variable also helps determine the type of probability distribution.Common Data Types Before jumping directly into explaining the different probability distributions, let us first understand the different types of probability distributions or the main categories of the probability distribution. Data analysts and data engineers have to deal with a broad spectrum of data, such as text, numerical, image, audio, voice, and many more. Each of these have a specific means to be represented and analyzed. Data in a probability distribution can either be discrete or continuous. Numerical data especially takes one of the two forms. Discrete data: They take specific values where the outcome of the data remains fixed. Like, for example, the consequence of rolling two dice or the number of overs in a T-20 match. In the first case, the result lies between 2 and 12. In the second case, the event will be less than 20. Different types of discrete distributions that use discrete data are: Binomial Distribution Hypergeometric Distribution Geometric Distribution Poisson Distribution Negative Binomial Distribution Multinomial Distribution  Continuous data: It can obtain any value irrespective of bound or limit. Example: weight, height, any trigonometric value, age, etc. Different types of continuous distributions that use continuous data are: Beta distribution Cauchy distribution Exponential distribution Gamma distribution Logistic distribution Weibull distribution Types of Probability Distribution explained Here are some of the popular types of Probability distributions used by data science professionals. (Try all the code using Jupyter Notebook) Normal Distribution: It is also known as Gaussian distribution. It is one of the simplest types of continuous distribution. This probability distribution is symmetrical around its mean value. It also shows that data at close proximity of the mean is frequently occurring, compared to data that is away from it. Here, mean = 0, variance = finite valueHere, you can see 0 at the center is the Normal Distribution for different mean and variance values. Here is a code example showing the use of Normal Distribution: from scipy.stats import norm  import matplotlib.pyplot as mpl  import numpy as np  def normalDist() -> None:      fig, ax = mpl.subplots(1, 1)      mean, var, skew, kurt = norm.stats(moments = 'mvsk')      x = np.linspace(norm.ppf(0.01),  norm.ppf(0.99), 100)      ax.plot(x, norm.pdf(x),          'r-', lw = 5, alpha = 0.6, label = 'norm pdf')      ax.plot(x, norm.cdf(x),          'b-', lw = 5, alpha = 0.6, label = 'norm cdf')      vals = norm.ppf([0.001, 0.5, 0.999])      np.allclose([0.001, 0.5, 0.999], norm.cdf(vals))      r = norm.rvs(size = 1000)      ax.hist(r, normed = True, histtype = 'stepfilled', alpha = 0.2)      ax.legend(loc = 'best', frameon = False)      mpl.show()  normalDist() Output: Bernoulli Distribution: It is the simplest type of probability distribution. It is a particular case of Binomial distribution, where n=1. It means a binomial distribution takes 'n' number of trials, where n > 1 whereas, the Bernoulli distribution takes only a single trial.   Probability Mass Function of a Bernoulli’s Distribution is:  where p = probability of success and q = probability of failureHere is a code example showing the use of Bernoulli Distribution: from scipy.stats import bernoulli  import seaborn as sb    def bernoulliDist():      data_bern = bernoulli.rvs(size=1200, p = 0.7)      ax = sb.distplot(          data_bern,           kde = True,           color = 'g',           hist_kws = {'alpha' : 1},          kde_kws = {'color': 'y', 'lw': 3, 'label': 'KDE'})      ax.set(xlabel = 'Bernouli Values', ylabel = 'Frequency Distribution')  bernoulliDist() Output:Continuous Uniform Distribution: In this type of continuous distribution, all outcomes are equally possible; each variable gets the same probability of hit as a consequence. This symmetric probabilistic distribution has random variables at an equal interval, with the probability of 1/(b-a). Here is a code example showing the use of Uniform Distribution: from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def uniformDist():      sb.distplot(random.uniform(size = 1200), hist = True)      mpl.show()  uniformDist() Output: Log-Normal Distribution: A Log-Normal distribution is another type of continuous distribution of logarithmic values that form a normal distribution. We can transform a log-normal distribution into a normal distribution. Here is a code example showing the use of Log-Normal Distribution import matplotlib.pyplot as mpl  def lognormalDist():      muu, sig = 3, 1      s = np.random.lognormal(muu, sig, 1000)      cnt, bins, ignored = mpl.hist(s, 80, normed = True, align ='mid', color = 'y')      x = np.linspace(min(bins), max(bins), 10000)      calc = (np.exp( -(np.log(x) - muu) **2 / (2 * sig**2))             / (x * sig * np.sqrt(2 * np.pi)))      mpl.plot(x, calc, linewidth = 2.5, color = 'g')      mpl.axis('tight')      mpl.show()  lognormalDist() Output: Pareto Distribution: It is one of the most critical types of continuous distribution. The Pareto Distribution is a skewed statistical distribution that uses power-law to describe quality control, scientific, social, geophysical, actuarial, and many other types of observable phenomena. The distribution shows slow or heavy-decaying tails in the plot, where much of the data reside at its extreme end. Here is a code example showing the use of Pareto Distribution – import numpy as np  from matplotlib import pyplot as plt  from scipy.stats import pareto  def paretoDist():      xm = 1.5        alp = [2, 4, 6]       x = np.linspace(0, 4, 800)      output = np.array([pareto.pdf(x, scale = xm, b = a) for a in alp])      plt.plot(x, output.T)      plt.show()  paretoDist() Output:Exponential Distribution: It is a type of continuous distribution that determines the time elapsed between events (in a Poisson process). Let’s suppose, that you have the Poisson distribution model that holds the number of events happening in a given period. We can model the time between each birth using an exponential distribution.Here is a code example showing the use of Pareto Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def expDist():      sb.distplot(random.exponential(size = 1200), hist = True)      mpl.show()   expDist()Output:Types of the Discrete probability distribution – There are various types of Discrete Probability Distribution a Data science aspirant should know about. Some of them are – Binomial Distribution: It is one of the popular discrete distributions that determine the probability of x success in the 'n' trial. We can use Binomial distribution in situations where we want to extract the probability of SUCCESS or FAILURE from an experiment or survey which went through multiple repetitions. A Binomial distribution holds a fixed number of trials. Also, a binomial event should be independent, and the probability of obtaining failure or success should remain the same. Here is a code example showing the use of Binomial Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb    def binomialDist():      sb.distplot(random.normal(loc = 50, scale = 6, size = 1200), hist = False, label = 'normal')      sb.distplot(random.binomial(n = 100, p = 0.6, size = 1200), hist = False, label = 'binomial')      plt.show()    binomialDist() Output:Geometric Distribution: The geometric probability distribution is one of the crucial types of continuous distributions that determine the probability of any event having likelihood ‘p’ and will happen (occur) after 'n' number of Bernoulli trials. Here 'n' is a discrete random variable. In this distribution, the experiment goes on until we encounter either a success or a failure. The experiment does not depend on the number of trials. Here is a code example showing the use of Geometric Distribution – import matplotlib.pyplot as mpl  def probability_to_occur_at(attempt, probability):      return (1-p)**(attempt - 1) * probability  p = 0.3  attempt = 4  attempts_to_show = range(21)[1:]  print('Possibility that this event will occur on the 7th try: ', probability_to_occur_at(attempt, p))  mpl.xlabel('Number of Trials')  mpl.ylabel('Probability of the Event')  barlist = mpl.bar(attempts_to_show, height=[probability_to_occur_at(x, p) for x in attempts_to_show], tick_label=attempts_to_show)  barlist[attempt].set_color('g')  mpl.show() Output:Poisson Distribution: Poisson distribution is one of the popular types of discrete distribution that shows how many times an event has the possibility of occurrence in a specific set of time. We can obtain this by limiting the Bernoulli distribution from 0 to infinity. Data analysts often use the Poisson distributions to comprehend independent events occurring at a steady rate in a given time interval. Here is a code example showing the use of Poisson Distribution from scipy.stats import poisson  import seaborn as sb  import numpy as np  import matplotlib.pyplot as mpl  def poissonDist():       mpl.figure(figsize = (10, 10))      data_binom = poisson.rvs(mu = 3, size = 5000)      ax = sb.distplot(data_binom, kde=True, color = 'g',                       bins=np.arange(data_binom.min(), data_binom.max() + 1),                       kde_kws={'color': 'y', 'lw': 4, 'label': 'KDE'})      ax.set(xlabel = 'Poisson Distribution', ylabel='Data Frequency')      mpl.show()      poissonDist() Output:Multinomial Distribution: A multinomial distribution is another popular type of discrete probability distribution that calculates the outcome of an event having two or more variables. The term multi means more than one. The Binomial distribution is a particular type of multinomial distribution with two possible outcomes - true/false or heads/tails. Here is a code example showing the use of Multinomial Distribution – import numpy as np  import matplotlib.pyplot as mpl  np.random.seed(99)   n = 12                      pvalue = [0.3, 0.46, 0.22]     s = []  p = []     for size in np.logspace(2, 3):      outcomes = np.random.multinomial(n, pvalue, size=int(size))        prob = sum((outcomes[:,0] == 7) & (outcomes[:,1] == 2) & (outcomes[:,2] == 3))/len(outcomes)      p.append(prob)      s.append(int(size))  fig1 = mpl.figure()  mpl.plot(s, p, 'o-')  mpl.plot(s, [0.0248]*len(s), '--r')  mpl.grid()  mpl.xlim(xmin = 0)  mpl.xlabel('Number of Events')  mpl.ylabel('Function p(X = K)') Output:Negative Binomial Distribution: It is also a type of discrete probability distribution for random variables having negative binomial events. It is also known as the Pascal distribution, where the random variable tells us the number of repeated trials produced during a specific number of experiments.  Here is a code example showing the use of Negative Binomial Distribution – import matplotlib.pyplot as mpl   import numpy as np   from scipy.stats import nbinom    x = np.linspace(0, 6, 70)   gr, kr = 0.3, 0.7        g = nbinom.ppf(x, gr, kr)   s = nbinom.pmf(x, gr, kr)   mpl.plot(x, g, "*", x, s, "r--") Output: Apart from these mentioned distribution types, various other types of probability distributions exist that data science professionals can use to extract reliable datasets. In the next topic, we will understand some interconnections & relationships between various types of probability distributions. Relationship between various Probability distributions – It is surprising to see that different types of probability distributions are interconnected. In the chart shown below, the dashed line is for limited connections between two families of distribution, whereas the solid lines show the exact relationship between them in terms of transformation, variable, type, etc. Conclusion  Probability distributions are prevalent among data analysts and data science professionals because of their wide usage. Today, companies and enterprises hire data science professionals in many sectors, namely, computer science, health, insurance, engineering, and even social science, where probability distributions appear as fundamental tools for application. It is essential for Data analysts and data scientists. to know the core of statistics. Probability Distributions perform a requisite role in analyzing data and cooking a dataset to train the algorithms efficiently. If you want to learn more about data science - particularly probability distributions and their uses, check out KnowledgeHut's comprehensive Data science course. 
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Types of Probability Distributions Every Data Scie...

Data Science has become one of the most popular in... Read More