Emilie  Okumu

Emilie Okumu

1668503651

Machine Learning Clustering Algorithms Explanation and Examples

In this Machine Learning article, let's learn about Clustering Algorithms in Machine Learning. Machine Learning problems deal with a great deal of data and depend heavily on the algorithms that are used to train the model. There are various approaches and algorithms to train a machine learning model based on the problem at hand. Supervised and unsupervised learning are the two most prominent of these approaches. An important real-life problem of marketing a product or service to a specific target audience can be easily resolved with the help of a form of unsupervised learning known as Clustering. This article will explain clustering algorithms along with real-life problems and examples. Let us start with understanding what clustering is.

What are Clusters?

The word cluster is derived from an old English word, ‘clyster, ‘ meaning a bunch. A cluster is a group of similar things or people positioned or occurring closely together. Usually, all points in a cluster depict similar characteristics; therefore, machine learning could be used to identify traits and segregate these clusters. This makes the basis of many applications of machine learning that solve data problems across industries.

What is Clustering?

As the name suggests, clustering involves dividing data points into multiple clusters of similar values. In other words, the objective of clustering is to segregate groups with similar traits and bundle them together into different clusters. It is ideally the implementation of human cognitive capability in machines enabling them to recognize different objects and differentiate between them based on their natural properties. Unlike humans, it is very difficult for a machine to identify an apple or an orange unless properly trained on a huge relevant dataset. Unsupervised learning algorithms achieve this training, specifically clustering.  

Simply put, clusters are the collection of data points that have similar values or attributes and clustering algorithms are the methods to group similar data points into different clusters based on their values or attributes. 

For example, the data points clustered together can be considered as one group or cluster. Hence the diagram below has two clusters (differentiated by color for representation). 

Why Clustering? 

When you are working with large datasets, an efficient way to analyze them is to first divide the data into logical groupings, aka clusters. This way, you could extract value from a large set of unstructured data. It helps you to glance through the data to pull out some patterns or structures before going deeper into analyzing the data for specific findings. 

Organizing data into clusters helps identify the data’s underlying structure and finds applications across industries. For example, clustering could be used to classify diseases in the field of medical science and can also be used in customer classification in marketing research. 

In some applications, data partitioning is the final goal. On the other hand, clustering is also a prerequisite to preparing for other artificial intelligence or machine learning problems. It is an efficient technique for knowledge discovery in data in the form of recurring patterns, underlying rules, and more. Try to learn more about clustering in this free course: Customer Segmentation using Clustering

Types of Clustering Methods/ Algorithms

Given the subjective nature of the clustering tasks, there are various algorithms that suit different types of clustering problems. Each problem has a different set of rules that define similarity among two data points, hence it calls for an algorithm that best fits the objective of clustering. Today, there are more than a hundred known machine learning algorithms for clustering.

A few Types of Clustering Algorithms

  • Connectivity Models

As the name indicates, connectivity models tend to classify data points based on their closeness of data points. It is based on the notion that the data points closer to each other depict more similar characteristics compared to those placed farther away. The algorithm supports an extensive hierarchy of clusters that might merge with each other at certain points. It is not limited to a single partitioning of the dataset. 

The choice of distance function is subjective and may vary with each clustering application. There are also two different approaches to addressing a clustering problem with connectivity models. First is where all data points are classified into separate clusters and then aggregated as the distance decreases. The second approach is where the whole dataset is classified as one cluster and then partitioned into multiple clusters as the distance increases. Even though the model is easily interpretable, it lacks the scalability to process bigger datasets. 

  • Distribution Models

Distribution models are based on the probability of all data points in a cluster belonging to the same distribution, i.e., Normal distribution or Gaussian distribution. The slight drawback is that the model is highly prone to suffering from overfitting. A well-known example of this model is the expectation-maximization algorithm.

  • Density Models

These models search the data space for varied densities of data points and isolate the different density regions. It then assigns the data points within the same region as clusters. DBSCAN and OPTICS are the two most common examples of density models. 

  • Centroid Models

Centroid models are iterative clustering algorithms where similarity between data points is derived based on their closeness to the cluster’s centroid. The centroid (center of the cluster) is formed to ensure that the distance of the data points is minimal from the center. The solution for such clustering problems is usually approximated over multiple trials. An example of centroid models is the K-means algorithm. 

Common Clustering Algorithms

K Means clustering with ‘R’

  • Having a glance at the first few records of the dataset using the head() function
head(iris)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa
  • Removing the categorical column ‘Species’ because k-means can be applied only on numerical columns
iris.new<- iris[,c(1,2,3,4)]

head(iris.new)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1          5.1         3.5          1.4         0.2
## 2          4.9         3.0          1.4         0.2
## 3          4.7         3.2          1.3         0.2
## 4          4.6         3.1          1.5         0.2
## 5          5.0         3.6          1.4         0.2
## 6          5.4         3.9          1.7         0.4
  • Making a scree-plot to identify the ideal number of clusters
totWss=rep(0,5)
for(k in 1:5){
  set.seed(100)
  clust=kmeans(x=iris.new, centers=k, nstart=5)
  totWss[k]=clust$tot.withinss
}
plot(c(1:5), totWss, type="b", xlab="Number of Clusters",
     ylab="sum of 'Within groups sum of squares'") 
  • Visualizing the clustering 
library(cluster) 
library(fpc) 

## Warning: package 'fpc' was built under R version 3.6.2

clus <- kmeans(iris.new, centers=3)

plotcluster(iris.new, clus$cluster)clusplot(iris.new, clus$cluster, color=TRUE,shade = T)
  • Adding the clusters to the original dataset
iris.new<-cbind(iris.new,cluster=clus$cluster) 

head(iris.new)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width cluster
## 1          5.1         3.5          1.4         0.2       1
## 2          4.9         3.0          1.4         0.2       1
## 3          4.7         3.2          1.3         0.2       1
## 4          4.6         3.1          1.5         0.2       1
## 5          5.0         3.6          1.4         0.2       1
## 6          5.4         3.9          1.7         0.4       1

Density-Based Spatial Clustering of Applications With Noise (DBSCAN)

DBSCAN is the most common density-based clustering algorithm and is widely used. The algorithm picks an arbitrary starting point, and the neighborhood to this point is extracted using a distance epsilon ‘ε’. All the points that are within the distance epsilon are the neighborhood points. If these points are sufficient in number, then the clustering process starts, and we get our first cluster. If there are not enough neighboring data points, then the first point is labeled noise.

For each point in this first cluster, the neighboring data points (the one which is within the epsilon distance with the respective point) are also added to the same cluster. The process is repeated for each point in the cluster until there are no more data points that can be added. 

Once we are done with the current cluster, an unvisited point is taken as the first data point of the next cluster, and all neighboring points are classified into this cluster. This process is repeated until all points are marked ‘visited’. 

DBSCAN has some advantages as compared to other clustering algorithms:

  1. It does not require a pre-set number of clusters
  2. Identifies outliers as noise
  3. Ability to find arbitrarily shaped and sized clusters easily

Implementing DBSCAN with Python

from sklearn import datasets
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN

iris = datasets.load_iris()
x = iris.data[:, :4]  # we only take the first two features.
DBSC = DBSCAN()
cluster_D = DBSC.fit_predict(x)
print(cluster_D)
plt.scatter(x[:,0],x[:,1],c=cluster_D,cmap='rainbow')
[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 -1  0  0  0  0  0  0
  0  0  1  1  1  1  1  1  1 -1  1  1 -1  1  1  1  1  1  1  1 -1  1  1  1
  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1  1  1  1  1 -1  1  1
  1  1 -1  1  1  1  1  1  1 -1 -1  1 -1 -1  1  1  1  1  1  1  1 -1 -1  1
  1  1 -1  1  1  1  1  1  1  1  1 -1  1  1 -1 -1  1  1  1  1  1  1  1  1
  1  1  1  1  1  1]
<matplotlib.collections.PathCollection at 0x7f38b0c48160>
graph

Hierarchical Clustering 

Hierarchical Clustering is categorized into divisive and agglomerative clustering. Basically, these algorithms have clusters sorted in an order based on the hierarchy in data similarity observations.

Divisive Clustering, or the top-down approach, groups all the data points in a single cluster. Then it divides it into two clusters with the least similarity to each other. The process is repeated, and clusters are divided until there is no more scope for doing so. 

Agglomerative Clustering, or the bottom-up approach, assigns each data point as a cluster and aggregates the most similar clusters. This essentially means bringing similar data together into a cluster. 

Out of the two approaches, Divisive Clustering is more accurate. But then, it again depends on the type of problem and the nature of the available dataset to decide which approach to apply to a specific clustering problem in Machine Learning. 

Implementing Hierarchical Clustering with Python

#Import libraries
from sklearn import datasets
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import AgglomerativeClustering

#import the dataset
iris = datasets.load_iris()
x = iris.data[:, :4]  # we only take the first two features.
hier_clustering = AgglomerativeClustering(3)
clusters_h = hier_clustering.fit_predict(x)
print(clusters_h )
plt.scatter(x[:,0],x[:,1],c=clusters_h ,cmap='rainbow')
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 2 2 0 2 2 2 2
 2 2 0 0 2 2 2 2 0 2 0 2 0 2 2 0 0 2 2 2 2 2 0 0 2 2 2 0 2 2 2 0 2 2 2 0 2
 2 0]
<matplotlib.collections.PathCollection at 0x7f38b0bcbb00>

Applications of Clustering 

Clustering has varied applications across industries and is an effective solution to a plethora of machine learning problems.

  • It is used in market research to characterize and discover a relevant customer bases and audiences.
  • Classifying different species of plants and animals with the help of image recognition techniques
  • It helps in deriving plant and animal taxonomies and classifies genes with similar functionalities to gain insight into structures inherent to populations.
  • It is applicable in city planning to identify groups of houses and other facilities according to their type, value, and geographic coordinates.
  • It also identifies areas of similar land use and classifies them as agricultural, commercial, industrial, residential, etc.
  • Classifies documents on the web for information discovery
  • Applies well as a data mining function to gain insights into data distribution and observe characteristics of different clusters
  • Identifies credit and insurance frauds when used in outlier detection applications
  • Helpful in identifying high-risk zones by studying earthquake-affected areas (applicable for other natural hazards too)
  • A simple application could be in libraries to cluster books based on the topics, genre, and other characteristics
  • An important application is into identifying cancer cells by classifying them against healthy cells
  • Search engines provide search results based on the nearest similar object to a search query using clustering techniques
  • Wireless networks use various clustering algorithms to improve energy consumption and optimise data transmission
  • Hashtags on social media also use clustering techniques to classify all posts with the same hashtag under one stream

In this article, we discussed different clustering algorithms in Machine Learning. While there is so much more to unsupervised learning and machine learning as a whole, this article specifically draws attention to clustering algorithms in Machine Learning and their applications. If you want to learn more about machine learning concepts, head to our blog. Also, if you wish to pursue a career in Machine Learning, then upskill with Great Learning’s PG program in Machine Learning.


Original article source at: https://www.mygreatlearning.com

#machinelearning #algorithm 

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Machine Learning Clustering Algorithms Explanation and Examples
Lawrence  Lesch

Lawrence Lesch

1677668905

TS-mockito: Mocking Library for TypeScript

TS-mockito

Mocking library for TypeScript inspired by http://mockito.org/

1.x to 2.x migration guide

1.x to 2.x migration guide

Main features

  • Strongly typed
  • IDE autocomplete
  • Mock creation (mock) (also abstract classes) #example
  • Spying on real objects (spy) #example
  • Changing mock behavior (when) via:
  • Checking if methods were called with given arguments (verify)
    • anything, notNull, anyString, anyOfClass etc. - for more flexible comparision
    • once, twice, times, atLeast etc. - allows call count verification #example
    • calledBefore, calledAfter - allows call order verification #example
  • Resetting mock (reset, resetCalls) #example, #example
  • Capturing arguments passed to method (capture) #example
  • Recording multiple behaviors #example
  • Readable error messages (ex. 'Expected "convertNumberToString(strictEqual(3))" to be called 2 time(s). But has been called 1 time(s).')

Installation

npm install ts-mockito --save-dev

Usage

Basics

// Creating mock
let mockedFoo:Foo = mock(Foo);

// Getting instance from mock
let foo:Foo = instance(mockedFoo);

// Using instance in source code
foo.getBar(3);
foo.getBar(5);

// Explicit, readable verification
verify(mockedFoo.getBar(3)).called();
verify(mockedFoo.getBar(anything())).called();

Stubbing method calls

// Creating mock
let mockedFoo:Foo = mock(Foo);

// stub method before execution
when(mockedFoo.getBar(3)).thenReturn('three');

// Getting instance
let foo:Foo = instance(mockedFoo);

// prints three
console.log(foo.getBar(3));

// prints null, because "getBar(999)" was not stubbed
console.log(foo.getBar(999));

Stubbing getter value

// Creating mock
let mockedFoo:Foo = mock(Foo);

// stub getter before execution
when(mockedFoo.sampleGetter).thenReturn('three');

// Getting instance
let foo:Foo = instance(mockedFoo);

// prints three
console.log(foo.sampleGetter);

Stubbing property values that have no getters

Syntax is the same as with getter values.

Please note, that stubbing properties that don't have getters only works if Proxy object is available (ES6).

Call count verification

// Creating mock
let mockedFoo:Foo = mock(Foo);

// Getting instance
let foo:Foo = instance(mockedFoo);

// Some calls
foo.getBar(1);
foo.getBar(2);
foo.getBar(2);
foo.getBar(3);

// Call count verification
verify(mockedFoo.getBar(1)).once();               // was called with arg === 1 only once
verify(mockedFoo.getBar(2)).twice();              // was called with arg === 2 exactly two times
verify(mockedFoo.getBar(between(2, 3))).thrice(); // was called with arg between 2-3 exactly three times
verify(mockedFoo.getBar(anyNumber()).times(4);    // was called with any number arg exactly four times
verify(mockedFoo.getBar(2)).atLeast(2);           // was called with arg === 2 min two times
verify(mockedFoo.getBar(anything())).atMost(4);   // was called with any argument max four times
verify(mockedFoo.getBar(4)).never();              // was never called with arg === 4

Call order verification

// Creating mock
let mockedFoo:Foo = mock(Foo);
let mockedBar:Bar = mock(Bar);

// Getting instance
let foo:Foo = instance(mockedFoo);
let bar:Bar = instance(mockedBar);

// Some calls
foo.getBar(1);
bar.getFoo(2);

// Call order verification
verify(mockedFoo.getBar(1)).calledBefore(mockedBar.getFoo(2));    // foo.getBar(1) has been called before bar.getFoo(2)
verify(mockedBar.getFoo(2)).calledAfter(mockedFoo.getBar(1));    // bar.getFoo(2) has been called before foo.getBar(1)
verify(mockedFoo.getBar(1)).calledBefore(mockedBar.getFoo(999999));    // throws error (mockedBar.getFoo(999999) has never been called)

Throwing errors

let mockedFoo:Foo = mock(Foo);

when(mockedFoo.getBar(10)).thenThrow(new Error('fatal error'));

let foo:Foo = instance(mockedFoo);
try {
    foo.getBar(10);
} catch (error:Error) {
    console.log(error.message); // 'fatal error'
}

Custom function

You can also stub method with your own implementation

let mockedFoo:Foo = mock(Foo);
let foo:Foo = instance(mockedFoo);

when(mockedFoo.sumTwoNumbers(anyNumber(), anyNumber())).thenCall((arg1:number, arg2:number) => {
    return arg1 * arg2; 
});

// prints '50' because we've changed sum method implementation to multiply!
console.log(foo.sumTwoNumbers(5, 10));

Resolving / rejecting promises

You can also stub method to resolve / reject promise

let mockedFoo:Foo = mock(Foo);

when(mockedFoo.fetchData("a")).thenResolve({id: "a", value: "Hello world"});
when(mockedFoo.fetchData("b")).thenReject(new Error("b does not exist"));

Resetting mock calls

You can reset just mock call counter

// Creating mock
let mockedFoo:Foo = mock(Foo);

// Getting instance
let foo:Foo = instance(mockedFoo);

// Some calls
foo.getBar(1);
foo.getBar(1);
verify(mockedFoo.getBar(1)).twice();      // getBar with arg "1" has been called twice

// Reset mock
resetCalls(mockedFoo);

// Call count verification
verify(mockedFoo.getBar(1)).never();      // has never been called after reset

You can also reset calls of multiple mocks at once resetCalls(firstMock, secondMock, thirdMock)

Resetting mock

Or reset mock call counter with all stubs

// Creating mock
let mockedFoo:Foo = mock(Foo);
when(mockedFoo.getBar(1)).thenReturn("one").

// Getting instance
let foo:Foo = instance(mockedFoo);

// Some calls
console.log(foo.getBar(1));               // "one" - as defined in stub
console.log(foo.getBar(1));               // "one" - as defined in stub
verify(mockedFoo.getBar(1)).twice();      // getBar with arg "1" has been called twice

// Reset mock
reset(mockedFoo);

// Call count verification
verify(mockedFoo.getBar(1)).never();      // has never been called after reset
console.log(foo.getBar(1));               // null - previously added stub has been removed

You can also reset multiple mocks at once reset(firstMock, secondMock, thirdMock)

Capturing method arguments

let mockedFoo:Foo = mock(Foo);
let foo:Foo = instance(mockedFoo);

// Call method
foo.sumTwoNumbers(1, 2);

// Check first arg captor values
const [firstArg, secondArg] = capture(mockedFoo.sumTwoNumbers).last();
console.log(firstArg);    // prints 1
console.log(secondArg);    // prints 2

You can also get other calls using first(), second(), byCallIndex(3) and more...

Recording multiple behaviors

You can set multiple returning values for same matching values

const mockedFoo:Foo = mock(Foo);

when(mockedFoo.getBar(anyNumber())).thenReturn('one').thenReturn('two').thenReturn('three');

const foo:Foo = instance(mockedFoo);

console.log(foo.getBar(1));    // one
console.log(foo.getBar(1));    // two
console.log(foo.getBar(1));    // three
console.log(foo.getBar(1));    // three - last defined behavior will be repeated infinitely

Another example with specific values

let mockedFoo:Foo = mock(Foo);

when(mockedFoo.getBar(1)).thenReturn('one').thenReturn('another one');
when(mockedFoo.getBar(2)).thenReturn('two');

let foo:Foo = instance(mockedFoo);

console.log(foo.getBar(1));    // one
console.log(foo.getBar(2));    // two
console.log(foo.getBar(1));    // another one
console.log(foo.getBar(1));    // another one - this is last defined behavior for arg '1' so it will be repeated
console.log(foo.getBar(2));    // two
console.log(foo.getBar(2));    // two - this is last defined behavior for arg '2' so it will be repeated

Short notation:

const mockedFoo:Foo = mock(Foo);

// You can specify return values as multiple thenReturn args
when(mockedFoo.getBar(anyNumber())).thenReturn('one', 'two', 'three');

const foo:Foo = instance(mockedFoo);

console.log(foo.getBar(1));    // one
console.log(foo.getBar(1));    // two
console.log(foo.getBar(1));    // three
console.log(foo.getBar(1));    // three - last defined behavior will be repeated infinity

Possible errors:

const mockedFoo:Foo = mock(Foo);

// When multiple matchers, matches same result:
when(mockedFoo.getBar(anyNumber())).thenReturn('one');
when(mockedFoo.getBar(3)).thenReturn('one');

const foo:Foo = instance(mockedFoo);
foo.getBar(3); // MultipleMatchersMatchSameStubError will be thrown, two matchers match same method call

Mocking interfaces

You can mock interfaces too, just instead of passing type to mock function, set mock function generic type Mocking interfaces requires Proxy implementation

let mockedFoo:Foo = mock<FooInterface>(); // instead of mock(FooInterface)
const foo: SampleGeneric<FooInterface> = instance(mockedFoo);

Mocking types

You can mock abstract classes

const mockedFoo: SampleAbstractClass = mock(SampleAbstractClass);
const foo: SampleAbstractClass = instance(mockedFoo);

You can also mock generic classes, but note that generic type is just needed by mock type definition

const mockedFoo: SampleGeneric<SampleInterface> = mock(SampleGeneric);
const foo: SampleGeneric<SampleInterface> = instance(mockedFoo);

Spying on real objects

You can partially mock an existing instance:

const foo: Foo = new Foo();
const spiedFoo = spy(foo);

when(spiedFoo.getBar(3)).thenReturn('one');

console.log(foo.getBar(3)); // 'one'
console.log(foo.getBaz()); // call to a real method

You can spy on plain objects too:

const foo = { bar: () => 42 };
const spiedFoo = spy(foo);

foo.bar();

console.log(capture(spiedFoo.bar).last()); // [42] 

Thanks


Download Details:

Author: NagRock
Source Code: https://github.com/NagRock/ts-mockito 
License: MIT license

#typescript #testing #mock 

sophia tondon

sophia tondon

1620898103

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  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Nora Joy

1607006620

Hire Machine Learning Developer | Hire ML Experts in India

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

#hire machine learning developers in india #hire dedicated machine learning developers in india #hire machine learning programmers in india #hire machine learning programmers #hire dedicated machine learning developers #hire machine learning developers