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Top 38 Machine Code Coders on 29 Jan 2020 on Toogit. Machine Code Coders on Toogit are highly skilled and talented. Hiring Machine Code Coders on Toogit is quite affordable as compared to a full-time employee and you can save upto 50% in business cost by hiring Machine Code Coders on Toogit. Hiring Machine Code Coders on Toogit is 100% safe as the money is released to the Freelancer only after you are 100% satisfied with the work.

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Omkar Analyst, India
$5 /hr
3 Years Exp.
0 Followers
• I am an Analyst with 3.1 years’ of experience in analytics field. • Strong in Machine learning, Predictive modelling techniques, Data mining, Stati...Read More
Telha Programmer, Pakistan
$5 /hr
1 Years Exp.
0 Followers
I am a Software Engineer with more than 2 years of experience in freelancing. I can develop mobile applications, desktop applications, and websites.
Bhawna Y.Penetration tester, machine learning, India
$5 /hr
2 Years Exp.
0 Followers
I am a Cybersecurity enthusiast who has been working from the past 2 years on projects and Research work .i have certification of Ethical hacking, mac...Read More
Arthur J.Data Scientist, Philippines
$1 /hr
10 Years Exp.
0 Followers
I am an experienced full-stack dev and Data Scientist with 10+ years in Dev work. My skills range from Spreadsheets to Data Science to Databases and t...Read More
Hash B.Machine learning engineer, India
$100 /hr
2 Years Exp.
0 Followers
I am a self tought machine learning developer, having 2 years of experience
Elizabeth S.Data Scientist, India
$34 /hr
4 Years Exp.
0 Followers
I am a data scientist working with an Analytics firm. I have worked on retail analytics which involved assortment planning and buy optimization, promo...Read More
Harsh K.Advanced analytics Consultant, India
$4 /hr
4 Years Exp.
0 Followers
I specialise in Machine learning in analytics. I have 4+ years of experience in Analytics domain. Looking for interesting jobs here.
Ronaldo Program developer, Kenya
$10 /hr
5 Years Exp.
1 Followers
I am certified html developer with prior knowledge in machine learning
Vivian O.Data Scientist, Kenya
$40 /hr
1 Years Exp.
0 Followers
I am a self taught data Scientist profecient with all data analysis techniques to deal with big data and machine learning models incorparation into s...Read More
Raviteja K.Lead Data Scientist, India
$12 /hr
8 Years Exp.
0 Followers
I am an experienced developer in the data science and automation using python and R with expertise in Natural language processing, image processing us...Read More
Syed F.Data Scientist, Pakistan
$25 /hr
1 Years Exp.
0 Followers
Data scientist, who works for credit scoring, risk scoring and persona decoding in bank
Pratik S.Computer Vision Deep learning, India
$5 /hr
1 Years Exp.
0 Followers
I am a Deep Vision enthusiast and I have worked on computer vision and deep learning tasks like Image captioning,image segmentation,keypoint detection...Read More
Interested and keen to design and develop innovative solutions for problems in business using my technical, managerial and analytical skills. Here...Read More
Shubhrajyoti S.Software engineer and data scientist, India
$9 /hr
2 Years Exp.
0 Followers
I am a multi language programmer with several years of practical experience. Artificial intelligence and Data science are also my well known domains.
Rithwik K.Data scientist, India
$9 /hr
0 Years Exp.
0 Followers
I am data-science and machine learning enthusiast from IIT Gandhinagar, a top-tier institute in India. I have been working with data science and machi...Read More
Vishal B.Deep Learning Engineer, India
$17 /hr
3 Years Exp.
0 Followers
Have worked on music composition using AI, object classification, detection, car steering prediction, face detection and recognition, industrial defec...Read More
Lesi S.Software Developer, Nigeria
$20 /hr
2 Years Exp.
0 Followers
A Python developer with experience in using flask and django frameworks. I have also build web API's to consume machine learning models
Gv. T.Business analyst, India
$22 /hr
1 Years Exp.
0 Followers
I am certified as tableau expert and with an work experience of 1 year in tableau. I am good at creating interactive dashboards mad report builders an...Read More
Syeda F.Data Scientist, Pakistan
$15 /hr
0 Years Exp.
0 Followers
I am working as an R&D Engineer in Data Science team. I have extensive experience in machine learning using python.
Raman K.Developer, India
$10 /hr
2 Years Exp.
0 Followers
I am a passionate developer, skilled in programming. I know different languages such as C, c++ , java, python. I have given internships as a lecturer...Read More
Amol U.Product Engineer, India
$40 /hr
10 Years Exp.
0 Followers
Expert at building data engineering products
I am having 7+ years of experience in software development, Data Science, Artificial intelligence, Machine Learning and having hands-on experience on...Read More
I have 4.7 years of Experience as senior software engineer in Digital assurance Centre of Excellence (Research & Development team). I have build 8...Read More
Noman K.Jr.Data scientist, Pakistan
$7 /hr
1 Years Exp.
0 Followers
An experienced individual with strong statistical analytic capabilities and the ability to work with a variety of data environments. Looking to apply...Read More
Melvin L.Python Developer and Machine Learning Expert, India
$7 /hr
5 Years Exp.
0 Followers
I am a Python Developer with 5+years of experience.
Omar S.Data Engineer (GCP Certified) | Machine Learning, Malaysia
$60 /hr
2 Years Exp.
0 Followers
I am a certified Professional Data Engineer with multiple years of experience working with GCP. Building end-to-end machine learning solution is my fo...Read More
Azeez S.DATA SCIENTIST, Nigeria
$3 /hr
3 Years Exp.
0 Followers
I am a certified data scientist with 3 years experience
Neeshu A.Data Engineer and NLP Enthusiast, India
$20 /hr
4 Years Exp.
0 Followers
Product Owner with the data science solutions development experience of 4+ years, ML and NLP enthusiast
Vasim M.Professio Design Engineer, India
$12 /hr
5 Years Exp.
0 Followers
I am CSWA certified solidworks design engineer working with machine manufacturer company.I am having good knowledge in the field of product developmen...Read More
Sumeet Lead Data Scientist, India
$10 /hr
3 Years Exp.
0 Followers
Highly interested in the practical applications of data modeling and optimization techniques & help organizations leverage data. Having an experie...Read More
Kavitha C.Senior Software Developer, India
$8 /hr
6 Years Exp.
0 Followers
I am Android Professional with Gaming in Unity,Machine Learning experience.
Abdulhafeez A.Software Engineer , Nigeria
$25 /hr
4 Years Exp.
0 Followers
I am a Backend & Machine learning software Engineer with 4 years of experience working with local startups in a fast paced environment. I also dab...Read More
Venkatsagar Data scientist, India
/hr
1 Years Exp.
0 Followers
i am a Data Scientist with 1 year experience working on own projects.
Hari Preethy Software Engineer, India
$0 /hr
1 Years Exp.
0 Followers
I am Well versed in Python, SQL and Dot Net, having 1+ yrs experience.
Lhen De Guzman Data Science Scholar, Philippines
$8 /hr
1 Years Exp.
0 Followers
Lhen is a Data Science scholar at FTW Foundation. She is an experienced research analyst with a Bachelor's degree in Secondary Education major in...Read More
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Articles Related To Machine Code


Python is one of the fastest growing programming languages. It has undergone more than 28 years of the successful span. Python itself reveals its success story and a promising future ahead. Python programming language is presently being used by a number of high traffic websites including Google, Yahoo Groups, Yahoo Maps, Shopzilla, Web Therapy, Facebook, NASA, Nokia, IBM, SGI Inc, Quora, Dropbox, Instagram and Youtube. Similarly, Python also discovers a countless use for creating gaming, financial, scientific and instructive applications.

 

Python is a fast, flexible, and powerful programing language that's freely available and used in many application domains. Python is known for its clear syntax, concise code, fast process, and cross-platform compatibility.

 

Python is considered to be in the first place in the list of all AI and machine learning development languages due to the simplicity. The syntaxes belonging to python are terribly easy and can be easily learn. Therefore, several AI algorithms will be easily implemented in it. Python takes short development time as compared to different languages like Java, C++ or Ruby. Python supports object oriented, functional as well as procedure oriented styles of programming. There are lots of libraries in python that make our tasks easier.

 

Some technologies relying on python:

Python has become the core language as far as the success of following technologies is concerned. Let’s dive into the technologies which use python as a core element for research, production and further developments.

 

  1. Networking: Networking is another field in which python has a brighter scope in the future. Python programming language is used to read, write and configure routers and switches and perform other networking automation tasks in a cost-effective and secure manner.
  2. Big Data: The future scope of python programming language can also be predicted by the way it has helped big data technology to grow. Python has been successfully contributing in analyzing a large number of data sets across computer clusters through its high-performance toolkits and libraries.
  3. Artificial Intelligence (AI): There are plenty of python frameworks, libraries, and tools that are specifically developed to direct Artificial Intelligence to reduce human efforts with increased accuracy and efficiency for various development purposes. It is only the Artificial Intelligence that has made it possible to develop speech recognition system, interpreting data like images, videos etc.

 

Why Choose Python for Artificial Intelligence and Machine Learning?

Whether a startup or associate MNC, Python provides a large list of benefits to all. The usage of Python is specified it cannot be restricted to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complicated processes like artificial intelligence (AI), Machine Learning (ML), natural language process, data science etc. The question is why Python is gaining such momentum in AI? And therefore the answer lies below:

 

Flexibility: Flexibility is one of the core advantages of Python. With the option to choose between OOPs approach and scripting, Python is suitable for every purpose. It works as a perfect backend and it also suitable for linking different data structures together.

 

Platform agnostic: Python provides developer with the flexibility to provide an API from the existing programming language. Python is also platform independent, with just minor changes in the source codes, you can get your project or application up and running on different operating systems.

 

Support: Python is a completely open source with a great community. There is a host of resources available which can get any developer up to speed in no time. Not to forget, there is a huge community of active coders willing to help programmers in every stage of developing cycle.

 

Prebuilt Libraries: Python has a lot of libraries for every need of your AI project. Few names include Numpy for scientific computation, Scipy for advanced computing and Pybrain for machine learning.

 

Less Code: Python provides ease of testing - one of the best among competitors. Python helps in easy writing and execution of codes. Python can implement the same logic with as much as 1/5th code as compared to other OOPs languages.

 

Applications of Python:

There are so many applications of Python in the real world. But over time we’ve seen that there are three main applications for Python

Web Development: Web frameworks that are based on Python like Django and Flask have recently become very popular for web development.

Data Science (including Machine Learning): Machine Learning with Python has made it possible to recognize images, videos, speech recognition and much more.

Data Analysis/Visualization: Python is also better for data manipulation and repeated tasks. Python helps in the analysis of a large amount of data through its high-performance libraries and tools. One of the most popular Python libraries for the data visualization is Matplotlib.

 

As you know, JavaScript is the top programming language in the world, the language of the web, of mobile hybrid apps (like PhoneGap or Appcelerator), of the server side (like NodeJS or Wakanda) and has many other implementations. It’s also the starting point for many new developers to the world of programming, as it can be used to display a simple alert in the web browser but also to control a robot (using nodebot, or nodruino). The developers who master JavaScript and write organized and performant code have become the most sought after in the job market.

 

In this article, I’ll share a set of JavaScript tips, tricks and best practices that should be known by all JavaScript developers regardless of their browser/engine or the SSJS (Server Side JavaScript) interpreter.

 

Don’t forget var” keyword when assigning a variable’s value for the first time.

Assignment to an undeclared variable automatically results in a global variable being created. Avoid global variables.

Use “===” instead of “==”

The == (or !=) operator performs an automatic type conversion if needed. The === (or !==) operator will not perform any conversion. It compares the value and the type, which could be considered faster than ==

[10] === 10    // is false

[10]  == 10    // is true

'10' == 10     // is true

'10' === 10    // is false

 []   == 0     // is true

 [] ===  0     // is false

 '' == false   // is true but true == "a" is false

 '' ===   false // is false 

undefined, null, 0, false, NaN, '' (empty string) are all falsy.

 

Use Semicolons for line termination

The use of semi-colons for line termination is a good practice. You won’t be warned if you forget it, because in most cases it will be inserted by the JavaScript parser. For more details about why you should use semi-colons.

 

Create an object constructor

function Person(firstName, lastName){

    this.firstName =  firstName;

    this.lastName = lastName;        

}  

var Khalid = new Person("Khalid", "Ansari");

 

Be careful when using typeof, instanceof and constructor.

typeof: a JavaScript unary operator used to return a string that represents the primitive type of a variable, don’t forget that typeof null will return “object”, and for the majority of object types (Array, Date, and others) will return also “object”.

constructor: is a property of the internal prototype property, which could be overridden by code.

 

instanceof: is another JavaScript operator that check in all the prototypes chain the constructor it returns true if it’s found and false if not.

 

var arr = ["a", "b", "c"];

typeof arr;   // return "object" 

arr  instanceof Array // true

arr.constructor();  //[]

 

Define a Self-calling Function

This is often called a Self-Invoked Anonymous Function or Immediately Invoked Function Expression (IIFE). It is a function that executes automatically when you create it, If you want to use this function you can write in the following way: 

 

(function(){

    // some private code that will be executed automatically

})();  

(function(p,q){

    var r = p+q;

    return r;

})(40,50);

 

Get a random item from an array

var items_array = [12, 548 , 'a' , 2 , 5478 , 'toogit' , 8852, , 'freelance' , 2145 , 119];

var  randomItem = items[Math.floor(Math.random() * items.length)];

 

Get a random number in a specific range

This code snippet can be useful when trying to generate fake data for testing purposes, such as a salary between min and max.

var x = Math.floor(Math.random() * (max - min + 1)) + min;

 

Generate an array of numbers with numbers from 0 to max

var numbersArray = [] , max = 100;

for( var i=1; numbersArray.push(i++) < max;);  // numbers = [1,2,3 ... 100] 

 

Generate a random set of alphanumeric characters

function generateRandomAlphaNum(len) {

    var rdmString = "";

    for( ; rdmString.length < len; rdmString  += Math.random().toString(36).substr(2));

    return  rdmString.substr(0, len);

}

 

Shuffle an array of numbers

var numbers = [5, 458 , 120 , -215 , 228 , 400 , 122205, -85411];

numbers = numbers.sort(function(){ return Math.random() - 0.5});

 

A better option could be to implement a random sort order by code (e.g. : Fisher-Yates shuffle), than using the native sort JavaScript function

 

A string trim function

The classic trim function of Java, C#, PHP and many other language that remove whitespace from a string doesn’t exist in JavaScript, so we could add it to the String object.

String.prototype.trim = function(){return this.replace(/^s+|s+$/g, "");};  

A native implementation of the trim() function is available in the recent JavaScript engines.

 

Append an array to another array

var array1 = [12 , "foo" , {name "Joe"} , -2458];

var array2 = ["Doe" , 555 , 100];

Array.prototype.push.apply(array1, array2);

 

Transform the arguments object into an array

var argArray = Array.prototype.slice.call(arguments);

 

Verify that a given argument is a number

function isNumber(n){

    return !isNaN(parseFloat(n)) && isFinite(n);

}

 

Verify that a given argument is an array

function isArray(obj){

    return Object.prototype.toString.call(obj) === '[object Array]' ;

}

Note that if the toString() method is overridden, you will not get the expected result using this trick.

Or Use..

Array.isArray(obj); // its a new Array method

You could also use instanceofif you are not working with multiple frames. However, if you have many contexts, you will get a wrong result.

var myFrame = document.createElement('iframe');

document.body.appendChild(myFrame);

var myArray = window.frames[window.frames.length-1].Array;

var arr = new myArray(a,b,10); // [a,b,10]  

// instanceof will not work correctly, myArray loses his constructor 

// constructor is not shared between frames

arr instanceof Array; // false

 

Get the max or the min in an array of numbers

var  numbers = [5, 458 , 120 , -215 , 228 , 400 , 122205, -85411]; 

var maxInNumbers = Math.max.apply(Math, numbers); 

var minInNumbers = Math.min.apply(Math, numbers);

 

Empty an array

var myArray = [12 , 222 , 1000 ];  

myArray.length = 0; // myArray will be equal to [].

 

Don’t use delete to remove an item from array

Use splice instead of using delete to delete an item from an array. Using delete replaces the item with undefined instead of the removing it from the array.

Instead of…

var items = [12, 548 ,'a' , 2 , 5478 , 'foo' , 8852, , 'Doe' ,2154 , 119 ]; 

items.length; // return 11 

delete items[3]; // return true 

items.length; // return 11 

Use

var items = [12, 548 ,'a' , 2 , 5478 , 'foo' , 8852, , 'Doe' ,2154 , 119 ]; 

items.length; // return 11 

items.splice(3,1) ; 

items.length; // return 10 

 

Clearing or truncating an array

An easy way of clearing or truncating an array without reassigning it is by changing its length property value:

const arr = [11,22,33,44,55,66];

// truncanting

arr.length = 3;

console.log(arr); //=> [11, 22, 33]

// clearing

arr.length = 0;

console.log(arr); //=> []

console.log(arr[2]); //=> undefined

 

Simulating named parameters with object destructuring

Chances are high that you’re already using configuration objects when you need to pass a variable set of options to some function, like this:

doSomething({ foo: 'Hello', bar: 'Toogit!', baz: 42 });

function doSomething(config) {  

const foo = config.foo !== undefined ? config.foo : 'Hi';  const bar = config.bar !== undefined ? config.bar : 'Me!';  const baz = config.baz !== undefined ? config.baz : 13;  // ...

}

This is an old but effective pattern, which tries to simulate named parameters in JavaScript. The function calling looks fine. On the other hand, the config object handling logic is unnecessarily verbose. With ES2015 object destructuring, you can circumvent this downside:

function doSomething({ foo = 'Hello', bar = 'Toogit!', baz = 13 }) {  // ...}

And if you need to make the config object optional, it’s very simple, too:

function doSomething({ foo = 'Hello', bar = 'Toogit!', baz = 13 } = {}) {  // ...}

 

Object destructuring for array items

Assign array items to individual variables with object destructuring:

const csvFileLine = '1997,John Doe,US,john@doe.com,New York';const { 2: country, 4: state } = csvFileLine.split(',');

 

 

What is the difference between Java and JavaScript?

 

These are two different programming languages.

 

Javascript is a language that has gained tremendous popularity as a language on the web browsers to create dynamic and interactive web pages.

 

Java is a language that has got a similar popularity when you build a “backend” system, which is a fancy word for “almost anything”.

 

Despite the common prefix, they are not related; there creators are different and so are their origin stories (as highlighted by other answers). 

- JavaScript is a genius marketing scam that polluted the world of browsers exceptionally well. The browser reads JavaScript’s code line by line and executes it.

 

- Java is a general purpose language that is used almost everywhere, from Android mobile apps and cryptography to OS and cloud computing. Java’s code is stored in bytecoded format and then gets JIT compiled before the actual execution. In other words, it translates the bytecode to machine code.

 

- Java is class based. JS is prototype based. All objects, like Array or Function inherit from the Object.prototype which remains on top of the chain.

 

- JavaScript uses dynamic type checking (checks the variables while the code executes), unlike Java’s static checking system (variables are verified at compile time), which is more bug free.

 

- The word “Script.” It’s a joke, in case you didn’t get it.

 

 

NLP is a branch of data science that consists of systematic processes for analyzing, understanding, and deriving information from the text information in a smart and efficient manner. By utilizing NLP and its parts, one can organize the massive chunks of text information, perform various automated tasks and solve a wide range of issues like – automatic summarization, machine translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation etc.

 

NLTK (Natural Language Toolkit) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to lexical resources like WordNet, along with a collection of text processing libraries for classification, tokenization, stemming, and tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.

 

NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.”

 

Downloading and installing NLTK

  1. Install NLTK: run pip install nltk
  2. Test installation: run python then type import nltk and run nltk.download() and download all packages.

 

Pre-Processing with NLTK

The main issue with text data is that it's all in text format. However, the Machine learning algorithms need some variety of numerical feature vector so as to perform the task. Thus before we have a tendency to begin with any NLP project we'd like to pre-process it to form it ideal for working. Basic text pre-processing includes:

 

  • Converting the whole text into uppercase or lowercase, in order that the algorithm doesn't treat the same words completely different in several cases.
  • Tokenization: Process of converting the normal text strings into a list of tokens i.e. words that we actually want. The NLTK data package includes a pre-trained Punkt tokenizer for English.

 

           import nltk

           from nltk.tokenize import word_tokenize

           text = "God is Great! I won a lottery."

           print(word_tokenize(text))

           Output: ['God', 'is', 'Great', '!', 'I', 'won', 'a', 'lottery', '.']

 

  • Noise removal: Process of removing everything that isn’t in a standard number or letter.
  • Stop word removal: A stop word is a commonly used word (such as “the”, “a”, “an”, “in”). We would not want these words or taking up valuable processing time. For this, we can remove them easily, by storing a list of words that you consider to be stop words. NLTK (Natural Language Toolkit) in python has a list of stopwords stored in sixteen different languages. You can find them in the nltk_data directory.  home/Saad/nltk_data/corpora/stopwords is the directory address.

           import nltk

           from nltk.corpus import stopwords

           set(stopwords.words('english'))

 

  • Stemming: Stemming is the process of reducing the words to its root form. Example if we were to stem the following words: “Connects”, “Connecting”, “Connected”, “and Connection”, the result would be a single word “Connect”.

           # import these modules

           from nltk.stem import PorterStemmer

           from nltk.tokenize import word_tokenize   

           ps = PorterStemmer()  

           # choose some words to be stemmed

           words = ["Connect", "Connects", “Connected”, "Connecting", "Connection", "Connections"]

 

           for w in words:

           print(w, " : ", ps.stem(w)) 

 

  • Lemmatization: Lemmatization is the process of grouping along the various inflected forms of a word in order that they may be analyzed as a single item. Lemmatization is similar to stemming but it brings context to the words. Therefore it links words with similar meaning to one word.

           # import these modules

           from nltk.stem import WordNetLemmatizer  

           lemmatizer = WordNetLemmatizer()  

           print("rocks :", lemmatizer.lemmatize("rocks"))

           print("corpora :", lemmatizer.lemmatize("corpora"))  

           # a denotes adjective in "pos"

          print("better :", lemmatizer.lemmatize("better", pos ="a"))

 

          -> rocks : rock

          -> corpora : corpus

          -> better : good

 

Now we need to transform text into a meaningful vector array. This vector array is a representation of text that describes the occurrence of words within a document. For example, if our dictionary contains the words {Learning, is, the, not, great}, and we want to vectorize the text “Learning is great”, we would have the following vector: (1, 1, 0, 0, 1). A problem is that extremely frequent words begin to dominate within the document (e.g. larger score), however might not contain as much informational content. Also, it will offer additional weight to longer documents than shorter documents.

 

One approach is to rescale the frequency of words or the scores for frequent words called Term Frequency-Inverse Document Frequency.

 

  • Term Frequency: is a scoring of the frequency of the word in the current document.

           TF = (Number of times term t appears in a document)/ (Number of terms in the document)

 

  • Inverse Document Frequency: It is a scoring of how rare the word is across documents.

           IDF = 1+log(N/n), where, N is the number of documents and n is the number of documents a term t has appeared in.

 

           Tf-idf weight is a weight often used in information retrieval and text mining.

           Tf-IDF can be implemented in scikit learn as:

 

           from sklearn.feature_extraction.text import TfidfVectorizer

           corpus = [

           ...     'This is the first document.’

           ...     'This document is the second document.’

           ...     'And this is the third one.’

           ...     'Is this the first document?',]

           >>> vectorizer = TfidfVectorizer()

           >>> X = vectorizer.fit_transform(corpus)

           >>> print(vectorizer.get_feature_names())

           ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']

           >>> print(X.shape)

           (4, 9)

 

  • Cosine similarity: TF-IDF is a transformation applied to texts to get two real-valued vectors in vector space. We can then obtain the Cosine similarity of any pair of vectors by taking their dot product and dividing that by the product of their norms. That yields the cosine of the angle between the vectors. Cosine similarity is a measure of similarity between two non-zero vectors.

           Cosine Similarity (d1, d2) =  Dot product(d1, d2) / ||d1|| * ||d2||

 

          import numpy as np

          from sklearn.metrics.pairwise import cosine_similarity

          # vectors

          a = np.array([1,2,3])

          b = np.array([1,1,4])

          # manually compute cosine similarity

          dot = np.dot(a, b)

          norma = np.linalg.norm(a)

          normb = np.linalg.norm(b)

          cos = dot / (norma * normb)

 

After completion of cosine similarity matric we perform algorithmic operation on it for Document similarity calculation, sentiment analysis, topic segmentation etc.

 

I have done my best to make the article simple and interesting for you, hope you found it useful and interesting too.

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