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Top 34 Uppercase Hr Freelancers on 25 Jun 2019 on Toogit. Uppercase Hr Freelancers on Toogit are highly skilled and talented. Hiring Uppercase Hr Freelancers on Toogit is quite affordable as compared to a full-time employee and you can save upto 50% in business cost by hiring Uppercase Hr Freelancers on Toogit. Hiring Uppercase Hr Freelancers 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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Sharon Kutima finance, Kenya
$3 /hr
1 Years Exp.
I am graduate with commerce pursuing CPA
Ravikant S.UI UX Designer, India
$20 /hr
5 Years Exp.
I have Expertize in Designing UI UX for WebApps, Mobile Apps, Websites and Landing Pages. I have 5+ year of experience with Master of Computer Applic...Read More
Sagar Y.User Experience Designer , India
$33 /hr
3 Years Exp.
An Interaction Designer with a strong affinity for Usability. Believes that a well-designed product is one that is simple to use and yet rigorously de...Read More
Code Complete Business Solution, India
$6 /hr
2 Years Exp.
We are a group of enthusiast technological people who keep completion of work as their first priority. With 10+ successful project and 15+ projects i...Read More
Olga B.UX/UI Designer, Netherlands
$35 /hr
1 Years Exp.
I’m a Amsterdam based UX/UI Designer. My true passion is to create emotions and boost happiness. I help companies make their users interaction exp...Read More
Pravesha J.User Experience Designer and Researcher, India
$10 /hr
6 Years Exp.
I am a UX designer with around 6 years of experience. I have experience on Mobile apps, desktop websites as well as standalone products. I wireframe...Read More
Ayyappan S.Web/Mobile Application Developer, India
$12 /hr
6 Years Exp.
I am a web and mobile application developer with expertise in PHP, PHP based frameworks, MySQL, Javascript and jQuery. I have been offering followi...Read More
Maharshi M.Virtual Assistant, India
1 Years Exp.
Hey! I'm a Mechanical Engineer and active freelancer in User testing in Validately, Test birds and Enrol. I have experience in conducting an Impa...Read More
Raj M.UI Designer, India
$8 /hr
0 Years Exp.
Hi I am UI Designer with 5 year experience. I create Awesome websites for conversion and not just cool fancy looking site. My designs are clean, easie...Read More
Arpit Jain UI/UX Designer, India
$5 /hr
1 Years Exp.
I am a pre-final year undergraduate at Indian Institute of Technology, Guwahati in Department of Design. I have good experience and skill set in User...Read More
Vast Experience in entire software development life cycle and decision-making. Skilled at building effective and productive working relationship with...Read More
Karmesh M.Programmer, India
$26 /hr
2 Years Exp.
Hello. I am a python developer with over 2 years experience. I am new to toogit but carry a good profile on Fiverr. Try my services once, you will nev...Read More
Ajay K.Sr.UX Designer, India
$22 /hr
6 Years Exp.
I'm a UX designer & an all round good guy, building awesome things for the web and mobile. I live in Bengaluru, work remotely with seriously...Read More
Mayur D.UI UX Designer, India
$43 /hr
4 Years Exp.
I am UX Designer with 4.5 years of experience in Mobile and Web design. Currently employed with an Unicorn Startup. Handling their android and iOS app...Read More
Utkarsh P.UI Designer/Graphic Designer, India
$3 /hr
4 Years Exp.
4+ year experience Adobe Photoshop. Over 1 year experience in Sketch for Mac for designing user interfaces of Apps, designing logos, brand identiti...Read More
Aravind K.Full Stack Designer, India
$7 /hr
6 Years Exp.
I'm a designer with 6 years of experience, i can do website, graphic,branding and other kinds of designing works also
Pratik S.PHP Developer & SaaS Based Software Developer, India
$9 /hr
7 Years Exp.
I am PHP based software developer and designer, I am working specially in customized Saad based softwares. Also I am working in other programs like O...Read More
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Articles Related To Uppercase Hr

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."


           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



  • 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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