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Divi expert to duplicate shopify page design 

Hourly - Est. Budget - $18.00, Expiry - Mar 28, 2021, Proposals(0) - posted at 10 hours ago
We are migrating from Shopify to wordpress, and I am looking for someone to duplicate the exact layout of a shopify page onto a existing wordpress with Divi environment. Please provide an hour estimate, and 3 links to previous sites.Thanks!

looking for a superstar mean stack developer to work on a forum application 

Hourly - Est. Budget - $24.00, Expiry - Mar 28, 2021, Proposals(17) - posted at 11 hours ago
Hi, i need to work with a kickass meanstack developer, the application requires good grip on angular 8. So please only bid if you have good experience with angular 8. Prior experience with Twilio Would be a plus.Mention ' super star' at start of your application Please write 'forum app' in the beginning of your proposal so i kno...read more

Shopify Subscription Recharge or Bold App Expert 

Fixed - Est. Budget - $200.00, Expiry - Mar 28, 2021, Proposals(12) - posted at 11 hours ago
I need a Shopify expert that has experience with the Recharge and /or Bold subscription app. Must know how to properly install and customize within the app. Please apply by telling me your experience with these apps. I need help installing, setting up and designing these apps on my website properly.

Window Server 2019 Administration 

Hourly - Est. Budget - $28.00, Expiry - Mar 28, 2021, Proposals(14) - posted at 12 hours ago
Need someone who highly experience in administrating Window Server 2019

FHIR Expert 

Fixed - Est. Budget - $1,000.00, Expiry - Mar 28, 2021, Proposals(18) - posted at 12 hours ago
We are a team of Stanford physicians and engineers who have built an Epic-integrated SMART on FHIR web application. We have extensive long-term work opportunities, but we will start with a short-term tangible fixed project, so that we can get to know each other better. Three deliverables: 1) Push clinical impression (ICD10 dia...read more

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Risk assessment templates 

Hourly - Est. Budget - $16.00, Expiry - Mar 28, 2021, Proposals(16) - posted at 13 hours ago
I am looking for someone who can help me put together risk assessment templates on various topicsNetwork Security, System Security, Application Security, Cloud Security, Wireless Security, Server Security, Physical Security, 3rd Party Vendor Assessment, etc.There is more opportunities available after if this is a successful gig

Security PenTester 

Hourly - Est. Budget - $16.00, Expiry - Mar 28, 2021, Proposals(13) - posted at 13 hours ago
I am looking for a pentester that can quickly do a scan and test a small class C network ( about 20 host devices).I can provide a copy of Kali but this will be completed via screen sharing. I can not give unattended access to the environment. If this project is a success there are much more opportunities available.

JIRA Admin Support 

Hourly - Est. Budget - $28.00, Expiry - Mar 28, 2021, Proposals(23) - posted at 13 hours ago
Looking for part-time JIRA sys admin resource to support short term project remotely. Qualified resource can handle both basic end user support (i.e., add field values, modify workflows, answer user questions, provisioning) for my company's clients.It is anticipated the project will require up to 20 hours per week for 3 months d...read more

Developer needed to create a simple asset management software 

Fixed - Est. Budget - $2,000.00, Expiry - Mar 28, 2021, Proposals(15) - posted at 14 hours ago
We need to create a software management tool that would allow us to keep track of equipment, assign (scanning in) equipment to the company profile, create accounts for workers and reassign(scanning out) equipment to the workers, create reports, pull up history of the managed assets, create reports for biweekly reconcile of the e...read more

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PROXY SUBNET 

Hourly - Est. Budget - $28.00, Expiry - Mar 28, 2021, Proposals(0) - posted at 14 hours ago
Looking to buy a private UK based subnet that i can generate proxies from. These have to be either residential or datacenter proxies. (SOCK4/SOCK5/HTTPS) The subnet must be private to me and me only. The proxies generated must be high speeds (under or close to 100ms) and high quality. I will need to sample examples of your work...read more

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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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