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Top 29 Process Architect Freelancers on 20 Jun 2019 on Toogit. Process Architect Freelancers on Toogit are highly skilled and talented. Hiring Process Architect Freelancers on Toogit is quite affordable as compared to a full-time employee and you can save upto 50% in business cost by hiring Process Architect Freelancers on Toogit. Hiring Process Architect 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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Hammad M.Freelance Designer, PrePress, Pakistan
$12 /hr
8 Years Exp.
0 Followers
Graphic Production artist with over 8 years of experience of working to tights deadlines on multiple projects and following trends for new branding. C...Read More
Business Analyst/Data Analytics professional with 10+ years of relevant work experience in the arena of Business Analysis, Data Visualization, Data An...Read More
Processing Word Processing Order Processing Payroll Processing Reports Process Improvement Product Sourcing Product Marketing Report Proposal Writing iReport CRM Entries HR Consulting Personal Budgeting HR Policies Insurance Consulting Data Warehousing Cold Calling Expense Reporting Sales Writing Product Management HR Benefits Business Process Management (BPM) Report Writing Product Development Sales Promotion Business Intelligence Macros Sales Copywriting Web Scraping Business Valuation Call Handling Financial Reporting Business Planning Analysts Office Administration Research Proposal Microsoft Office SharePoint Server Business Activity Monitoring BPO Call Center Agents Sales Management Investigative Reporting Transact-SQL Sales Consultants HRsoft Talent Management Insurance Policy Analysis Master Production Schedule Training Online LMS Business Cards Email Marketing Consultants Business Mathematics Evolus Pencil Healthcare Management Business Scenario Development Microsoft Power BI Data Visualization Outbound Sales Budgeting & Forecasting QlikSense Healthcare Information Technology Live Chat Operator Budget Management Insurance Agency Operations Email Campaign Setup Customer Acquistion Business Development Analysts Stakeholder Management Custom Insight Employee Engagement Survey Email Support Data Science & Analytics Lucid Software Lucidchart ActiveX Data Objects (ADO) Word-of-Mouth Email Security QlikTech QlikView Document Control Data Visualization Email Deliverability Email Technical Support Qlikview VBA 
Rajendra Singh C.Photo editing and retouching, India
$3 /hr
15 Years Exp.
0 Followers
I am a best photo correction and editing in cmyk color for offset machine and news paper industries. as well as RGB colour photo editing for or digita...Read More
Aleksandr D.Creative Designer, Russia
$18 /hr
12 Years Exp.
0 Followers
I have five years experience in print design and advertising, two years I worked in GameDev industry, and for six years provide services as a freelanc...Read More
Ritwik has extensive engineering skills and in-depth knowledge and professional experience in the area of materials and process development, integrate...Read More
Manesh Prepress, India
$20 /hr
20 Years Exp.
0 Followers
I am a graphic designer and prepress operator.. worked with one of the most reputed firm THOMSON PRESS INDIA LTD.
Bi B.Graphic Communication Designer, India
$15 /hr
3 Years Exp.
0 Followers
Typography and Print Design, calligraphy and custom type design, Illustrations in various mediums.
Varadhajulu K.Project manager, India
$5 /hr
10 Years Exp.
1 Followers
I have experience in Managing projects with excellent feedback.
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Articles Related To Process


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.

With such a huge number of choices it may be hard to find the right designer for your specific needs. Toogit is always there to help you make the right choice when choosing a logo designer. These points could also be useful when hiring a web designer, graphic designer, or any designer for that matter.

I don’t want this post to come across as self promotional, however, I’ve linked to my own examples to show you how I personally communicate to potential clients the value of my design work. For other designers, I hope this in turn, gives you an idea of how you too can communicate the value of your work.

In no particular order:

  1. Experience : Previous identity projects will give you a good idea of what skill level your designer is at and what you can expect though this is not to say that a new designer can not produce top quality results – this point has to be considered with all of the other points mentioned below, in which case a strong portfolio is probably the best indicator.
  2. Positive Testimonials : Have they got positive testimonials from past clients and colleagues? Ensure you check the testimonials validity which can be done by looking for a web address or even by emailing the company. It’s a good idea to check if the company even exists.
  3. A Thorough Design Process : Do they have a logo design process in which they follow or are they simply producing logos like fast food? A typical process does not take under 24 hours to complete.
  4. Awards Won / Published Work : Have they won any awards for their work? Is their work published in any books or magazines? How recognised are they in the industry?
  5. A Strong Portfolio : How strong is their portfolio?   What is the make up of real to ‘fake’ logo designs? When I say ‘fake logos’ I refer to the logos made for fictional companies, rather than for real clients.
  6. Price : The price of the service is usually quite evident of what he is going to deliver. In most cases, freelancer should deliver what you pay for but don’t take price as the only indication. How much does a logo design actually cost? In my experience, this is the most frequently asked question and the hardest to answer. This is because every company has different requirements the best approach is to draw up a customised quote for each individual client.
  7. Design Affiliations : Are they affiliated with any design associations or publications? This is a good indication of how dedicated they are to their craft though is not at all essential.
  8. Great Customer Service : Do they respond to your emails quickly? How do they communicate & present themselves? A designer should provide great customer service throughout the whole process, from the initial email right through to after sales support.
  9. Business Professionalism : Attention to detail, trustworthiness, strong communication skills and time management are all vital and go hand in hand with great customer service.
  10. Appropriate Questions : A designer should ask a no of questions to find out your needs in relation to your business goals. Questions should revolve around the companies history, target market, competitors, company goals, etc.

A customer service representative talk to customers on behalf of companies and businesses. They answer customer queries, resolve customer complaints, and address emergency issues. If a customer needs help placing an order or needs to change their order, the customer service representative walks them through the correct steps. Customer service representatives will also provide additional sales or products, or sell products or services directly.

 

What Customer Service Representatives do?

Customer service representatives typically do the following:

 

  • Listen to customer’s questions and concerns, and provide answers or responses
  • Provide information about products and services
  • Take orders, calculate charges, and process billing or payments
  • Review or make changes to customer accounts
  • Handle returns or complaints
  • Record details of customer contacts and actions taken
  • Refer customers to supervisors or more experienced employees

The specific duties of customer service representatives vary by industry. For example, representatives who work in banks may answer customer’s questions about their accounts. Representatives who work for utility and telecommunication companies may help customers with service problems, such as outages. Those who work in retail stores often handle returns, process refunds, and help customers locate items. Some representatives make changes to customers' accounts, such as updating addresses or canceling orders. Although selling is not their main job, some representatives may help generate sales while providing information about a product or service.

 

How to define your Customer service project?

Your job description is the 1st touch point between your company and your new hire. With millions of people looking for jobs on so every month, an excellent description will assist you attract the most qualified candidates to your open position. To urge you started, here are some tips for making an efficient description.

 

A great job description starts with a compelling summary of the position and its role within your company. Your summary should provide an overview of your company and expectations for the position. Outline the types of activities and responsibilities required for the job so job seekers can determine if they are qualified, or if the job is a good fit.

 

Basic of customer service project description are as follow:

  1. State a brief one to two sentence overview of the project objectives.
  2. State this in one sentence so the professional understands how the work fits into the bigger picture.
  3. Be specific with requirements, such as specific software knowledge, typing speed, and language fluency.
  4. Enter a start and end date. Most independent contractor projects are project based and/or short-term. If you’re unsure of the end date, or if the work may be ongoing, you may need to consult your HR partner or other advisor to verify the worker’s classification.
  5. Independent contractor work is often paid at a fixed price. When paying hourly, you may want to specify the maximum number of hours for the project if any.

 

Sample:

We need an experience agent for a ten month project, expected to require 25 hours/week of services, from [start date] to [end date]. We’re launching a multimedia campaign that’s expected to dramatically spike call volume. You’ll work from your own location to handle product calls, take orders, and process payments.

Skill Requirements:

  • Knowledge of Zendesk and Salesforce software
  • Expertise in providing customer service via phone and live chat
  • Fluent in English and Spanish
  • Handle 10-15 calls per hour
  • Ability to upsell and cross-sell products on the phone
  • Basic knowledge of Microsoft Excel

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