Hire the best
Human Resources

Work with top freelancers on Toogit
Best freelancing website for any kind of projects - urgent bug fixes, minor enhancements,
short-term tasks, recurring projects, and full-time contract work.
Top rated, curated and experienced Human Resources

Get Started

Hire Trusted Freelancers for your project

More than 150,000 freelancers ready to tackle any kind of project

How it works

Post a job

Define your project

What you need in as much detail as possible. We will connect you with top talented ready to work freelancers best suitable for your requirement around the world, or near you.

Proposals

Find your expert

Get qualified proposals within 24 hours. Compare bids, reviews, and prior work. Interview favorites and hire the best fit. Auto proposal will help for urgent hiring

Communicate

Communicate

Use Toogit Messenger to chat, share files, and track project milestones from your desktop or mobile with realtime updates.

Payment

Pay Securely

Pay securely through Toogit's Partial/Full Payment system. Simply create invoices for project milestones, and only release the funds when you are 100% satisfied with the work completed.

Browse Our Top Rated Human Resources


Itisha S.Currently a Research Scholar, India
$19 /hr
3 Years Exp.
0 Followers
Hi, I am currently pursuing my PhD in Aviation. Earlier I have worked with e-commerce and education stalwarts as HR Manager. You can visit my LinkedIn...Read More
Sami K.Chartered Accountant, Pakistan
$9 /hr
5 Years Exp.
0 Followers
I am a Chartered Accountant and have 5 years of professional experience.
Vanessa M.Virtual Assistant, Philippines
$5 /hr
11 Years Exp.
0 Followers
A graduate of Bachelor of Science in Business Administration from the University of Antique. Currently working as HR Officer in a Construction Company...Read More
Arlene I.Accountant, Philippines
$35 /hr
15 Years Exp.
0 Followers
I am a CPA with more than 10 years experience in full sets of accounts, financial reporting and payroll administration. Accounting system used Quickbo...Read More
Anushreeta G.HRM, India
$60 /hr
0 Years Exp.
0 Followers
I've 9 years of experience in service industry as a Sr.call quality analyst. Recently I've completed my Masters in Social welfare. I possess...Read More
John K.Financial Accountant, Kenya
$50 /hr
7 Years Exp.
0 Followers
I am proud to be an accountant that helps business clients make sound financial decisions on a daily basis. I am fully committed to helping businesses...Read More
Jianna Krishna C.Recruitment/Admin Support, Philippines
$2 /hr
2 Years Exp.
0 Followers
I worked as an HR Associate for two years in a private company. I handled most of the recruitment and some were administrative support. I also handled...Read More
Princes Admin/Data Analyst, Philippines
$2 /hr
5 Years Exp.
0 Followers
I'm a data converter/encoder for 4 years and 5 years as Human Resources Staff, I can do admin work
Ndidiamaka O.English teacher, Nigeria
$20 /hr
2 Years Exp.
0 Followers
I am a graduate of English and Literature from the best university in Africa, University of Nigeria, Nsukka. I am currently undergoing my masters prog...Read More
Michele H.Knowledge Specialist , Philippines
$50 /hr
3 Years Exp.
0 Followers
Candidate Profile: 4 years - BPO Industry for Sales and Market Intelligence Account | 6 years- Global Human Resources Management | 5 years in Technica...Read More
Nessa Mutia Administrative Assistant, Philippines
$6 /hr
2 Years Exp.
0 Followers
I am a graduate in a Degree of Bachelor of Science in Business Administration, and I took up a course in Education program. I experienced marketing co...Read More
Charity Grace P.Human Resource Specialist, Philippines
$3 /hr
5 Years Exp.
0 Followers
I am a Human Resources Specialist with a demonstrated history of working in the outsourcing/offshoring industry. Skilled in Company Secretarial Work,...Read More
Kuldip US Accounting and Taxation, India
$8 /hr
10 Years Exp.
0 Followers
I have 10+ years of experience in Accounting and Taxation. An extremely motivated individual with a natural ability to solve problems and handle multi...Read More
I graduated as cum laude from a prestigious university with a bachelors degree in Psychology. With my knowledge in the field of Psychology and Human R...Read More
Aniket S.Business manager, India
$3 /hr
4 Years Exp.
0 Followers
I am the Founder of an NGO. Where I work with 135+ Member.
Rajesh R.Online Teaching in c#,Android,AR and VR, India
$4 /hr
1 Years Exp.
0 Followers
i am certified c# developer and having a experience in software development,Android,Hybrid Mobile Application,AR and VR
Heena D.Hardwork, Discipline, Teamwork, Innovative, India
$20 /hr
0 Years Exp.
1 Followers
A result-driven Chartered Accountant with work equity of 2+ years with strong sense of urgency and a fearless 'can-do' attitude. Completed m...Read More
Hema C.HR, India
$3 /hr
10 Years Exp.
0 Followers
I am HR and Pay roll specialist with 10 years of expereince
Imam Accounts Manager, India
$2 /hr
4 Years Exp.
0 Followers
I am a Semi Qualified Chartered Accountant with experience of 4 years in the fields of Accounts, Audit and Taxation.
I am successful entrepreneur who is motivating educated homemakers to start up business of their interest. I have been mentoring young students to imp...Read More
Hazel Valerie Payroll and Implementation Consultant, Philippines
$10 /hr
11 Years Exp.
0 Followers
With professional experience of 7 years as Payroll Specialist and 4 years as Implementation Consultant in Outsourcing Industry. Experience in managing...Read More
Lorie-anne C.Freelancer, Philippines
$11 /hr
0 Years Exp.
0 Followers
Willing to do a certain task in order to improve my skills and become flexible in every task that will be given.
Lhen M.HUMAN RESOURCE AND ADMINISTRATION SPECIALIST, Philippines
$10 /hr
1 Years Exp.
0 Followers
An experienced Human Resources Specialist with a demonstrated history of working in the human resources industry. Skilled in Microsoft Word, Technical...Read More
Kraznnah P.Virtual Assistant, Data Encoder, Bookkeeper, Paralegal, Philippines
$18 /hr
2 Years Exp.
1 Followers
I am a Management Accountancy and Law graduate with experiences as a clerk, service desk, and a paralegal.
Ankit C.Assembly supervisor, India
$10 /hr
2 Years Exp.
0 Followers
I'm a graduate person and I have a work experience as a assembly supervisor in Philippines
Musa I.Data Entry Operator, Pakistan
$5 /hr
0 Years Exp.
0 Followers
I am a currently completing my bs hons. degress in accounting and finance from FCCU with a CGPA 3.79. I have completed my alevels recently. About my w...Read More
Kathleen J.Human Resource Management, Philippines
/hr
4 Years Exp.
0 Followers
I'm part of the Human Resource Department in a private dental company in the Philippines. I'm former Receptionist and Admin of the same comp...Read More
To view more profile join Toogit

Get Started
 



Are you looking for Human Resources Freelance Job? We’ll help you find the perfect matching job here

Top Earning Freelancers

Syed Rameez H.

Syed Rameez H.

Mobile Developer
Shilpi G.

Shilpi G.

Full stack frontend developer
Shital S.

Shital S.

QA Engineer
Pratik

Pratik

Web and Mobile Developer

Articles Related To Human Resources


A chatbot is an artificial intelligence powered piece of software in a device, application, web site or alternative networks that try to complete consumer’s needs and then assist them to perform a selected task. Now a days almost every company has a chatbot deployed to interact with the users.

 

Chatbots are often used in many departments, businesses and every environment. They are artificial narrow intelligence (ANI). Chatbots only do a restricted quantity of task i.e. as per their design. However, these Chatbots make our lives easier and convenient. The trend of Chatbots is growing rapidly between businesses and entrepreneurs, and are willing to bring chatbots to their sites. You might also produce it yourself using Python.

 

How do chatbots work?

There are broadly two variants of chatbotsRule-Based and Self learning.

  1. In a Rule-based approach, a bot answers questions based on some rules on that it is trained on. The rules outlined could be very easy to very complicated. The bots will handle easy queries but fail to manage complicated ones.
  2. The Self learning bots are those that use some Machine Learning-based approaches and are positively a lot of economical than rule-based bots. These bots may be of additional two types: Retrieval based or Generative.
    1. In retrieval-based models, Chatbot uses the message and context of conversation for selecting the best response from a predefined list of bot messages.
    2. Generative bots can generate the answers and not always reply with one of the answers from a set of answers. This makes them more intelligent as they take word by word from the query and generates the answers.

 

Building a chatbot using Python

NLP:

The field of study that focuses on the interactions between human language and computers is called Natural Language Processing. NLP is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way. However, if you are new to NLP, you can read Natural Language Processing in Python.

 

NLTK:

NLTK (Natural Language Toolkit) is a leading platform for building Python programs to work with human language data. It provides easy-to-use lexical resources such as WordNet, along with a suite of text processing libraries.

 

Importing necessary libraries

import nltk 

import numpy as np 

import random 

import string # to process standard python strings

 

Copy the content in text file named ‘chatbot.txt’, read in the text file and convert the entire file content into a list of sentences and a list of words for further pre-processing.

 

f=open('chatbot.txt','r',errors = 'ignore')

raw=f.read()

raw=raw.lower()# converts to lowercase

nltk.download('punkt') # first-time use only

nltk.download('wordnet') # first-time use only

sent_tokens = nltk.sent_tokenize(raw)# converts to list of sentences 

word_tokens = nltk.word_tokenize(raw)# converts to list of words

 

Pre-processing the raw text

We shall now define a function called LemTokens which will take as input the tokens and return normalized tokens.

 

lemmer = nltk.stem.WordNetLemmatizer()

#WordNet is a semantically-oriented dictionary of English included in NLTK.

def LemTokens(tokens):     

return [lemmer.lemmatize(token) for token in tokens]

remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation) 

def LemNormalize(text):     

return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))

 

Keyword matching

Define a function for greeting by bot i.e. if user’s input is greeting, the bot shall return a greeting response.

GREETING_INPUTS = ("hello", "hi", "greetings", "sup", "what's up","hey",)

GREETING_RESPONSES = ["hi", "hey", "*nods*", "hi there", "hello", "I am glad! You are talking to me"]

def greeting(sentence):

for word in sentence.split():

if word.lower() in GREETING_INPUTS:

return random.choice(GREETING_RESPONSES)

 

Generate responses

To generate a response from our bot for input queries, the concept of document similarity is used. Therefore, we start by importing necessary modules.

From scikit learn library, import the TFidf vector to convert a collection of raw documents to a matrix of TF-IDF features

from sklearn.feature_extraction.text import TfidfVectorizer

Also, import cosine similarity module from scikit learn library

from sklearn.metrics.pairwise import cosine_similarity

This will be used to find the similarity between words entered by the user and therefore the words within the corpus. This can be the simplest possible implementation of a chatbot.

Define a function response that searches the user’s vocalization for one or more known keywords and returns one of several possible responses. If it doesn’t find the input matching any of the keywords, it returns a response: “I’m sorry! I don’t understand you”

 

def response(user_response):

robo_response=''

sent_tokens.append(user_response)

TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english')

tfidf = TfidfVec.fit_transform(sent_tokens)

vals = cosine_similarity(tfidf[-1], tfidf)

idx=vals.argsort()[0][-2]

flat = vals.flatten()

flat.sort()

req_tfidf = flat[-2]

if(req_tfidf==0):

robo_response=robo_response+"I am sorry! I don't understand you"

return robo_response

else:  robo_response = robo_response+sent_tokens[idx]

return robo_response

 

I have tried to explain in simple steps how you can build your own chatbot using NLTK and of course it’s not an intelligent one.

I hope you guys have enjoyed reading.

Happy Learning!!!

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.

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.

 

Articles Related To Human Resources


Understanding chatbots and how to build one simple chatbot in Python
Understanding chatbots and how to build one simple...
Other - Software Development

A chatbot is an artificial intelligence powered piece of software in a device, application, web site or alternative networks that try to complete consumer’s needs and then assist t...

Read More
Natural Language Processing in Python
Natural Language Processing in Python
Web Development

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 mann...

Read More
Choose Python Language for Bright Future
Choose Python Language for Bright Future
Other - Software Development

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 futu...

Read More

What our users are discussing about Human Resources