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Sumit D.Perfect PCB Designer, India
$4 /hr
0 Years Exp.
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I'm an Electronic Hardware & PCB designer who's designing PCBs since few years. And have professional experience of 2 years
Ashish Embedded engineer, India
$3 /hr
1 Years Exp.
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I'm having 1 year experience in micro controllers programming. Ive worked with Raspberry Pi, Arduino and ARM 32 bit controllers from different co...Read More
Jigar P.Freelance Embedded & Linux System Consultant, India
$0 /hr
5 Years Exp.
1 Followers
I'm working on Embedded systems from the last 5 years which include in-depth knowledge of Microcontrollers & Linux Product Development.
Claudiu N.Software Engineer, United Kingdom
$60 /hr
15 Years Exp.
0 Followers
Experienced on Bash shell scripting and Linux shell commands, and Linux system configurations. The C language in GCC context is the main programming...Read More
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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.

 

Are you planing to hire a CSS developer—how can you find a top developer for your front-end or PSD to CSS project?

 

CSS has been in use for more than 20 years and has become an integral part of any front-end development. Therefore, there is no shortage of developers with CSS listed on their resumes. Locating CSS developers is fairly easy, but choosing the perfect one is that much more of a challenge. This article offers a sampling of effective questions to help you identify the best CSS developers who are experts in their field.

 

What is CSS?

CSS stands for Cascading Style Sheets, CSS is a programming language that describes the style of a HTML document. If you want to customize your website background image, text colors or border then you need CSS design. Alongside HTML (responsible for structure) and JavaScript (responsible for interactivity), CSS (responsible for style) is one of the big three core components of the web.

 

Next, we learn about what a CSS developer does, and provide you with a general framework for writing a CSS project description to help you find the right developer for your requirements. How to hire a top css developer to get work done.

 

What kind of work a CSS developer should deliver to you? A front-end developer uses a combination of HTML, CSS, and JavaScript to build everything a user sees and interacts with on a website—everything from front-end features like fonts and sliders, to the overall manner in which web content like photos, videos, and articles are displayed in your web browser. A CSS developer who specializes in CSS, taking .psd files and mockups and writing the CSS code that incorporates all of the colors, padding, margins, and more that comprise those designs. Beyond the fundamentals, they can work magic with raw CSS, are well versed in preprocessors like LESS/Sass, and may even use a front-end CSS framework like Bootstrap or Foundation.

 

Hire the best CSS Developers Work with the world's best talent on Toogit — the top growing freelancing website trusted by over 150,000 users.

 

Writing A CSS Development Project Description

After you get a firm idea of your project deliverables, it’s time to write a project description. The way you write a description will determine the quality of developer that you’ll attract. It’s important to be concise yet detailed enough so developers interested in your project can submit proposals with fairly accurate cost and time estimates. Here our recommendation to use Toogit’s auto-proposal to speed up your hiring procedure and feel the power of AI in freelancing.

 

The title of your project description can include the type of development that you need. You know that you need a CSS developer, but why specifically a front-end developer specialized in CSS? The title should attract CSS developers with the specific technologies or skills you require for your project.

 

Next is the project overview. Describe what you’re planning to build or what you’ll need the developer to do. Be as detailed as possible, and include any wireframes or mockups that can help you attract the talented developer for your needs. 

 

Part of your description should also define the deliverables including any designs, documentation, or source code. 

 

Sample CSS Project Description

Below sample will help you to write a perfect project description. 

 

Project Title:

CSS Developer for a Fashion design website 

 

Description: 

We’re looking for an expert CSS developer to help us build an exciting new fashion design website template. The project is based on the (MongoDB, AngularJS, and Node.js) stack, so familiarity using Bootstrap with AngularJS is required.

The right developer will be able to provide us with the following skills and services:

  • Translation of designer mock-ups and wireframes into front-end code
  • Front-end integration with a MEAN back-end
  • Unit testing
  • Bootstrap, LESS, AngularJS
  • Familiarity with API Creation and RESTful services

 

Project Scope & Deliverables:

While much of the project has already been completed, we still need additional support to help us polish our website and meet our launch deadline in 4 months (mm/dd/yyyy). We will need the following three deliverables:

Deliverable #1 by (date) 

Deliverable #2 by (date) 

Deliverable #3 by (date)

Hire a CSS Designer

On Toogit.com you can hire CSS coders and designers to make your web design and custom CSS project shine. Get started today.

 

Conclusion

For a top CSS developer, read our css interview question and answer section this might come off as a bit basic. However, It cover most of the core CSS concepts and principles, and provide a starting point for evaluating individuals. Being able to discuss CSS principles and concepts in a clear and coherent manner will demonstrate candidate’s communication skills as well as their theoretical and peripheral subject knowledge. Finding true CSS expert is a challenge. We hope you find the interview questions to be a useful foundation in your quest for the elite few among CSS developers. Finding such candidates is well worth the effort, as they will undoubtedly have a significant positive impact on your team’s productivity and results.

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!!!

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